Gall-forming arthropods are highly specialized herbivores that, in combination with their hosts, produce extended phenotypes with unique morphologies [1]. Many are economically important, and others have improved our understanding of ecology and adaptive radiation [2]. However, the mechanisms that these arthropods use to induce plant galls are poorly understood. We sequenced the genome of the Hessian fly (Mayetiola destructor; Diptera: Cecidomyiidae), a plant parasitic gall midge and a pest of wheat (Triticum spp.), with the aim of identifying genic modifications that contribute to its plant-parasitic lifestyle. Among several adaptive modifications, we discovered an expansive reservoir of potential effector proteins. Nearly 5% of the 20,163 predicted gene models matched putative effector gene transcripts present in the M. destructor larval salivary gland. Another 466 putative effectors were discovered among the genes that have no sequence similarities in other organisms. The largest known arthropod gene family (family SSGP-71) was also discovered within the effector reservoir. SSGP-71 proteins lack sequence homologies to other proteins, but their structures resemble both ubiquitin E3 ligases in plants and E3-ligase-mimicking effectors in plant pathogenic bacteria. SSGP-71 proteins and wheat Skp proteins interact in vivo. Mutations in different SSGP-71 genes avoid the effector-triggered immunity that is directed by the wheat resistance genes H6 and H9. Results point to effectors as the agents responsible for arthropod-induced plant gall formation.

Mediator is an evolutionarily conserved transcriptional regulatory complex. Mechanisms of Mediator function are poorly understood. Here we show that Arabidopsis MED18 is a multifunctional protein regulating plant immunity, flowering time and responses to hormones through interactions with distinct transcription factors. MED18 interacts with YIN YANG1 to suppress disease susceptibility genes glutaredoxins GRX480, GRXS13 and thioredoxin TRX-h5. Consequently, yy1 and med18 mutants exhibit deregulated expression of these genes and enhanced susceptibility to fungal infection. In addition, MED18 interacts with ABA INSEN-SITIVE 4 and SUPPRESSOR OF FRIGIDA4 to regulate abscisic acid responses and flowering time, respectively. MED18 associates with the promoter, coding and terminator regions of target genes suggesting its function in transcription initiation, elongation and termination. Notably, RNA polymerase II occupancy and histone H3 lysine tri-methylation of target genes are affected in the med18 mutant, reinforcing MED18 function in different mechanisms of transcriptional control. Overall, MED18 conveys distinct cues to engender transcription underpinning plant responses.

Herpes simplex virus (HSV) is a widespread pathogen that causes epithelial lesions with recurrent disease that manifests over a lifetime. The lifelong aspect of infection results from latent viral infection of neurons, a reservoir from which the virus reactivates periodically. Recent work has demonstrated the breadth of genetic variation in globally distributed HSV strains. However, the amount of variation or capacity for mutation within one strain has not been well studied. Here we developed and applied a streamlined new approach for assembly and comparison of large DNA viral genomes such as HSV-1. This viral genome assembly (VirGA) workflow incorporates a combination of de novo assembly, alignment, and annotation strategies to automate the generation of draft genomes for large viruses. We applied this approach to quantify the amount of variation between clonal derivatives of a common parental virus stock. In addition, we examined the genetic basis for syncytial plaque phenotypes displayed by a subset of these strains. In each of the syncytial strains, we found an identical DNA change, affecting one residue in the gB (UL27) fusion protein. Since these identical mutations could have appeared after extensive in vitro passaging, we applied the VirGA sequencing and comparison approach to two clinical HSV-1 strains isolated from the same patient. One of these strains was syncytial upon first culturing; its sequence revealed the same gB mutation. These data provide insight into the extent and origin of genome-wide intrastrain HSV-1 variation and present useful methods for expansion to in vivo patient infection studies.

PURPOSE Patients with myelodysplastic syndromes (MDS) have a survival that can range from months to decades. Prognostic systems that incorporate advanced analytics of clinical, pathologic, and molecular data have the potential to more accurately and dynamically predict survival in patients receiving various therapies. METHODS A total of 1,471 MDS patients with comprehensively annotated clinical and molecular data were included in a training cohort and analyzed using machine learning techniques. A random survival algorithm was used to build a prognostic model, which was then validated in external cohorts. The accuracy of the proposed model, compared with other established models, was assessed using a concordance (c)index. RESULTS The median age for the training cohort was 71 years. Commonly mutated genes included SF3B1, TET2, and ASXL1. The algorithm identified chromosomal karyotype, platelet, hemoglobin levels, bone marrow blast percentage, age, other clinical variables, seven discrete gene mutations, and mutation number as having prognostic impact on overall and leukemia-free survivals. The model was validated in an independent external cohort of 465 patients, a cohort of patients with MDS treated in a prospective clinical trial, a cohort of patients with paired samples at different time points during the disease course, and a cohort of patients who underwent hematopoietic stem-cell transplantation. CONCLUSION A personalized prediction model on the basis of clinical and genomic data outperformed established prognostic models in MDS. The new model was dynamic, predicting survival and leukemia transformation probabilities at different time points that are unique for a given patient, and can upstage and downstage patients into more appropriate risk categories.

In addition to proteins, L-phenylalanine is a versatile precursor for thousands of plant metabolites. Production of phenylalanine-derived compounds is a complex multi-compartmental process using phenylalanine synthesized predominantly in plastids as precursor. The transporter(s) exporting phenylalanine from plastids, however, remains unknown. Here, a gene encoding a Petunia hybrida plastidial cationic amino-acid transporter (PhpCAT) functioning in plastidial phenylalanine export is identified based on homology to an Escherichia coli phenylalanine transporter and co-expression with phenylalanine metabolic genes. Radiolabel transport assays show that PhpCAT exports all three aromatic amino acids. PhpCAT downregulation and overexpression result in decreased and increased levels, respectively, of phenylalanine-derived volatiles, as well as phenylalanine, tyrosine and their biosynthetic intermediates. Metabolic flux analysis reveals that flux through the plastidial phenylalanine biosynthetic pathway is reduced in PhpCAT RNAi lines, suggesting that the rate of phenylalanine export from plastids contributes to regulating flux through the aromatic amino-acid network.