Multi-omic analysis tools for microbial metabolites prediction.

How to resolve the metabolic dark matter of microorganisms has long been a challenging problem in discovering active molecules. Diverse omics tools have been developed to guide the discovery and characterization of various microbial metabolites, which make it gradually possible to predict the overall metabolites for individual strains. The combinations of multi-omic analysis tools effectively compensates for the shortcomings of current studies that focus only on single omics or a broad class of metabolites. In this review, we systematically update, categorize and sort out different analysis tools for microbial metabolites prediction in the last five years to appeal for the multi-omic combination on the understanding of the metabolic nature of microbes. First, we provide the general survey on different updated prediction databases, webservers, or software that based on genomics, transcriptomics, proteomics, and metabolomics, respectively. Then, we discuss the essentiality on the integration of multi-omics data to predict metabolites of different microbial strains and communities, as well as stressing the combination of other techniques, such as systems biology methods and data-driven algorithms. Finally, we identify key challenges and trends in developing multi-omic analysis tools for more comprehensive prediction on diverse microbial metabolites that contribute to human health and disease treatment.

Deciphering and designing microbial communities by genome-scale metabolic modelling.

Microbial communities are shaped by the complex interactions among organisms and the environment. Genome-scale metabolic models (GEMs) can provide deeper insights into the complexity and ecological properties of various microbial communities, revealing their intricate interactions. Many researchers have modified GEMs for the microbial communities based on specific needs. Thus, GEMs need to be comprehensively summarized to better understand the trends in their development. In this review, we summarized the key developments in deciphering and designing microbial communities using different GEMs. A timeline of selected highlights in GEMs indicated that this area is evolving from the single-strain level to the microbial community level. Then, we outlined a framework for constructing GEMs of microbial communities. We also summarized the models and resources of static and dynamic community-level GEMs. We focused on the role of external environmental and intracellular resources in shaping the assembly of microbial communities. Finally, we discussed the key challenges and future directions of GEMs, focusing on the integration of GEMs with quorum sensing mechanisms, microbial ecology interactions, machine learning algorithms, and automatic modeling, all of which contribute to consortia-based applications in different fields.

Development of high-concentration labeled colloidal gold immunochromatographic test strips for detecting african swine fever virus p30 protein antibodies.

African Swine Fever (ASF), caused by the African swine fever virus (ASFV), has inflicted significant economic losses on the pig industry in China. The key to mitigating its impact lies in accurate screening and strict biosecurity measures. In this regard, the development of colloidal gold immunochromatographic test strips (CGITS) has proven to be an effective method for detecting ASFV antibodies. These test strips are based on the ASFV p30 recombinant protein and corresponding monoclonal antibodies. The design of the test strip incorporates a high-concentration colloidal gold-labeled p30 recombinant protein as the detection sensor, utilizing Staphylococcal Protein A (SPA) as the test line (T line), and p30 monoclonal antibody as the control line (C line). The sensitivity and specificity of the test strip were evaluated after optimizing the labeling concentration, pH, and protein dosage. The research findings revealed that the optimal colloidal gold labeling concentration was 0.05 %, the optimal pH was 8.4, and the optimal protein dosage was 10 µg/mL. Under these conditions, the CGITS demonstrated a detection limit of 1:512 dilution of ASFV standard positive serum, without exhibiting cross-reactivity with antibodies against other viral pathogens. Furthermore, the test strips remained stable for up to 20 days when stored at 50 °C and 4 °C. Comparatively, the CGITS outperformed commercial ELISA kits, displaying a sensitivity of 90.9 % and a specificity of 96.2 %. Subsequently, 108 clinical sera were tested to assess its performance. The data showed that the coincidence rate between the CGITS and ELISA was 93.5 %. In conclusion, the rapid colloidal gold test strip provides an efficient and reliable screening tool for on-site clinical detection of ASF in China. Its accuracy, stability, and simplicity make it a valuable asset in combating the spread of ASF and limiting its impact on the pig industry.

Enhanced Enzymatic Sugar Recovery of Dilute-Acid-Pretreated Corn Stover by Sodium Carbonate Deacetylation.

The prehydrolysate from dilute acid pretreatment of lignocellulosic feedstocks often contains inhibitory compounds that can seriously inhibit the subsequent enzymatic and fermentation processes. Acetic acid is one of the most representative toxic compounds. In this research, alkaline deacetylation of corn stover was carried out using sodium carbonate under mild conditions to selectively remove the acetyl groups of the biomass and reduce the toxicity of the prehydrolysate. The deacetylation process was optimized by adjusting factors such as temperature, treatment time, and sodium carbonate concentration. Sodium carbonate solutions (2~6 wt%) at 30~50 °C were used for the deacetylation step, followed by dilute acid pretreatment with 1.5% H2SO4 at 121 °C. Results showed that the acetyl content of the treated corn stover could be reduced up to 87%, while the hemicellulose loss remained low. The optimal deacetylation condition was found to be 40 °C, 6 h, and 4 wt% Na2CO3, resulting in a removal of 80.55% of the acetyl group in corn stover and a hemicellulose loss of 4.09%. The acetic acid concentration in the acid prehydrolysate decreased from 1.38 to 0.34 g/L. The enzymatic hydrolysis of solid corn stover and the whole slurry after pretreatment increased by 17% and 16%, respectively.

