Bacteria have evolved an array of adaptive mechanisms enabling them to survive and grow in the presence of different environmental stresses. These mechanisms include either modifications of the membrane or changes in the overall energy status, cell morphology, and cell surface properties. Long-term adaptations are dependent on transcriptional regulation, the induction of anabolic pathways, and cell growth. However, to survive sudden environmental changes, bacterial short-term responses are essential to keep the cells alive after the occurrence of an environmental stress factor such as heat shock or the presence of toxic organic solvents. Thus far, two main short-term responses are known. On the one hand, a fast isomerization of cis into trans unsaturated fatty leads to a quick rigidification of the cell membrane, a mechanism known in some genera of Gram-negative bacteria. On the other hand, a fast, effective, and ubiquitously present countermeasure is the release of outer membrane vesicles (OMVs) from the cell surface leading to a rapid increase in cell surface hydrophobicity and finally to the formation of cell aggregates and biofilms. These immediate response mechanisms just allow the bacteria to stay physiologically active and to employ long-term responses to assure viability upon changing environmental conditions. Here, we provide insight into the two aforementioned rapid adaptive mechanisms affecting ultimately the cell envelope of Gram-negative bacteria.

No abstract

Co-expression networks have recently emerged as a useful approach for updating and improving gene annotation at a near-genome level. This is based on the hypothesis that function can be inferred by delineating transcriptional networks in which a gene of interest is embedded. In this study, we generated a co-expression network for the filamentous cell factoryAspergillus nigerfrom 128 RNA-seq experiments. We confirm that over 70% of the >14,000 A. niger genes are represented in this network and show that gene functions can be accurately predicted as evidenced by analysis of various control sub-networks. Our analyses further indicate that this RNA-seq co-expression network has a higher predictive power compared to the microarray co-expression network that we published in 2019. To demonstrate the potential of the new co-expression network to unveil complex and non-intuitive predictions for gene regulation phenomena, we provide here new insights into the temporal, spatial and metabolic expression profile that connects a secreted antifungal peptide with mycelial growth, asexual development, secondary metabolism and pectin degradation inA. niger. To empower biologists to generate or apply co-expression networks in the fungal kingdom and beyond, we also demonstrate that (i) high quality networks can be generated from only 32 transcriptional experiments; (ii) such low numbers of experiments can be safely compensated for by using higher thresholds for defining co-expression pairs; and (iii) a 'safety in numbers' rule applies, whereby experimental conditions have limited impacts on network content provided a certain number of experiments are included.

No abstract

No abstract