Identification of central genes and proteins in biomolecular networks provides credible candidates for pathway analysis, functional analysis, and essentiality prediction. The DiffSLC centrality measure predicts central and essential genes and proteins using a protein-protein interaction network. Network centrality measures prioritize nodes and edges based on their importance to the network topology. These measures helped identify critical genes and proteins in biomolecular networks. The proposed centrality measure, DiffSLC, combines the number of interactions of a protein and the gene coexpression values of genes from which those proteins were translated, as a weighting factor to bias the identification of essential proteins in a protein interaction network. Potentially essential proteins with low node degree are promoted through eigenvector centrality. Thus, the gene coexpression values are used in conjunction with the eigenvector of the network’s adjacency matrix and edge clustering coefficient to improve essentiality prediction. The outcome of this prediction is shown using three variations: (1) inclusion or exclusion of gene co-expression data, (2) impact of different coexpression measures, and (3) impact of different gene expression data sets. For a total of seven networks, DiffSLC is compared to other centrality measures using Saccharomyces cerevisiae protein interaction networks and gene expression data. Comparisons are also performed for the top ranked proteins against the known essential genes from the Saccharomyces Gene Deletion Project, which show that DiffSLC detects more essential proteins and has a higher area under the ROC curve than other compared methods. This makes DiffSLC a stronger alternative to other centrality methods for detecting essential genes using a protein-protein interaction network that obeys centrality-lethality principle. DiffSLC is implemented using the package in , and package in . The python package can be obtained from git.io/diffslcpy. The implementation and code to reproduce the analysis is available via git.io/diffslc.

The authors have retracted ‘Broadly Conserved Fungal Effector BEC1019 Suppresses Host Cell Death and Enhances Pathogen Virulence in Powdery Mildew of Barley (Hordeum vulgare L.)’ by Ehren Whigham, Shan Qi, Divya Mistry, Priyanka Surana, Ruo Xu, Gregory Fuerst, Clara Pliego, Laurence V. Bindschedler, Pietro D. Spanu, Julie A. Dickerson, Roger W. Innes, Dan Nettleton, Adam J. Bogdanove, and Roger P. Wise published in Mol. Plant-Microbe Interact. Vol. 28, pages 968–983, doi: 10.1094/MPMI-02-15-0027-FI. In a re-examination of some of the results of the bacterial type III secretion–based assays, the authors discovered a confounding effect of the titer of the bacterium used that had not been controlled for, rendering some of the experimental results presented in the paper inconclusive. For details, please see their letter to the editor [Carter et al. 2018]. This article was retracted on 24 May 2018.

Genes encoding early signaling events in pathogen defense often are identified only by their phenotype. Such genes involved in barley-powdery mildew interactions include Mla, specifying race-specific resistance; Rar1 (Required for Mla12-specified resistance1), and Rom1 (Restoration of Mla-specified resistance1). The HSP90-SGT1-RAR1 complex appears to function as chaperone in MLA-specified resistance, however, much remains to be discovered regarding the precise signaling underlying plant immunity. Genetic analyses of fastneutron mutants derived from CI 16151 (Mla6) uncovered a novel locus, designated Rar3 (Required for Mla6-specified resistance3). Rar3 segregates independent of Mla6 and Rar1, and rar3 mutants are susceptible to Blumeria graminis f. sp. hordei (Bgh) isolate 5874 (AVRa6), whereas, wild-type progenitor plants are resistant. Comparative expression analyses of the rar3 mutant vs. its wild-type progenitor were conducted via Barley1 GeneChip and GAIIx paired-end RNA-Seq. Whereas Rar1 affects transcription of relatively few genes; Rar3 appears to influence thousands, notably in genes controlling ATP binding, catalytic activity, transcription, and phosphorylation; possibly membrane bound or in the nucleus. eQTL analysis of a segregating doubled haploid population identified over two-thousand genes as being regulated by Mla (q value/FDR=0.00001), a subset of which are significant in Rar3 interactions. The intersection of datasets derived from mla-loss-of-function mutants, Mla-associated eQTL, and rar3-mediated transcriptome reprogramming are narrowing the focus on essential genes required for Mla-specified immunity. RightsWorks produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted. AbstractGenes encoding early signaling events in pathogen defense often are identified only by their phenotype. Such genes involved in barley-powdery mildew interactions include Mla, specifying race-specific resistance; Rar1 (Required for Mla12-specified resistance1), and Rom1 (Restoration of Mla-specified resistance1). The HSP90-SGT1-RAR1 complex appears to function as chaperone in MLA-specified resistance, however, much remains to be discovered regarding the precise signaling underlying plant immunity. Genetic analyses of fast-neutron mutants derived from CI 16151 (Mla6) uncovered a novel locus, designated Rar3 (Required for Mla6-specified resistance3). Rar3 segregates independent of Mla6 and Rar1, and rar3 mutants are susceptible to Blumeria graminis f. sp. hordei (Bgh) isolate 5874 (AVR a6 ), whereas, wild-type progenitor plants are resistant.Comparative expression analyses of the rar3 mutant vs. its wild-type progenitor were conducted via Barley1 GeneChip and GAIIx paired-end RNA-Seq. Whereas Rar1 affects transcription of relatively few genes; Rar3 appears to influence thousands, notably in genes controlling ATP binding, catalytic activity, transcription, and phosphorylation; possibly membrane bound or in th...

Network centrality measures allow ranking of nodes and edges based on their importance to the network topology. Closeness centrality [1] and shortest path betweenness centrality [2] are two of the most popular and well-utilized centrality measures that have provided good results [3,4,5,6]. Both of these centralities rely exclusively on topological features of the network [7] to calculate node importance. We propose an improvement to these path length based centrality measures that incorporate nodespecific metadata to provide biologically relevant node ranking. We choose gene annotations and gene ontology (GO) evidences as our metadata to highlight the new approach. Application of the newly proposed centrality measures to synthetic networks, and pathogen infected barley's gene co-expression networks resulted in a significantly better prioritization of the nodes. We compared our results against unmodified centrality measures applied to the same networks. Our proposed improvements provide a new avenue for tailoring centrality measures for biological networks, and hold great potential for further improvement of random walk based [8] and motif-based centrality [9] measures.