Swertia bimaculata moderated liver damage in mice by regulating intestine microbiota.

Swertia bimaculata (SB) is a medicinal herb in China having an array of therapeutic and biological properties. This study aimed to explore the attenuating effect of SB on carbon tetrachloride (CCl4) induced hepato-toxicity by regulation of gut microbiome in ICR mice. For this purpose, CCl4 was injected intraperitoneally in different mice groups (B, C, D and E) every 4th day for a period of 47 days. Additionally, C, D, and E groups received a daily dose (50 mg/kg, 100 mg/kg, and 200 mg/kg respectively) of Ether extract of SB via gavage for the whole study period. The results of serum biochemistry analysis, ELISA, H&E staining, and sequencing of the gut microbiome, indicated that SB significantly alleviates the CCl4-induced liver damage and hepatocyte degeneration. The serum levels of alanine transaminase, aspartate aminotransferase, malondialdehyde, interleukin 1 beta and tumor necrosis factor-alpha were significantly lower in SB treated groups compared to control while levels of glutathione peroxidase were raised. Also, the sequencing data indicate that supplementation with SB could restore the microbiome and its function in CCl4-induced variations in intestinal microbiome of mice by significantly downregulating the abundances of pathogenic intestinal bacteria species including Bacteroides, Enterococcus, Eubacterium, Bifidobacterium while upregulating the levels of beneficial bacteria like Christensenella in the gut. In conclusion, we revealed that SB depicts a beneficial effect against hepatotoxicity induced by CCl4 in mice through the remission of hepatic inflammation and injury, through regulation of oxidative stress, and by restoring gut microbiota dysbiosis.

The transformations of cellulose after concentrated sulfuric acid treatment and its impact on the enzymatic saccharification.

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.

Quorum sensing-based interactions among drugs, microbes, and diseases.

Many diseases and health conditions are closely related to various microbes, which participate in complex interactions with diverse drugs; nonetheless, the detailed targets of such drugs remain to be elucidated. Many existing studies have reported causal associations among drugs, gut microbes, or diseases, calling for a workflow to reveal their intricate interactions. In this study, we developed a systematic workflow comprising three modules to construct a Quorum Sensing-based Drug-Microbe-Disease (QS-DMD) database ( http://www.qsdmd.lbci.net/ ), which includes diverse interactions for more than 8,000 drugs, 163 microbes, and 42 common diseases. Potential interactions between microbes and more than 8,000 drugs have been systematically studied by targeting microbial QS receptors combined with a docking-based virtual screening technique and in vitro experimental validations. Furthermore, we have constructed a QS-based drug-receptor interaction network, proposed a systematic framework including various drug-receptor-microbe-disease connections, and mapped a paradigmatic circular interaction network based on the QS-DMD, which can provide the underlying QS-based mechanisms for the reported causal associations. The QS-DMD will promote an understanding of personalized medicine and the development of potential therapies for diverse diseases. This work contributes to a paradigm for the construction of a molecule-receptor-microbe-disease interaction network for human health that may form one of the key knowledge maps of precision medicine in the future.

Optical Properties of Ag Nanoparticle Arrays: Near-Field Enhancement and Photo-Thermal Temperature Distribution.

The near-field and photo-thermal properties of nanostructures have always been the focus of attention due to their wide applications in nanomaterials. In this work, we numerically investigate the near-field and photo-thermal temperature distribution in a nanoparticle array when the scattering light field among particles is considered. 'Hot spots', which represent strong electric field enhancement, were analyzed at the difference of the particle size, particle spacing and the polarization direction of the incident light. Interestingly, it is found that the position of the 'hot spots' does not rotate with the polarization direction of the incident light and always remains in the particle gaps along the line between particle centers. Moreover, the near-field is independent of the polarization in some special areas, and the factor of near-field enhancement keeps constant in these spots when the illumination polarization varies. As for photo-induced heating, our results show that both the temperature of the structure center and maximum temperature increase linearly with the particle number of the array while decreasing with the increase in particle spacing. This work provides some theoretical considerations for the near-field manipulation and photo-thermal applications of nanoarrays.

Microbe-bridged disease-metabolite associations identification by heterogeneous graph fusion.

