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ABSTRACT Some soil bacteria, called rhizobia, can interact symbiotically with legumes, in which they form nodules on the plant roots, where they can reduce atmospheric dinitrogen to ammonia, a form of nitrogen that can be used by growing plants. Rhizobium-plant combinations can differ in how successful this symbiosis is: for example, Sinorhizobium meliloti Rm1021 forms a relatively ineffective symbiosis with Medicago truncatula Jemalong A17, but Sinorhizobium medicae WSM419 is able to support more vigorous plant growth. Using proteomic data from free-living and symbiotic S. medicae WSM419, we previously identified a subset of proteins that were not closely related to any S. meliloti Rm1021 proteins and speculated that adding one or more of these proteins to S. meliloti Rm1021 would increase its effectiveness on M. truncatula A17. Three genes, Smed_3503, Smed_5985, and Smed_6456, were cloned into S. meliloti Rm1021 downstream of the E. coli lacZ promoter. Strains with these genes increased nodulation and improved plant growth, individually and in combination with one another. Smed_3503, renamed iseA ( i ncreased s ymbiotic e ffectiveness), had the largest impact, increasing M. truncatula biomass by 61%. iseA homologs were present in all currently sequenced S. medicae strains but were infrequent in other Sinorhizobium isolates. Rhizobium leguminosarum bv. viciae 3841 containing iseA led to more nodules on pea and lentil. Split-root experiments with M. truncatula A17 indicated that S. meliloti Rm1021 carrying the S. medicae iseA is less sensitive to plant-induced resistance to rhizobial infection, suggesting an interaction with the plant’s regulation of nodule formation. IMPORTANCE Legume symbiosis with rhizobia is highly specific. Rhizobia that can nodulate and fix nitrogen on one legume species are often unable to associate with a different species. The interaction can be more subtle. Symbiotically enhanced growth of the host plant can differ substantially when nodules are formed by different rhizobial isolates of a species, much like disease severity can differ when conspecific isolates of pathogenic bacteria infect different cultivars. Much is known about bacterial genes essential for a productive symbiosis, but less is understood about genes that marginally improve performance. We used a proteomic strategy to identify Sinorhizobium genes that contribute to plant growth differences that are seen when two different strains nodulate M. truncatula A17. These genes could also alter the symbiosis between R. leguminosarum bv. viciae 3841 and pea or lentil, suggesting that this approach identifies new genes that may more generally contribute to symbiotic productivity. 

ABSTRACT Symbiotic nitrogen fixation (SNF) in the interaction between the soil bacteria Sinorhizobium meliloti and legume plant Medicago sativa is carried out in specialized root organs called nodules. During nodule development, each symbiont must drastically alter their proteins, transcripts, and metabolites in order to support nitrogen fixation. Moreover, bacteria within the nodules are under stress, including challenges by plant antimicrobial peptides, low pH, limited oxygen availability, and strongly reducing conditions, all of which challenge proteome integrity. S. meliloti stress adaptation, proteome remodeling, and quality control are controlled in part by the large oligomeric protease complexes HslUV and ClpXP1. To improve understanding of the roles of S. meliloti HslUV and ClpXP1 under free-living conditions and in symbiosis with M. sativa , we generated Δ hslU , Δ hslV , Δ hslUV , and Δ clpP1 knockout mutants. The shoot dry weight of M. sativa plants inoculated with each deletion mutant was significantly reduced, suggesting a role in symbiosis. Further, slower free-living growth of the Δ hslUV and Δ clpP1 mutants suggests that HslUV and ClpP1 were involved in adapting to heat stress, the while Δ hslU and Δ clpP1 mutants were sensitive to kanamycin. All deletion mutants produced less exopolysaccharide and succinoglycan, as shown by replicate spot plating and calcofluor binding. We also generated endogenous C-terminal enhanced green fluorescent protein (eGFP) fusions to HslU, HslV, ClpX, and ClpP1 in S. meliloti . Using anti-eGFP antibodies, native coimmunoprecipitation experiments with proteins from free-living and nodule tissues were performed and analyzed by mass spectrometry. The results suggest that HslUV and ClpXP were closely associated with ribosomal and proteome quality control proteins, and they identified several novel putative protein-protein interactions. IMPORTANCE Symbiotic nitrogen fixation (SNF) is the primary means by which biologically available nitrogen enters the biosphere, and it is therefore a critical component of the global nitrogen cycle and modern agriculture. SNF is the result of highly coordinated interactions between legume plants and soil bacteria collectively referred to as rhizobia, e.g., Medicago sativa and S. meliloti , respectively. Accomplishing SNF requires significant proteome changes in both organisms to create a microaerobic environment suitable for high-level bacterial nitrogenase activity. The bacterial protease systems HslUV and ClpXP are important in proteome quality control, in metabolic remodeling, and in adapting to stress. This work shows that S. meliloti HslUV and ClpXP are involved in SNF, in exopolysaccharide production, and in free-living stress adaptation. 

The quality of data is extremely important for data analytics. Data quality tests typically involve checking constraints specified by domain experts. Existing approaches detect trivial constraint violations and identify outliers without explaining the constraints that were violated. Moreover, domain experts may specify constraints in an ad hoc manner and miss important ones. We describe an automated data quality test approach, ADQuaTe2, which uses an autoencoder to (1) discover constraints that may have been missed by experts, (2) label as suspicious those records that violate the constraints, and (3) provide explanations about the violations. An interactive learning technique incorporates expert feedback, which improves the accuracy. We evaluate the effectiveness of ADQuaTe2 on real-world datasets from health and plant domains. We also use datasets from the UCI repository to evaluate the improvement in the accuracy after incorporating ground truth knowledge. 

Abstract Objective In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations. Materials and Methods We developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using 4 federated Common Data Models. N3C data quality (DQ) review involves both automated and manual procedures. In the process, several DQ heuristics were discovered in our centralized context, both within the pipeline and during downstream project-based analysis. Feedback to the sites led to many local and centralized DQ improvements. Results Beyond well-recognized DQ findings, we discovered 15 heuristics relating to source Common Data Model conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness, and fitness for use. Of 56 sites, 37 sites (66%) demonstrated issues through these heuristics. These 37 sites demonstrated improvement after receiving feedback. Discussion We encountered site-to-site differences in DQ which would have been challenging to discover using federated checks alone. We have demonstrated that centralized DQ benchmarking reveals unique opportunities for DQ improvement that will support improved research analytics locally and in aggregate. Conclusion By combining rapid, continual assessment of DQ with a large volume of multisite data, it is possible to support more nuanced scientific questions with the scale and rigor that they require.