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Goss's wilt, caused by the Gram-positive actinobacterium Clavibacter nebraskensis, is an important bacterial disease of maize. The molecular and genetic mechanisms of resistance to the bacterium, or, in general, Gram-positive bacteria causing plant diseases, remain poorly understood. Here, we examined the genetic basis of Goss's wilt through differential gene expression, standard genome-wide association mapping (GWAS), extreme phenotype (XP) GWAS using highly resistant (R) and highly susceptible (S) lines, and quantitative trait locus (QTL) mapping using 3 bi-parental populations, identifying 11 disease association loci. Three loci were validated using near-isogenic lines or recombinant inbred lines. Our analysis indicates that Goss's wilt resistance is highly complex and major resistance genes are not commonly present. RNA sequencing of samples separately pooled from R and S lines with or without bacterial inoculation was performed, enabling identification of common and differential gene responses in R and S lines. Based on expression, in both R and S lines, the photosynthesis pathway was silenced upon infection, while stress-responsive pathways and phytohormone pathways, namely, abscisic acid, auxin, ethylene, jasmonate, and gibberellin, were markedly activated. In addition, 65 genes showed differential responses (up- or down-regulated) to infection in R and S lines. Combining genetic mapping and transcriptional data, individual candidate genes conferring Goss's wilt resistance were identified. Collectively, aspects of the genetic architecture of Goss's wilt resistance were revealed, providing foundational data for mechanistic studies.

 

Phytopathogenic bacteria play important roles in plant productivity, and developments in gene editing have potential for enhancing the genetic tools for the identification of critical genes in the pathogenesis process. CRISPR-based genome editing variants have been developed for a wide range of applications in eukaryotes and prokaryotes. However, the unique mechanisms of different hosts restrict the wide adaptation for specific applications. Here, CRISPR-dCas9 (dead Cas9) and nCas9 (Cas9 nickase) deaminase vectors were developed for a broad range of phytopathogenic bacteria. A gene for a dCas9 or nCas9, cytosine deaminase CDA1, and glycosylase inhibitor fusion protein (cytosine base editor, or CBE) was applied to base editing under the control of different promoters. Results showed that the RecA promoter led to nearly 100% modification of the target region. When residing on the broad host range plasmid pHM1, CBE_RecApis efficient in creating base edits in strains ofXanthomonas,Pseudomonas,ErwiniaandAgrobacterium. CBE based on nCas9 extended the editing window and produced a significantly higher editing rate inPseudomonas. Strains with nonsynonymous mutations in test genes displayed expected phenotypes. By multiplexing guide RNA genes, the vectors can modify up to four genes in a single round of editing. Whole-genome sequencing of base-edited isolates ofXanthomonas oryzaepv.oryzaerevealed guide RNA-independent off-target mutations. Further modifications of the CBE, using a CDA1 variant (CBE_RecAp-A) reduced off-target effects, providing an improved editing tool for a broad group of phytopathogenic bacteria.

 

A great part of software development involves conceptualizing or communicating the underlying procedures and logic that needs to be expressed in programs. One major difficulty of programming is turning concept into code , especially when dealing with the APIs of unfamiliar libraries. Recently, there has been a proliferation of machine learning methods for code generation and retrieval from natural language queries , but these have primarily been evaluated purely based on retrieval accuracy or overlap of generated code with developer-written code, and the actual effect of these methods on the developer workflow is surprisingly unattested. In this article, we perform the first comprehensive investigation of the promise and challenges of using such technology inside the PyCharm IDE, asking, “At the current state of technology does it improve developer productivity or accuracy, how does it affect the developer experience, and what are the remaining gaps and challenges?” To facilitate the study, we first develop a plugin for the PyCharm IDE that implements a hybrid of code generation and code retrieval functionality, and we orchestrate virtual environments to enable collection of many user events (e.g., web browsing, keystrokes, fine-grained code edits). We ask developers with various backgrounds to complete 7 varieties of 14 Python programming tasks ranging from basic file manipulation to machine learning or data visualization, with or without the help of the plugin. While qualitative surveys of developer experience are largely positive, quantitative results with regards to increased productivity, code quality, or program correctness are inconclusive. Further analysis identifies several pain points that could improve the effectiveness of future machine learning-based code generation/retrieval developer assistants and demonstrates when developers prefer code generation over code retrieval and vice versa. We release all data and software to pave the road for future empirical studies on this topic, as well as development of better code generation models. 

