Dynamic sporulation gene co-expression networks for Bacillus subtilis 168 and the food-borne isolate Bacillus amyloliquefaciens: a transcriptomic model

Optimization of degradation conditions and analysis of degradation mechanism for nitrite by Bacillus aryabhattai 47

Excessive nitrite levels cause significant damage to aquaculture, making it crucial to explore green and reliable nitrite removal technologies. In this study, A Bacillus aryabhattai (designated as the strain 47) isolated from aquaculture wastewater was used as the experimental strain. The nitrite degradation conditions of the strain 47 were optimized, and the optimal conditions are: glucose was 12.74 g/L, fermented special soybean meal was 21.27 g/L, MgCl2 369 mg/L, pH 7.0, incubated at 30 °C with the inoculum size of 2 % and the rotation speed of 170 rpm. Under the optimal conditions, the nitrite concentration of the culture solution was 200 mg/L, and the nitrite removal rate reached 91.4 %. Meanwhile, the mechanism by which Mg2+ enhanced the nitrite degradation ability of the strain 47 was investigated by transcriptomics. An operon structure directed cellular trafficking of Mg2+, and then, the Mg2+-mediated catalytic reaction of multiple enzymes enhanced and improved cellular metabolic processes (e.g. the transport and metabolism of nitrite, central carbohydrate metabolism oxidative phosphorylation). At the same time, with the progress of cell metabolism, cells secreted a series of enzymes related to nitrite transport and metabolism to promote the metabolism of nitrite. And the process of the assimilated nitrate reduction pathway of nitrite degradation in the strain 47 was elaborated at the transcriptome level. This study provided a new insight into nitrite treatment mediated by microbial organisms.

High Resolution Analysis of Proteome Dynamics during Bacillus subtilis Sporulation

Bacillus subtilis vegetative cells switch to sporulation upon nutrient limitation. To investigate the proteome dynamics during sporulation, high-resolution time-lapse proteomics was performed in a cell population that was induced to sporulate synchronously. Here, we are the first to comprehensively investigate the changeover of sporulation regulatory proteins, coat proteins, and other proteins involved in sporulation and spore biogenesis. Protein co-expression analysis revealed four co-expressed modules (termed blue, brown, green, and yellow). Modules brown and green are upregulated during sporulation and contain proteins associated with sporulation. Module blue is negatively correlated with modules brown and green, containing ribosomal and metabolic proteins. Finally, module yellow shows co-expression with the three other modules. Notably, several proteins not belonging to any of the known transcription regulons were identified as co-expressed with modules brown and green, and might also play roles during sporulation. Finally, levels of some coat proteins, for example morphogenetic coat proteins, decreased late in sporulation.

Reconstruction and inference of the Lactococcus lactis MG1363 gene co-expression network

Lactic acid bacteria are Gram-positive bacteria used throughout the world in many industrial applications for their acidification, flavor and texture formation attributes. One of the species, Lactococcus lactis, is employed for the production of fermented milk products like cheese, buttermilk and quark. It ferments lactose to lactic acid and, thus, helps improve the shelf life of the products. Many physiological and transcriptome studies have been performed in L. lactis in order to comprehend and improve its biotechnological assets. Using large amounts of transcriptome data to understand and predict the behavior of biological processes in bacterial or other cell types is a complex task. Gene networks enable predicting gene behavior and function in the context of transcriptionally linked processes. We reconstruct and present the gene co-expression network (GCN) for the most widely studied L. lactis strain, MG1363, using publicly available transcriptome data. Several methods exist to generate and judge the quality of GCNs. Different reconstruction methods lead to networks with varying structural properties, consequently altering gene clusters. We compared the structural properties of the MG1363 GCNs generated by five methods, namely Pearson correlation, Spearman correlation, GeneNet, Weighted Gene Co-expression Network Analysis (WGCNA), and Sparse PArtial Correlation Estimation (SPACE). Using SPACE, we generated an L. lactis MG1363 GCN and assessed its quality using modularity and structural and biological criteria. The L. lactis MG1363 GCN has structural properties similar to those of the gold-standard networks of Escherichia coli K-12 and Bacillus subtilis 168. We showcase that the network can be used to mine for genes with similar expression profiles that are also generally linked to the same biological process.