Protein Network Signatures Associated with Exogenous Biofuels Treatments in Cyanobacterium Synechocystis sp. PCC 6803

Expression of tardigrade disordered proteins impacts the tolerance to biofuels in a model cyanobacterium Synechocystis sp. PCC 6803

Tardigrades, known colloquially as water bears or moss piglets, are diminutive animals capable of surviving many extreme environments, even been exposed to space in low Earth orbit. Recently termed tardigrade disordered proteins (TDPs) include three families as cytoplasmic-(CAHS), secreted-(SAHS), and mitochondrial-abundant heat soluble (MAHS) proteins. How these tiny animals survive these stresses has remained relatively mysterious. Cyanobacteria cast attention as a "microbial factory" to produce biofuels and high-value-added chemicals due to their ability to photosynthesis and CO₂ sequestration. We explored a lot about biofuel stress and related mechanisms in Synechocystis sp. PCC 6803. The previous studies show that CAHS protein heterogenous expression in bacteria, yeast, and human cells increases desiccation tolerance in these hosts. In this study, the expression of three CAHS proteins in cyanobacterium was found to affect the tolerance to biofuels, while the tolerance to Cd²⁺ and Zn²⁺ were slightly affected in several mutants. A quantitative transcriptomics approach was applied to decipher response mechanisms at the transcriptional level further.

Contrast-Enhanced Computed Tomography–Based Radiogenomics Analysis for Predicting Prognosis in Gastric Cancer

Objective

The aim of this study is to identify prognostic imaging biomarkers and create a radiogenomics nomogram to predict overall survival (OS) in gastric cancer (GC).

Material

RNA sequencing data from 407 patients with GC and contrast-enhanced computed tomography (CECT) imaging data from 46 patients obtained from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) were utilized to identify radiogenomics biomarkers. A total of 392 patients with CECT images from the Nanfang Hospital database were obtained to create and validate a radiogenomics nomogram based on the biomarkers.

Methods

The prognostic imaging features that correlated with the prognostic gene modules (selected by weighted gene coexpression network analysis) were identified as imaging biomarkers. A nomogram that integrated the radiomics score and clinicopathological factors was created and validated in the Nanfang Hospital database. Nomogram discrimination, calibration, and clinical usefulness were evaluated.

Results

Three prognostic imaging biomarkers were identified and had a strong correlation with four prognostic gene modules (P < 0.05, FDR < 0.05). The radiogenomics nomogram (AUC = 0.838) resulted in better performance of the survival prediction than that of the TNM staging system (AUC = 0.765, P = 0.011; Delong et al.). In addition, the radiogenomics nomogram exhibited good discrimination, calibration, and clinical usefulness in both the training and validation cohorts.

Conclusions

The novel prognostic radiogenomics nomogram that was constructed achieved excellent correlation with prognosis in both the training and validation cohort of Nanfang Hospital patients with GC. It is anticipated that this work may assist in clinical preferential treatment decisions and promote the process of precision theranostics in the future.

Gene-Metabolite Network Analysis Revealed Tissue-Specific Accumulation of Therapeutic Metabolites in Mallotus japonicus

Mallotus japonicus is a valuable traditional medicinal plant in East Asia for applications as a gastrointestinal drug. However, the molecular components involved in the biosynthesis of bioactive metabolites have not yet been explored, primarily due to a lack of omics resources. In this study, we established metabolome and transcriptome resources for M. japonicus to capture the diverse metabolite constituents and active transcripts involved in its biosynthesis and regulation. A combination of untargeted metabolite profiling with data-dependent metabolite fragmentation and metabolite annotation through manual curation and feature-based molecular networking established an overall metabospace of M. japonicus represented by 2129 metabolite features. M. japonicus de novo transcriptome assembly showed 96.9% transcriptome completeness, representing 226,250 active transcripts across seven tissues. We identified specialized metabolites biosynthesis in a tissue-specific manner, with a strong correlation between transcripts expression and metabolite accumulations in M. japonicus. The correlation- and network-based integration of metabolome and transcriptome datasets identified candidate genes involved in the biosynthesis of key specialized metabolites of M. japonicus. We further used phylogenetic analysis to identify 13 C-glycosyltransferases and 11 methyltransferases coding candidate genes involved in the biosynthesis of medicinally important bergenin. This study provides comprehensive, high-quality multi-omics resources to further investigate biological properties of specialized metabolites biosynthesis in M. japonicus.