MOTIVATION: Metabolomics has developed rapidly in recent years, and metabolism-related databases are also gradually constructed. Nowadays, more and more studies are being carried out on diverse microbes, metabolites and diseases. However, the logics of various associations among microbes, metabolites and diseases are limited understanding in the biomedicine of gut microbial system. The collection and analysis of relevant microbial bioinformation play an important role in the revelation of microbe-metabolite-disease associations. Therefore, the dataset that integrates multiple relationships and the method based on complex heterogeneous graphs need to be developed. RESULTS: In this study, we integrated some databases and extracted a variety of associations data among microbes, metabolites and diseases. After obtaining the three interconnected bilateral association data (microbe-metabolite, metabolite-disease and disease-microbe), we considered building a heterogeneous graph to describe the association data. In our model, microbes were used as a bridge between diseases and metabolites. In order to fuse the information of disease-microbe-metabolite graph, we used the bipartite graph attention network on the disease-microbe and metabolite-microbe bipartite graph. The experimental results show that our model has good performance in the prediction of various disease-metabolite associations. Through the case study of type 2 diabetes mellitus, Parkinson's disease, inflammatory bowel disease and liver cirrhosis, it is noted that our proposed methodology are valuable for the mining of other associations and the prediction of biomarkers for different human diseases.Availability and implementation: https://github.com/Selenefreeze/DiMiMe.git.

Machine learning aided construction of the quorum sensing communication network for human gut microbiota.

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.

Genomic Analysis Based on Chromosome-Level Genome Assembly Reveals an Expansion of Terpene Biosynthesis of Azadirachta indica.

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.

Combinational quorum sensing devices for dynamic control in cross-feeding cocultivation.

Quorum sensing (QS) offers cell density dependent dynamic regulations in cell culture through devices such as synchronized lysis circuit (SLC) and metabolic toggle switch (MTS). However, there is still a lack of studies on cocultivation with a combination of different QS-based devices. Taking the production of isopropanol and salidroside as case studies, we have mathematically modeled a comprehensive set of QS-regulated cocultivation schemes and constructed experimental combinations of QS devices, respectively, to evaluate their feasibility and optimality for regulating growth competition and corporative production. Glucose split ratio is proposed for the analysis of competition between cell growth and targeted production. Results show that the combination of different QS devices across multiple members offers a new tool with the potential to effectively coordinate synthetic microbial consortia for achieving high product titer in cross-feeding cocultivation. It is also evident that the performance of such systems is significantly affected by dynamic characteristics of chosen QS devices, carbon source control and the operational settings. This study offers insights for future applications of combinational QS devices in synthetic microbial consortia.

Vertical and horizontal quorum-sensing-based multicellular communications.

Quorum sensing (QS) plays an important role in both natural and synthetic microbial systems. The complexity of QS entails multilayer controls, biomolecular crosstalk, and population-based interactions. In this review, we divide complex QS-based interactions into vertical and horizontal interactions. With respect to the former, we discuss QS-based interactions among phages, bacteria, and hosts in natural microbial systems, which are based on various QS signals and hormones. With regard to the latter, we highlight manipulations of QS-based interactions for multicomponent synthetic microbial consortia. We further present the recent and emerging applications of manipulating these interactions (collectively referred to as 'QS communication networks') in natural and synthetic microbiota. Finally, we identify key challenges in engineering diverse QS communication networks for various future applications.

QSIdb: quorum sensing interference molecules.

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.

Quorum sensing for population-level control of bacteria and potential therapeutic applications.

Quorum sensing (QS), a microbial cell-to-cell communication process, dynamically regulates a variety of metabolism and physiological activities. In this review, we provide an update on QS applications based on autoinducer molecules including acyl-homoserine lactones (AHLs), auto-inducing peptides (AIPs), autoinducer 2 (AI-2) and indole in population-level control of bacteria, and highlight the potential in developing novel clinical therapies. We summarize the development in the combination of various genetic circuits such as genetic oscillators, toggle switches and logic gates with AHL-based QS devices in Gram-negative bacteria. An overview is then offered to the state-of-the-art of much less researched applications of AIP-based QS devices with Gram-positive bacteria, followed by a review of the applications of AI-2 and indole based QS for interspecies communication among microbial communities. Building on these general-purpose QS applications, we highlight the disruptions and manipulations of QS devices as potential clinical therapies for diseases caused by biofilm formation, antibiotic resistance and the phage invasion. The last part of reviewed literature is dedicated to mathematical modelling for QS applications. Finally, the key challenges and future perspectives of QS applications in monoclonal synthetic biology and synthetic ecology are discussed.

Simultaneous measurement of liquid surface tension and contact angle by light reflection.

We present an optical method of simultaneous measurement of liquid surface tension, contact angle, and the curved liquid surface shape, which uses the light reflection from this liquid surface due to the wettability. When an expanded and collimated laser beam is incident upon the curved liquid surfaces vertically, the special light reflection pattern, which includes a dark central region and a bright field outside, was observed. A critical spot on the curved liquid surface was found, and the dark field distribution is related to both the width of incidence beam and this critical spot. In our experiment, the different dark field distribution patterns were recorded when the width of the incidence beam changed. The liquid surface tension, contact angle, and the liquid surface shape were measured simultaneously. The proposed method is a new effective tool for present wetting characterization methods.