New antimycotic drugs are challenging to find, as potential target proteins may have close human orthologs. We here focus on identifying metabolic targets that are critical for fungal growth and have minimal similarity to targets among human proteins. We compare and combine here: (I) direct metabolic network modeling using elementary mode analysis and flux estimates approximations using expression data, (II) targeting metabolic genes by transcriptome analysis of condition-specific highly expressed enzymes, and (III) analysis of enzyme structure, enzyme interconnectedness (“hubs”), and identification of pathogen-specific enzymes using orthology relations. We have identified 64 targets including metabolic enzymes involved in vitamin synthesis, lipid, and amino acid biosynthesis including 18 targets validated from the literature, two validated and five currently examined in own genetic experiments, and 38 further promising novel target proteins which are non-orthologous to human proteins, involved in metabolism and are highly ranked drug targets from these pipelines.

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The plant hormone auxin regulates a whole repertoire of plant growth and development. Many plant-associated microorganisms, by virtue of their auxin production capability, mediate phytostimulation effects on plants. Recent studies, however, demonstrate diverse mechanisms whereby plant pathogens manipulate auxin biosynthesis, signaling and transport pathways to promote host susceptibility. Auxin responses have been coupled to their antagonistic and synergistic interactions with salicylic acid and jasmonate mediated defenses, respectively. Here, we discuss that a better understanding of auxin crosstalk to plant immune networks would enable us to engineer crop plants with higher protection and low unintended yield losses.

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The cardiovascular and immune systems undergo profound and intertwined alterations with aging. Recent studies have reported that an accumulation of memory and terminally differentiated T cells in elderly subjects can fuel myocardial aging and boost the progression of heart diseases. Nevertheless, it remains unclear whether the immunological senescence profile is sufficient to cause age-related cardiac deterioration or merely acts as an amplifier of previous tissue-intrinsic damage. Herein, we sought to decompose the causality in this cardio-immune crosstalk by studying young mice harboring a senescent-like expanded CD4+ T cell compartment. Thus, immunodeficient NSG-DR1 mice expressing HLA-DRB1*01:01 were transplanted with human CD4+ T cells purified from matching donors that rapidly engrafted and expanded in the recipients without causing xenograft reactions. In the donor subjects, the CD4+ T cell compartment was primarily composed of naïve cells defined as CCR7+CD45RO-. However, when transplanted into young lymphocyte-deficient mice, CD4+ T cells underwent homeostatic expansion, upregulated expression of PD-1 receptor and strongly shifted towards effector/memory (CCR7- CD45RO+) and terminally-differentiated phenotypes (CCR7-CD45RO-), as typically seen in elderly. Differentiated CD4+ T cells also infiltrated the myocardium of recipient mice at comparable levels to what is observed during physiological aging. In addition, young mice harboring an expanded CD4+ T cell compartment showed increased numbers of infiltrating monocytes, macrophages and dendritic cells in the heart. Bulk mRNA sequencing analyses further confirmed that expanding T-cells promote myocardial inflammaging, marked by a distinct age-related transcriptomic signature. Altogether, these data indicate that exaggerated CD4+ T-cell expansion and differentiation, a hallmark of the aging immune system, is sufficient to promote myocardial alterations compatible with inflammaging in juvenile healthy mice.

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Staphylococcus aureus is an important model organism and pathogen. This S. aureus proteome overview details shared and specific proteins and selected virulence-relevant protein complexes from representative strains of all three major clades. To determine the strain distribution and major clades we used a refined strain comparison combining ribosomal RNA, MLST markers, and looking at highly-conserved regions shared between strains. This analysis shows three sub-clades (A–C) for S. aureus. As calculations are complex and strain annotation is quite time consuming we compare here key representatives of each clade with each other: model strains COL, USA300, Newman, and HG001 (clade A), model strain N315 and Mu50 (clade B) and ED133 and MRSA252 (clade C). We look at these individual proteomes and compare them to a background of 64 S. aureus strains. There are overall 13,284 S. aureus proteins not part of the core proteome which are involved in different strain-specific or more general complexes requiring detailed annotation and new experimental data to be accurately delineated. By comparison of the eight representative strains, we identify strain-specific proteins (e.g., 18 in COL, 105 in N315 and 44 in Newman) that characterize each strain and analyze pathogenicity islands if they contain such strain-specific proteins. We identify strain-specific protein repertoires involved in virulence, in cell wall metabolism, and phosphorylation. Finally we compare and analyze protein complexes conserved and well-characterized among S. aureus (a total of 103 complexes), as well as predict and analyze several individual protein complexes, including structure modeling in the three clades.

Apart from some model organisms, the interactome of most organisms is largely unidentified. Highthroughput experimental techniques to determine protein-protein interactions (ppis) are resource intensive and highly susceptible to noise. Computational methods of PPI determination can accelerate biological discovery by identifying the most promising interacting pairs of proteins and by assessing the reliability of identified PPIs. Here we present a first in-depth study describing a global view of the ant Camponotus floridanus interactome. Although several ant genomes have been sequenced in the last eight years, studies exploring and investigating PPIs in ants are lacking. Our study attempts to fill this gap and the presented interactome will also serve as a template for determining PPIs in other ants in future. our C. floridanus interactome covers 51,866 non-redundant PPIs among 6,274 proteins, including 20,544 interactions supported by domain-domain interactions (DDIs), 13,640 interactions supported by DDIs and subcellular localization, and 10,834 high confidence interactions mediated by 3,289 proteins. These interactions involve and cover 30.6% of the entire C. floridanus proteome.understanding of PPIs in various organisms [11][12][13][14] . Here we used domain information, subcellular localization and isoform information to filter the preliminary global PPI network of C. floridanus reconstructed on stringent interolog based criteria. We focus on interactions predicted with high confidence to reduce noise. This conservative approach rejects 79.1% of the preliminary predicted interactions. We then explored the topologically important and evolutionary conserved proteins by analyzing the reconstructed interactome regarding cellular functions. Scientific RepoRtS |(2020) 10:2334 | https://doi.Assigning the confidence score. In fact, the preliminary network is filtered successively as mentioned above to reconstruct the final network, in this way the final network is already of high-confidence as many network biologists working on PPI networks have used DDIs and subcellular localization either to increase confidence or validate the interacting pairs. Here additionally we used topology-based method CAPPIC (cluster-based assessment of protein-protein interaction confidence) to assign the interaction confidence score 31,69 in the filtered network. In brief, CAPPIC calculations are based on the assumption that the proteins existing in the same network module are expected to have a higher number of common neighbours (neighbourhood interconnectedness 70 ), and a short path length inbetween 71 . For scoring the confidence level, CAPPIC first performs the clustering of the network using a robust clustering algorithm, Markov Cluster (MCL) 72 and then scores the interactions according to their level of compliance with the basic assumptions of topology-based methods. For the clustering we used an MCL inflation value of 1.5. Scores were classified to three subsets; low confidence score between 0 to 0.3, medium confidence score betwe...