Obstacles to bacterial survival and replication in the cytosol of host cells, and the mechanisms used by bacterial pathogens to adapt to this niche are not well understood. Listeria monocytogenes is a well-studied Gram-positive foodborne pathogen that has evolved to invade and replicate within the host cell cytosol; yet the mechanisms by which it senses and responds to stress to survive in the cytosol are largely unknown. To assess the role of the L. monocytogenes penicillin-binding-protein and serine/threonine associated (PASTA) kinase PrkA in stress responses, cytosolic survival and virulence, we constructed a ΔprkA deletion mutant. PrkA was required for resistance to cell wall stress, growth on cytosolic carbon sources, intracellular replication, cytosolic survival, inflammasome avoidance and ultimately virulence in a murine model of Listeriosis. In Bacillus subtilis and Mycobacterium tuberculosis, homologues of PrkA phosphorylate a highly conserved protein of unknown function, YvcK. We found that, similar to PrkA, YvcK is also required for cell wall stress responses, metabolism of glycerol, cytosolic survival, inflammasome avoidance and virulence. We further demonstrate that similar to other organisms, YvcK is directly phosphorylated by PrkA, although the specific site(s) of phosphorylation are not highly conserved. Finally, analysis of phosphoablative and phosphomimetic mutants of YvcK in vitro and in vivo demonstrate that while phosphorylation of YvcK is irrelevant to metabolism and cell wall stress responses, surprisingly, a phosphomimetic, nonreversible negative charge of YvcK is detrimental to cytosolic survival and virulence in vivo. Taken together our data identify two novel virulence factors essential for cytosolic survival and virulence of L. monocytogenes. Furthermore, our data demonstrate that regulation of YvcK phosphorylation is tightly controlled and is critical for virulence. Finally, our data suggest that yet to be identified substrates of PrkA are essential for cytosolic survival and virulence of L. monocytogenes and illustrate the importance of studying protein phosphorylation in the context of infection.

Recent advances on quantum computing hardware have pushed quantum computing to the verge of quantum supremacy. Here we bring together many-body quantum physics and quantum computing by using a method for strongly interacting two-dimensional systems, the Projected Entangled-Pair States, to realize an effective general-purpose simulator of quantum algorithms. We apply our method to study random quantum circuits, which are outstanding candidates to demonstrate quantum supremacy on quantum computers that supports nearest-neighbour gate operations on a two-dimensional configuration. Our approach allows to quantify precisely the memory usage and the time requirements of random quantum circuits, thus showing the frontier of quantum supremacy. Applying this general quantum circuit simulator we measured amplitudes for a 7 × 7 lattice of qubits with depth (1 + 40 + 1) and double-precision numbers in 31 minutes using less than 93 TB memory on the Tianhe-2 supercomputer. Our analytic complexity bounds also show that simulating a 8 × l circuit (l > 8) with depth (1 + 40 + 1), or a 10 × l (l > 10) circuit with depth (1 + 32 + 1) is within reach of current supercomputers.Quantum computers offer the promise of efficiently solving certain problems that are intractable for classical computers, most famously factorizing large numbers [1][2][3]. With the rapid progress of various quantum systems towards Noisy Intermediate-Scale Quantum computing devices [4][5][6][7][8][9][10][11], we are now on the verge of quantum supremacy [12], i.e. demonstrating that an actual quantum computer has the ability to do a computation that no classical computers can tackle, an important milestone in the field of computer science. Various candidates have been suggested to demonstrate quantum supremacy, such as BosonSampling [13,14], the instantaneous quantum polynomial protocol [15,16] and random quantum circuits (RQCs) [3,17] which demand less physical resources and are easier to implement compared to, for instance, factorization. The central aspect for all these near-term supremacy proof-of-principle computations, which poses fundamental limitations to classical computations, is that the quantum states produced, and from which we wish to sample configurations, live in a Hilbert space that grows exponentially with the system size.In view of recent progresses in quantum computing hardware, it is important to find effective ways to simulate accurately quantum algorithms on classical computers. While the quantum circuit simulator we present can tackle generic circuits, in the following we focus on RQCs. They consist of a series of single and two-qubit gates which are applied to different qubits in a particular order. A group of commuting gates which can be applied simultaneously constitute one layer of the circuit, and the more groups of operations that do not commute, the deeper the circuit is. The qualification of random circuit comes from the fact that the single-qubit gates applied are chosen at random from a small set of them (for more details about...

BackgroundRheumatoid arthritis (RA) is a chronic systemic autoimmune disorder characterized by inflammatory cell infiltration, leading to persistent synovitis and joint destruction. The pathogenesis of RA remains unclear. This study aims to explore the immune molecular mechanism of RA through bioinformatics analysis.MethodsFive microarray datasets and a high throughput sequencing dataset were downloaded. CIBERSORT algorithm was performed to evaluate immune cell infiltration in synovial tissues between RA and healthy control (HC). Wilcoxon test and Least Absolute Shrinkage and Selection Operator (LASSO) regression were conducted to identify the significantly different infiltrates of immune cells. Differentially expressed genes (DEGs) were screened by “Batch correction” and “RobustRankAggreg” methods. Functional correlation of DEGs were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Candidate biomarkers were identified by cytoHubba of Cytoscape, and their diagnostic effectiveness was predicted by Receiver Operator Characteristic Curve (ROC) analysis. The association of the identified biomarkers with infiltrating immune cells was explored using Spearman’s rank correlation analysis in R software.ResultsTen significantly different types of immune cells between RA and HC were identified. A total of 202 DEGs were obtained by intersection of DEGs screened by two methods. The function of DEGs were significantly associated with immune cells. Five hub genes (CXCR4, CCL5, CD8A, CD247, and GZMA) were screened by R package “UpSet”. CCL5+CXCR4 and GZMA+CD8A were verified to have the capability to diagnose RA and early RA with the most excellent specificity and sensitivity, respectively. The correlation between immune cells and biomarkers showed that CCL5 was positively correlated with M1 macrophages, CXCR4 was positively correlated with memory activated CD4+ T cells and follicular helper T (Tfh) cells, and GZMA was positively correlated with Tfh cells.ConclusionsCCL5, CXCR4, GZMA, and CD8A can be used as diagnostic biomarker for RA. GZMA-Tfh cells, CCL5-M1 macrophages, and CXCR4- memory activated CD4+ T cells/Tfh cells may participate in the occurrence and development of RA, especially GZMA-Tfh cells for the early pathogenesis of RA.