Triangle network motifs predict complexes by complementing high-error interactomes with structural information

BackgroundA lot of high-throughput studies produce protein-protein interaction networks (PPINs) with many errors and missing information. Even for genome-wide approaches, there is often a low overlap between PPINs produced by different studies. Second-level neighbors separated by two protein-protein interactions (PPIs) were previously used for predicting protein function and finding complexes in high-error PPINs. We retrieve second level neighbors in PPINs, and complement these with structural domain-domain interactions (SDDIs) representing binding evidence on proteins, forming PPI-SDDI-PPI triangles.ResultsWe find low overlap between PPINs, SDDIs and known complexes, all well below 10%. We evaluate the overlap of PPI-SDDI-PPI triangles with known complexes from Munich Information center for Protein Sequences (MIPS). PPI-SDDI-PPI triangles have ~20 times higher overlap with MIPS complexes than using second-level neighbors in PPINs without SDDIs. The biological interpretation for triangles is that a SDDI causes two proteins to be observed with common interaction partners in high-throughput experiments. The relatively few SDDIs overlapping with PPINs are part of highly connected SDDI components, and are more likely to be detected in experimental studies. We demonstrate the utility of PPI-SDDI-PPI triangles by reconstructing myosin-actin processes in the nucleus, cytoplasm, and cytoskeleton, which were not obvious in the original PPIN. Using other complementary datatypes in place of SDDIs to form triangles, such as PubMed co-occurrences or threading information, results in a similar ability to find protein complexes.ConclusionGiven high-error PPINs with missing information, triangles of mixed datatypes are a promising direction for finding protein complexes. Integrating PPINs with SDDIs improves finding complexes. Structural SDDIs partially explain the high functional similarity of second-level neighbors in PPINs. We estimate that relatively little structural information would be sufficient for finding complexes involving most of the proteins and interactions in a typical PPIN

Multi-ancestry genome-wide association meta-analysis of Parkinson’s disease

\ua9 2023, This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply. Although over 90 independent risk variants have been identified for Parkinson’s disease using genome-wide association studies, most studies have been performed in just one population at a time. Here we performed a large-scale multi-ancestry meta-analysis of Parkinson’s disease with 49,049 cases, 18,785 proxy cases and 2,458,063 controls including individuals of European, East Asian, Latin American and African ancestry. In a meta-analysis, we identified 78 independent genome-wide significant loci, including 12 potentially novel loci (MTF2, PIK3CA, ADD1, SYBU, IRS2, USP8, PIGL, FASN, MYLK2, USP25, EP300 and PPP6R2) and fine-mapped 6 putative causal variants at 6 known PD loci. By combining our results with publicly available eQTL data, we identified 25 putative risk genes in these novel loci whose expression is associated with PD risk. This work lays the groundwork for future efforts aimed at identifying PD loci in non-European populations

Multi-ancestry genome-wide association meta-analysis of Parkinson's disease

Although over 90 independent risk variants have been identified for Parkinson's disease using genome-wide association studies, most studies have been performed in just one population at a time. Here we performed a large-scale multi-ancestry meta-analysis of Parkinson's disease with 49,049 cases, 18,785 proxy cases and 2,458,063 controls including individuals of European, East Asian, Latin American and African ancestry. In a meta-analysis, we identified 78 independent genome-wide significant loci, including 12 potentially novel loci (MTF2, PIK3CA, ADD1, SYBU, IRS2, USP8, PIGL, FASN, MYLK2, USP25, EP300 and PPP6R2) and fine-mapped 6 putative causal variants at 6 known PD loci. By combining our results with publicly available eQTL data, we identified 25 putative risk genes in these novel loci whose expression is associated with PD risk. This work lays the groundwork for future efforts aimed at identifying PD loci in non-European populations. Multi-ancestry genome-wide association analyses identify new risk loci for Parkinson's disease, and fine-mapping and co-localization analyses implicate candidate genes whose expression is associated with disease susceptibility

Genetic variation and environmental stability of grain mineral nutrient concentrations in Triticum dicoccoides under five environments

Nineteen wild emmer wheat [Triticum turgidum ssp. dicoccoides (Körn.) Thell.] genotypes were evaluated for the grain concentrations of phosphorous (P), potassium (K), sulfur (S), magnesium (Mg), calcium (Ca), zinc (Zn), manganese (Mn), iron (Fe) and cooper (Cu) under five different environments in Turkey and Israel. Each mineral nutrient has been investigated for the (1) genotype by environment (G × E) interactions, (2) genotype stability, (3) correlation among minerals and (4) mineral stability. Among the macronutrients analyzed, grain concentrations of Ca (range 338–2,034 mg kg−1) and S (range 0.18–0.43%) showed the largest variation. In the case of micronutrients, the largest variation was observed in the grain Mn concentration (range 13–87 mg kg−1). Grain concentrations of Fe and Zn also showed important variation (range 27–86 and 39–115 mg kg−1, respectively). Accessions with higher nutrient concentrations (especially Zn and Fe) had also greater grain weight, suggesting that higher grain Zn and Fe concentrations are not necessarily related to small grain size or weight. Analysis of variance showed that environment was the most important source of variation for K, S, Ca, Fe, Mn and Zn, explaining between 44 and 78% of the total variation and G × E explained between 20 and 40% of the total variation in all the minerals, except for S and Zn where its effect accounted for less than 16%. Genotype was the most important source of variation for Cu (explaining 38% of the total variation). However, genotype effect was also important for Mg, Mn, Zn and S. Sulfur and Zn showed the largest heritability values (77 and 72%, respectively). Iron exhibited low heritability and high ratio value between the G × E and genotype variance components , suggesting that specific adaptation for this mineral could be positively exploited. The wild emmer germplasm tested in the current study revealed some outstanding accessions (such as MM 5/4 and 24-39) in terms of grain Zn and Fe concentrations and environmental stability that can be used as potential donors to enhance grain micronutrient concentrations in wheats. Keywords Genotype × environment interaction - Grain quality - Micronutrients - Plant breeding - Mineral stability - Triticum turgidum ssp. dicoccoide