Rhg4 is a major genetic locus that contributes to soybean cyst nematode (SCN) resistance in the Peking-type resistance of soybean (Glycine max), which also requires the rhg1 gene. By map-based cloning and functional genomic approaches, we previously showed that the Rhg4 gene encodes a predicted cytosolic serine hydroxymethyltransferase (GmSHMT08); however, the novel gain of function of GmSHMT08 in SCN resistance remains to be characterized. Using a forward genetic screen, we identified an allelic series of GmSHMT08 mutants that shed new light on the mechanistic aspects of GmSHMT08-mediated resistance. The new mutants provide compelling genetic evidence that Peking-type rhg1 resistance in cv Forrest is fully dependent on the GmSHMT08 gene and demonstrates that this resistance is mechanistically different from the PI 88788-type of resistance that only requires rhg1. We also demonstrated that rhg1-a from cv Forrest, although required, does not exert selection pressure on the nematode to shift from HG type 7, which further validates the bigenic nature of this resistance. Mapping of the identified mutations onto the SHMT structural model uncovered key residues for structural stability, ligand binding, enzyme activity, and protein interactions, suggesting that GmSHMT08 has additional functions aside from its main enzymatic role in SCN resistance. Lastly, we demonstrate the functionality of the GmSHMT08 SCN resistance gene in a transgenic soybean plant.

Soybean cyst nematode (SCN) Heterodera glycines is an obligate parasite that relies on the secretion of effector proteins to manipulate host cellular processes that favor the formation of a feeding site within host roots to ensure its survival. The sequence complexity and co-evolutionary forces acting upon these effectors remain unknown. Here we generated a de novo transcriptome assembly representing the early life stages of SCN in both a compatible and an incompatible host interaction to facilitate global effector mining efforts in the absence of an available annotated SCN genome. We then employed a dual effector prediction strategy coupling a newly developed nematode effector prediction tool, N-Preffector, with a traditional secreted protein prediction pipeline to uncover a suite of novel effector candidates. Our analysis distinguished between effectors that co-evolve with the host genotype and those conserved by the pathogen to maintain a core function in parasitism and demonstrated that alternative splicing is one mechanism used to diversify the effector pool. In addition, we confirmed the presence of viral and microbial inhabitants with molecular sequence information. This transcriptome represents the most comprehensive whole-nematode sequence currently available for SCN and can be used as a tool for annotation of expected genome assemblies.

The dimorphic alphaproteobacterium Prosthecomicrobium hirschii has both short-stalked and long-stalked morphotypes. Notably, these morphologies do not arise from transitions in a cell cycle. Instead, the maternal cell morphology is typically reproduced in daughter cells, which results in microcolonies of a single cell type. In this work, we further characterized the shortstalked cells and found that these cells have a Caulobacter-like life cycle in which cell division leads to the generation of two morphologically distinct daughter cells. Using a microfluidic device and total internal reflection fluorescence (TIRF) microscopy, we observed that motile short-stalked cells attach to a surface by means of a polar adhesin. Cells attached at their poles elongate and ultimately release motile daughter cells. Robust biofilm growth occurs in the microfluidic device, enabling the collection of synchronous motile cells and downstream analysis of cell growth and attachment. Analysis of a draft P. hirschii genome sequence indicates the presence of CtrA-dependent cell cycle regulation. This characterization of P. hirschii will enable future studies on the mechanisms underlying complex morphologies and polymorphic cell cycles. IMPORTANCEBacterial cell shape plays a critical role in regulating important behaviors, such as attachment to surfaces, motility, predation, and cellular differentiation; however, most studies on these behaviors focus on bacteria with relatively simple morphologies, such as rods and spheres. Notably, complex morphologies abound throughout the bacteria, with striking examples, such as P. hirschii, found within the stalked Alphaproteobacteria. P. hirschii is an outstanding candidate for studies of complex morphology generation and polymorphic cell cycles. Here, the cell cycle and genome of P. hirschii are characterized. This work sets the stage for future studies of the impact of complex cell shapes on bacterial behaviors. The Alphaproteobacteria comprise a diverse group of bacteria, including important pathogens of animals (Brucella spp. and Rickettsia spp.) and plants (Agrobacterium spp.), plant symbionts (Rhizobium spp., Sinorhizobium spp., and Mesorhizobium spp.), photosynthetic bacteria (Rhodobacter spp.), freshwater bacteria (Caulobacter spp.), and marine bacteria (Hyphomonas spp.). Despite the diverse habitats and lifestyles of these bacteria, many alphaproteobacterial species have a characteristic life cycle that culminates in asymmetric cell division (1-4). Caulobacter crescentus has served as a model bacterial system for the study of cell cycle regulation, and decades of research have provided insights into the mechanism underlying the precise cell cycle control that allows

RNA sequencing (RNA-seq) is becoming a prevalent approach to quantify gene expression and is expected to gain better insights into a number of biological and biomedical questions compared to DNA microarrays. Most importantly, RNA-seq allows us to quantify expression at the gene or transcript levels. However, leveraging the RNA-seq data requires development of new data mining and analytics methods. Supervised learning methods are commonly used approaches for biological data analysis that have recently gained attention for their applications to RNA-seq data. Here, we assess the utility of supervised learning methods trained on RNA-seq data for a diverse range of biological classification tasks. We hypothesize that the transcript-level expression data are more informative for biological classification tasks than the gene-level expression data. Our large-scale assessment utilizes multiple data sets, organisms, lab groups, and RNA-seq analysis pipelines. Overall, we performed and assessed 61 biological classification problems that leverage three independent RNA-seq data sets and include over 2000 samples that come from multiple organisms, lab groups, and RNA-seq analyses. These 61 problems include predictions of the tissue type, sex, or age of the sample, healthy or cancerous phenotypes, and pathological tumor stages for the samples from the cancerous tissue. For each problem, the performance of three normalization techniques and six machine learning classifiers was explored. We find that for every single classification problem, the transcript-based classifiers outperform or are comparable with gene expression-based methods. The top-performing techniques reached a near perfect classification accuracy, demonstrating the utility of supervised learning for RNA-seq based data analysis.

• Cyst nematodes induce a multicellular feeding site within roots called a syncytium. It remains unknown how root cells are primed for incorporation into the developing syncytium. Furthermore, it is an enigma how CLAVATA3/ESR (CLE) peptide effectors secreted into the cytoplasm of the initial feeding cell could have an effect on plant cells so distant from where the nematode is feeding as the syncytium expands. • Here we describe a novel translocation signal within nematode CLE effectors that is recognized by plant cell secretory machinery to redirect these peptides from the cytoplasm to the apoplast of plant cells. • We show that the translocation signal is functionally conserved across CLE effectors identified in nematode species spanning three genera and multiple plant species, operative across plant cell types, and can traffic other unrelated small peptides from the cytoplasm to the apoplast of host cells via a previously unknown post-translational mechanism of ER translocation. • Our results uncover an unprecedented mechanism of effector trafficking by any plant pathogen to date and illustrates how phytonematodes can deliver effector proteins into host cells and then hijack plant cellular processes for their export back out of the cell to function as external signaling molecules to distant cells.