New insights into the function of the proteins IsiC and IsiD from Synechocystis sp. PCC 6803 under iron limitation

Iron is a common cofactor in biological processes such as respiration, photosynthesis, and nitrogen fixation. The genes isiC and isiD encode unknown proteins, and the growth of ΔisiC and ΔisiD mutants is inhibited under iron-deficient conditions. To study the regulatory mechanisms of IsiC and IsiD during iron starvation, we carried out transcriptome and metabolome sequencing. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the photosynthesis, nitrogen metabolism, and ABC transporter pathways play a vital role in regulating iron deficiency. Upon iron repletion, IsiC and IsiD also have a regulatory effect on these pathways. Additionally, KEGG analysis of the differential metabolites of wild type (WT) and mutants showed that they were all enriched in starch and sucrose metabolism after iron limitation. Weighted gene co-expression network analysis (WGCNA) constructed a co-expression network of differentially expressed genes with phenotypes and metabolites, and finally identified five modules. The turquoise module was positively correlated with iron deficiency. In contrast, the WT and blue module exhibited a negative correlation, and the mutants ΔisiC and ΔisiD were positively correlated with the gray and brown modules, respectively. WGCNA also analyzed the relationship between metabolites and phenotypes, and the green module was related to iron starvation. The co-expression network determined the hub genes and metabolites of each module. This study lays a foundation for a better understanding of cyanobacteria in response to iron deficiency. KEY POINTS: • Nitrogen metabolism and ABC transporters are involved in iron regulation. • Starch and sucrose metabolism is related to the regulation of iron deficiency. • WGCNA analyzes the correlation between genes and metabolites.

Four New Genes of Cyanobacterium Synechococcus elongatus PCC 7942 Are Responsible for Sensitivity to 2-Nonanone

Microbial volatile organic compounds (VOCs) are cell metabolites that affect many physiological functions of prokaryotic and eukaryotic organisms. Earlier we have demonstrated the inhibitory effects of soil bacteria volatiles, including ketones, on cyanobacteria. Cyanobacteria are very sensitive to ketone action. To investigate the possible molecular mechanisms of the ketone 2-nonanone influence on cyanobacterium Synechococcus elongatus PCC 7942, we applied a genetic approach. After Tn5-692 transposon mutagenesis, several 2-nonanone resistant mutants have been selected. Four different mutant strains were used for identification of the impaired genes (Synpcc7942_1362, Synpcc7942_0351, Synpcc7942_0732, Synpcc7942_0726) that encode correspondingly: 1) a murein-peptide ligase Mpl that is involved in the biogenesis of cyanobacteria cell wall; 2) a putative ABC transport system substrate-binding proteins MlaD, which participates in ABC transport system that maintains lipid asymmetry in the gram-negative outer membrane by aberrantly localized phospholipids transport from outer to inner membranes of bacterial cells; 3) a conserved hypothetical protein that is encoding by gene belonging to phage gene cluster in Synechococcus elongatus PCC 7942 genome; 4) a protein containing the VRR-NUC (virus-type replication-repair nuclease) domain present in restriction-modification enzymes involved in replication and DNA repair. The obtained results demonstrated that 2-nonanone may have different targets in Synechococcus elongatus PCC 7942 cells. Among them are proteins involved in the biogenesis and functioning of the cyanobacteria cell wall (Synpcc7942_1362, Synpcc7942_0351, Synpcc7942_0732) and protein participating in stress response at DNA restriction-modification level (Synpcc7942_0726). This paper is the first report about the genes that encode protein products, which can be affected by 2-nonanone.

Metabolomic analysis and lipid accumulation in a glucose tolerant Crypthecodinium cohnii strain obtained by adaptive laboratory evolution

Adaptive laboratory evolution (ALE) was commonly used for strain improvement. Crypthecodinium cohnii is known to accumulate lipids with a high fraction of docosahexaenoic acid (DHA). In order to improve DHA production under high substrate concentration, a glucose-tolerant C. cohnii strain was firstly obtained by ALE after 260 cycles for 650days with gradually increased glucose concentration. The results of lipids content showed that DHA-rich lipids accumulation in the evolved strain could increase by 15.49% at 45g/L glucose concentrations. To reveal mechanisms related to glucose tolerance of C. cohnii through ALE, metabolic profiles were then compared and the results showed that hub metabolites including glycerol, glutamic acid, malonic acid and succinic acid were positively regulated during ALE. The study demonstrated that metabolomic analysis complemented with ALE could be an effective and valuable strategy for basic mechanisms of molecular evolution and adaptive changes in C. cohnii.

