New antimycotic drugs are challenging to find, as potential target proteins may have close human orthologs. We focus here on metabolic targets as they are critical for fungal growth. We furthermore minimize the similarity of potential targets to 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.

Staphylococcus aureus (SA) causes infections including severe sepsis by antibiotic-resistant strains. It forms biofilms to protect itself from the host and antibiotics. Biofilm and planktonic lifestyle are regulated by a complex quorum sensing system (QS) with the central regulator agr. To study biofilm formation and QS we set up a Boolean node interaction network (94 nodes, 184 edges) that included different two component systems such as agr, sae and arl. Proteins such as sar, rot and sigB were included. Each gene node represents the resulting activity of its gene products (mRNA and protein). Network consistency was tested according to previous knowledge and the literature. Regulator mutation combinations (agr⁻, sae⁻, sae⁻/agr⁻, sigB⁺, sigB⁺/sae⁻) were tested in silico in the model and compared regarding system changes and responses to experimental gene expression data. High connectivity served as a guide to identify master regulators, and their detailed behaviour was studied both in vitro and in the model. System analysis showed two stable states, biofilm forming versus planktonic, with clearly different sub-networks turned on. Predicted node activity changes from the in silico model were in line with microarray gene expression data of different knockout strains. Additional in silico predictions about node activity and biofilm formation were compared to new in vitro experiments (northern blots and biofilm adherence assays) which confirmed these. Further experiments in silico as well as in vitro showed the sae locus as the central modulator of biofilm production. Sae knockout strains showed stronger biofilms. Wild type phenotype was rescued by sae complementation. The in silico network provides a theoretical model that agrees well with the presented experimental data on how integration of different inputs is achieved in the QS of SA. It faithfully reproduces the behaviour of QS mutants and their biofilm forming ability and allows predictions about mutations and mutation combinations for any node in the network. The model and simulations allow us to study QS and biofilm formation in SA including behaviour of MRSA strains and mutants. The in vitro and in silico evidence stresses the role of sae and agr in fine-tuning biofilm repression and/or SA dissemination.

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.