Intrabody-Induced Cell Death by Targeting the T. brucei Cytoskeletal Protein Tb BILBO1

Trypanosoma brucei belongs to a group of important zoonotic parasites. We investigated how these organisms develop their cytoskeleton (the internal skeleton that controls cell shape) and focused on an essential protein (BILBO1) first described in T. brucei .

Ypd1 Is an Essential Protein of the Major Fungal Pathogen Aspergillus fumigatus and a Key Element in the Phosphorelay That Is Targeted by the Antifungal Drug Fludioxonil

Aspergillus fumigatus is a major fungal pathogen causing life threatening infections in immunocompromised humans and certain animals. The HOG pathway is for two reasons interesting in this context: firstly, it is a stress signaling pathway that contributes to the ability of this pathogen to adapt to various stress conditions and secondly, it is the target of antifungal agents, such as fludioxonil or pyrrolnitrin. In this study, we demonstrate that Ypd1 is an essential protein in A. fumigatus. As the central component of the multistep phosphorelay it represents the functional link between the sensor histidine kinases and the downstream response regulators SskA and Skn7. A GFP-Ypd1 fusion was found to reside in both, the cytoplasm and the nucleus and this pattern was only slightly affected by fludioxonil. A strain in which the ypd1 gene is expressed from a tet-on promoter construct is unable to grow under non-inducing conditions and shows the characteristic features of A. fumigatus wild type hyphae treated with fludioxonil. Expression of wild type Ypd1 prevents this lethal phenotype, but expression of an Ypd1 mutant protein lacking the conserved histidine at position 89 was unable to do so, which confirms that A. fumigatus Ypd1 is a phosphotransfer protein. Generation of ypd1tet−on variants of several mutant strains revealed that the lethal phenotype associated with low amounts of Ypd1 depends on SskA, but not on TcsC or Skn7. The ΔsskA ypd1tet−on, but not the ΔsskAΔskn7 ypd1tet−on mutant, was sensitive to fludioxonil, which underlines the importance of Skn7 in this context. We finally succeeded to delete ypd1, but only if sskA and skn7 were both inactivated, not in a ΔsskA single mutant. Hence, a deletion of ypd1 and an inactivation of Ypd1 by fludioxonil result in similar phenotypes and the two response regulators SskA and Skn7 are involved in both processes albeit with a different relative importance.

Star Topology Convolution for Graph Representation Learning

<p>We present a novel graph convolutional method called star topology convolution (STC). This method makes graph convolution more similar to conventional convolutional neural networks (CNNs) in Euclidean feature spaces. STC learns subgraphs which have a star topology rather than learning a fixed graph like most spectral methods. Due to the properties of a star topology, STC is graph-scale free (without a fixed graph size constraint). It has fewer parameters in its convolutional filter and is inductive, so it is more flexible and can be applied to large and evolving graphs. The convolutional filter is learnable and localized, similar to CNNs in Euclidean feature spaces, and maintains a good weight sharing property. To test the method, STC was compared with state-of-the-art graph convolutional methods in a supervised learning setting on six node properties prediction benchmark datasets: Cora, Citeseer, Pubmed, PPI, Ogbn-Arxiv, and Ogbn-MAG. The experimental results showed that STC achieved state-of-the-art performance on all these datasets and maintained good robustness. In an essential protein identification task, STC outperformed state-of-the-art essential protein identification methods.</p>

Identification of Essential Protein Domains From High-density Transposon Insertion Sequencing

A first clue to gene function can be obtained by examining whether a gene is required for life in certain standard conditions, that is, whether a gene is essential. In bacteria, essential genes are usually identified by high-density transposon mutagenesis followed by sequencing of insertion sites (Tn-seq). These studies assign the term “essential” to whole genes rather than the protein domain sequences that confer the essential functions. However, genes can code for multiple protein domains that evolve their functions independently. Therefore, when essential genes code for more than one protein domain, only one of them could be essential. In this study, we defined this subset of genes as “essential domain-containing” (EDC) genes. Using a Tn-seq data set built-in Burkholderia cenocepacia K56-2, we developed an in silico pipeline to identify EDC genes and the essential protein domains they encode. We found forty candidate EDC genes and demonstrated growth defect phenotypes using CRISPR interference (CRISPRi). This analysis included two knockdowns of genes encoding the protein domains of unknown function DUF2213 and DUF4148. These essential domains are conserved in more than two hundred bacterial species, including human and plant pathogens. Together, our study suggests that essentiality should be assigned to individual protein domains rather than genes, contributing to a first functional characterization of protein domains of unknown function.

A novel essential protein identification method based on PPI networks and gene expression data

Background Some proposed methods for identifying essential proteins have better results by using biological information. Gene expression data is generally used to identify essential proteins. However, gene expression data is prone to fluctuations, which may affect the accuracy of essential protein identification. Therefore, we propose an essential protein identification method based on gene expression and the PPI network data to calculate the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network. Our experiments show that the method can improve the accuracy in predicting essential proteins. Results In this paper, we propose a new measure named JDC, which is based on the PPI network data and gene expression data. The JDC method offers a dynamic threshold method to binarize gene expression data. After that, it combines the degree centrality and Jaccard similarity index to calculate the JDC score for each protein in the PPI network. We benchmark the JDC method on four organisms respectively, and evaluate our method by using ROC analysis, modular analysis, jackknife analysis, overlapping analysis, top analysis, and accuracy analysis. The results show that the performance of JDC is better than DC, IC, EC, SC, BC, CC, NC, PeC, and WDC. We compare JDC with both NF-PIN and TS-PIN methods, which predict essential proteins through active PPI networks constructed from dynamic gene expression. Conclusions We demonstrate that the new centrality measure, JDC, is more efficient than state-of-the-art prediction methods with same input. The main ideas behind JDC are as follows: (1) Essential proteins are generally densely connected clusters in the PPI network. (2) Binarizing gene expression data can screen out fluctuations in gene expression profiles. (3) The essentiality of the protein depends on the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network.