Systematic and functional identification of small non-coding RNAs associated with exogenous biofuel stress in cyanobacterium Synechocystis sp. PCC 6803

BACKGROUND

The unicellular model cyanobacterium Synechocystis sp. PCC 6803 is considered a promising microbial chassis for biofuel production. However, its low tolerance to biofuel toxicity limits its potential application. Although recent studies showed that bacterial small RNAs (sRNAs) play important roles in regulating cellular processes in response to various stresses, the role of sRNAs in resisting exogenous biofuels is yet to be determined.

RESULTS

Based on genome-wide sRNA sequencing combined with systematic analysis of previous transcriptomic and proteomic data under the same biofuel or environmental perturbations, we report the identification of 133 trans-encoded sRNA transcripts with high-resolution mapping of sRNAs in Synechocystis, including 23 novel sRNAs identified for the first time. In addition, according to quantitative expression analysis and sRNA regulatory network prediction, sRNAs potentially involved in biofuel tolerance were identified and functionally confirmed by constructing sRNA overexpression or suppression strains of Synechocystis. Notably, overexpression of sRNA Nc117 revealed an improved tolerance to ethanol and butanol, while suppression of Nc117 led to increased sensitivity.

CONCLUSIONS

The study provided the first comprehensive responses to exogenous biofuels at the sRNA level in Synechocystis and opens an avenue to engineering sRNA regulatory elements for improved biofuel tolerance in the cyanobacterium Synechocystis.

WGCNA Application to Proteomic and Metabolomic Data Analysis

Progresses in mass spectrometric instrumentation and bioinformatics identification algorithms made over the past decades allow quantitative measurements of relative or absolute protein/metabolite amounts in cells in a high-throughput manner, which has significantly expedited the exploration into functions and dynamics of complex biological systems. However, interpretation of high-throughput data is often restricted by the limited availability of suitable computational methods and enough statistical power. While many computational methodologies have been developed in the past decades to address the issue, it becomes clear that network-focused rather than individual gene/protein-focused strategies would be more appropriate to obtain a complete picture of cellular responses. Recently, an R analytical package named as weighted gene coexpression network analysis (WGCNA) was developed and applied to high-throughput microarray or RNA-seq datasets since it provides a systems-level insights, high sensitivity to low abundance, or small fold changes genes without any information loss. The approach was also recently applied to proteomic and metabolomic data analysis. However, due to the fact that low coverage of the current proteomic and metabolomic analytical technologies, causing the format of datasets are often incomplete, the method needs to be modified so that it can be properly utilized for meaningful biologically interpretation. In this chapter, we provide a detailed introduction of the modified protocol and its tutorials for applying the WGCNA approach in analyzing proteomic and metabolomic datasets.

Cyanobacterial Biofuels: Strategies and Developments on Network and Modeling

Cyanobacteria, the phototrophic microorganisms, have attracted much attention recently as a promising source for environmentally sustainable biofuels production. However, barriers for commercial markets of cyanobacteria-based biofuels concern the economic feasibility. Miscellaneous strategies for improving the production performance of cyanobacteria have thus been developed. Among these, the simple ad hoc strategies resulting in failure to optimize fully cell growth coupled with desired product yield are explored. With the advancement of genomics and systems biology, a new paradigm toward systems metabolic engineering has been recognized. In particular, a genome-scale metabolic network reconstruction and modeling is a crucial systems-based tool for whole-cell-wide investigation and prediction. In this review, the cyanobacterial genome-scale metabolic models, which offer a system-level understanding of cyanobacterial metabolism, are described. The main process of metabolic network reconstruction and modeling of cyanobacteria are summarized. Strategies and developments on genome-scale network and modeling through the systems metabolic engineering approach are advanced and employed for efficient cyanobacterial-based biofuels production.