Marine dissolved organic matter (DOM) is a major reservoir that links global carbon, nitrogen, and phosphorus. DOM is also important for marine sulfur biogeochemistry as the largest water column reservoir of organic sulfur. Dissolved organic sulfur (DOS) can originate from phytoplankton-derived biomolecules in the surface ocean or from abiotically “sulfurized” organic matter diffusing from sulfidic sediments. These sources differ in 34S/32S isotope ratios (δ34S values), with phytoplankton-produced DOS tracking marine sulfate (21‰) and sulfurized DOS mirroring sedimentary porewater sulfide (∼0 to –10‰). We measured the δ34S values of solid-phase extracted (SPE) DOM from marine water columns and porewater from sulfidic sediments. Marine DOM_SPE δ34S values ranged from 14.9‰ to 19.9‰ and C:S ratios from 153 to 303, with lower δ34S values corresponding to higher C:S ratios. Marine DOM_SPE samples showed consistent trends with depth: δ34S values decreased, C:S ratios increased, and δ13C values were constant. Porewater DOM_SPE was 34S-depleted (∼-0.6‰) and sulfur-rich (C:S ∼37) compared with water column samples. We interpret these trends as reflecting at most 20% (and on average ∼8%) contribution of abiotic sulfurized sources to marine DOS_SPE and conclude that sulfurized porewater is not a main component of oceanic DOS and DOM. We hypothesize that heterogeneity in δ34S values and C:S ratios reflects the combination of sulfurized porewater inputs and preferential microbial scavenging of sulfur relative to carbon without isotope fractionation. Our findings strengthen links between oceanic sulfur and carbon cycling, supporting a realization that organic sulfur, not just sulfate, is important to marine biogeochemistry. 

This research note illustrates the importance of a holistic approach to family demography and children’s well-being. Using the family configurations published in a previous study, we show that a configurational measure of family patterns predicts better the country-level proportion of stunted and wasted children across 75 low- and middle-income countries than 20 single family-related variables, world regions, and the Human Development Index. We contend that demographers need to do a better job of measuring social systems because individuals’ choices are influenced by contexts that are better represented with measures that capture multiple related factors (holistic approach) than with a single variable (analytical approach). 

Understanding gene regulatory networks is essential to elucidate developmental processes and environmental responses. Here, we studied regulation of a maize (Zea mays) transcription factor gene using designer transcription activator-like effectors (dTALes), which are synthetic Type III TALes of the bacterial genus Xanthomonas and serve as inducers of disease susceptibility gene transcription in host cells. The maize pathogen Xanthomonas vasicola pv. vasculorum was used to introduce 2 independent dTALes into maize cells to induced expression of the gene glossy3 (gl3), which encodes a MYB transcription factor involved in biosynthesis of cuticular wax. RNA-seq analysis of leaf samples identified, in addition to gl3, 146 genes altered in expression by the 2 dTALes. Nine of the 10 genes known to be involved in cuticular wax biosynthesis were upregulated by at least 1 of the 2 dTALes. A gene previously unknown to be associated with gl3, Zm00001d017418, which encodes aldehyde dehydrogenase, was also expressed in a dTALe-dependent manner. A chemically induced mutant and a CRISPR-Cas9 mutant of Zm00001d017418 both exhibited glossy leaf phenotypes, indicating that Zm00001d017418 is involved in biosynthesis of cuticular waxes. Bacterial protein delivery of dTALes proved to be a straightforward and practical approach for the analysis and discovery of pathway-specific genes in maize.