No abstract

No abstract

Quorum sensing (QS) is a cell-cell communication mechanism that connects members in various microbial systems. Conventionally, a small number of QS entries are collected for specific microbes, which is far from being able to fully depict communication-based complex microbial interactions in human gut microbiota. In this study, we propose a systematic workflow including three modules and the use of machine learning-based classifiers to collect, expand, and mine the QS-related entries. Furthermore, we develop the Quorum Sensing of Human Gut Microbes (QSHGM) database (http://www.qshgm.lbci.net/) including 28,567 redundancy removal entries, to bridge the gap between QS repositories and human gut microbiota. With the help of QSHGM, various communication-based microbial interactions can be searched and a QS communication network (QSCN) is further constructed and analysed for 818 human gut microbes. This work contributes to the establishment of the QSCN which may form one of the key knowledge maps of the human gut microbiota, supporting future applications such as new manipulations to synthetic microbiota and potential therapies to gut diseases.

Quorum sensing interference (QSI), the disruption and manipulation of quorum sensing (QS) in the dynamic control of bacteria populations could be widely applied in synthetic biology to realize dynamic metabolic control and develop potential clinical therapies. Conventionally, limited QSI molecules (QSIMs) were developed based on molecular structures or for specific QS receptors, which are in short supply for various interferences and manipulations of QS systems. In this study, we developed QSIdb (http://qsidb.lbci.net/), a specialized repository of 633 reported QSIMs and 73 073 expanded QSIMs including both QS agonists and antagonists. We have collected all reported QSIMs in literatures focused on the modifications of N-acyl homoserine lactones, natural QSIMs and synthetic QS analogues. Moreover, we developed a pipeline with SMILES-based similarity assessment algorithms and docking-based validations to mine potential QSIMs from existing 138 805 608 compounds in the PubChem database. In addition, we proposed a new measure, pocketedit, for assessing the similarities of active protein pockets or QSIMs crosstalk, and obtained 273 possible potential broad-spectrum QSIMs. We provided user-friendly browsing and searching facilities for easy data retrieval and comparison. QSIdb could assist the scientific community in understanding QS-related therapeutics, manipulating QS-based genetic circuits in metabolic engineering, developing potential broad-spectrum QSIMs and expanding new ligands for other receptors.

No abstract

Azadirachta indica (neem), an evergreen tree of the Meliaceae family, is a source of the potent biopesticide azadirachtin. The lack of a chromosome-level assembly impedes an in-depth understanding of its genome architecture and the comparative genomic analysis of A. indica. Here, a high-quality genome assembly of A. indica was constructed using a combination of data from Illumina, PacBio, and Hi-C technology, which is the first chromosome-scale genome assembly of A. indica. Based on the length of our assembly, the genome size of A. indica is estimated to be 281 Mb anchored to 14 chromosomes (contig N50 = 6 Mb and scaffold N50 = 19 Mb). The genome assembly contained 115 Mb repetitive elements and 25,767 protein-coding genes. Evolutional analysis revealed that A. indica didn’t experience any whole-genome duplication (WGD) event after the core eudicot γ event, but some genes and genome segment might likely experienced recent duplications. The secondary metabolite clusters, TPS genes, and CYP genes were also identified. Comparative genomic analysis revealed that most of the A. indica-specific TPS genes and CYP genes were located on the terpene-related clusters on chromosome 13. It is suggested that chromosome 13 may play an important role in the specific terpene biosynthesis of A. indica. The gene duplication events may be responsible for the terpene biosynthesis expansion in A. indica. The genomic dataset and genomic analysis created for A. indica will shed light on terpene biosynthesis in A. indica and facilitate comparative genomic research of the family Meliaceae.

No abstract

Background The dense structure of cellulose lowers its reactivity and hinders its applications. Concentrated sulfuric acid is an ideal solvent to dissolve cellulose and thus has been used widely to treat cellulose. However, the changes of cellulose after reaction with concentrated sulfuric acid at near-limit S/L ratio and its effect on enzymatic saccharification still need further investigation. Results In this study, the interactions between cellulose (Avicel) and 72% sulfuric acid at very low acid loading conditions of 1:2 to 1:3 (S/L ratio) were studied for the enhanced production of glucose. The Avicel gradually transformed from cellulose I structure to cellulose II structure during the sulfuric acid treatment. Other physicochemical characteristics of Avicel also changed dramatically, such as the degree of polymerization, particle size, crystallinity index, and surface morphology. After acid treatment, both the yield and productivity of glucose from cellulose increased significantly under a very low enzyme loading of 5 FPU/g-cellulose. The glucose yields for raw cellulose and acid-treated (30 min) were 57% and 85%, respectively. Conclusion Low loadings of concentrated sulfuric acid were proven to be effective to break the recalcitrance of cellulose for enzymatic saccharification. A positive correlation between cellulose CrI and glucose yield was found for concentrated sulfuric acid-treated cellulose, which was opposite to previous reports. Cellulose II content was found to be an important factor that affects the conversion of cellulose to glucose.