FDH Knockout and TsFDH Transformation Led to Enhanced Growth Rate of Escherichia Coli

Background:Increased Atmospheric CO2 to over 400 ppm has prompted global climate irregularities. Reducing the released CO2 from biotechnological processes could remediate these phenomena. In this study, we sought to find a solution to reduce the amount of CO2 in the process of growth and reproduction by preventing the conversion of formic acid into CO2.Results:The (bio)chemical conversion of formic acid to CO2 is a key reaction. Therefore, we compared the growth of BL21, being a subfamily of K12, alongside two strains in which two different genes related to the formate metabolism were deleted, in complex and simple media. Experimental results were entirely consistent with metabolic predictions. Subsequently, the knockout bacteria grew more efficiently than BL21. Interestingly, TsFDH, a formate dehydrogenase with the tendency of converting CO2 to formate, increased the growth of all strains compared with cells without the TsFDH. Most mutants grew in a simple medium containing glycerol, which showed that glycerol is the preferred carbon source compared to glucose for the growth of E. coli. Conclusion:These results explain the reasons for the inconsistency of predictions in previous metabolic models that declared glycerol as a suitable carbon source for the growth of E. coli but failed to achieve it in practice. To conduct a more mechanistic evaluation of our observations, RNA sequencing data analysis was conducted on an E. coli RNA-seq dataset. The gene expression correlation outcome revealed the increased expression levels of several genes related to protein biosynthesis and glycerol degradation as a possible explanation of our observations.

Differentially expressed genes reflect disease-induced rather than disease-causing changes in the transcriptome

Comparing transcript levels between healthy and diseased individuals allows the identification of differentially expressed genes, which may be causes, consequences or mere correlates of the disease under scrutiny. We propose a method to decompose the observational correlation between gene expression and phenotypes driven by confounders, forward- and reverse causal effects. The bi-directional causal effects between gene expression and complex traits are obtained by Mendelian Randomization integrating summary-level data from GWAS and whole-blood eQTLs. Applying this approach to complex traits reveals that forward effects have negligible contribution. For example, BMI- and triglycerides-gene expression correlation coefficients robustly correlate with trait-to-expression causal effects (rBMI = 0.11, PBMI = 2.0 × 10−51 and rTG = 0.13, PTG = 1.1 × 10−68), but not detectably with expression-to-trait effects. Our results demonstrate that studies comparing the transcriptome of diseased and healthy subjects are more prone to reveal disease-induced gene expression changes rather than disease causing ones.

The report of anthocyanins in the betalain-pigmented genus Hylocereus is not well evidenced and is not a strong basis to refute the mutual exclusion paradigm

Here we respond to the paper entitled “Contribution of anthocyanin pathways to fruit flesh coloration in pitayas” (Fan et al., BMC Plant Biol 20:361, 2020). In this paper Fan et al. 2020 propose that the anthocyanins can be detected in the betalain-pigmented genus Hylocereus, and suggest they are responsible for the colouration of the fruit flesh. We are open to the idea that, given the evolutionary maintenance of fully functional anthocyanin synthesis genes in betalain-pigmented species, anthocyanin pigmentation might co-occur with betalain pigments, as yet undetected, in some species. However, in absence of the LC-MS/MS spectra and co-elution/fragmentation of the authentic standard comparison, the findings of Fan et al. 2020 are not credible. Furthermore, our close examination of the paper, and re-analysis of datasets that have been made available, indicate numerous additional problems. Namely, the failure to detect betalains in an untargeted metabolite analysis, accumulation of reported anthocyanins that does not correlate with the colour of the fruit, absence of key anthocyanin synthesis genes from qPCR data, likely mis-identification of key anthocyanin genes, unreproducible patterns of correlated RNAseq data, lack of gene expression correlation with pigmentation accumulation, and putative transcription factors that are weak candidates for transcriptional up-regulation of the anthocyanin pathway.

Blood RNA sequencing shows overlapping gene expression across COPD phenotype domains

RationaleCOPD can be assessed using multidimensional grading systems with components from three domains: pulmonary function tests, symptoms and systemic features. Clinically, measures may be used interchangeably, though it is not known if they share similar pathobiology.ObjectiveTo use RNA sequencing (RNA-seq) to determine if there is an overlap in the underlying biological mechanisms and consequences driving different components of the multidimensional grading systems.MethodsWhole blood was collected for RNA-seq from current and former smokers in the Genetic Epidemiology of COPD study. We tested the overlap in gene expression and biological pathways associated with case–control status and quantitative COPD phenotypes within and between the three domains.ResultsIn 2647 subjects, there were 3030 genes differentially expressed in any of the three domains or case–control status. There were five genes that overlapped between the three domains and case–control status, including G protein-coupled receptor 15(GPR15), sestrin 1 (SESN1) and interferon-induced guanylate-binding protein 1 (GBP1), which were associated with longitudinal decline in FEV1. The overlap between the three domains was enriched for pathways related to cellular components.ConclusionsWe identified gene sets and pathways that overlap between 12 COPD-related phenotypes and case–control status. There were no pathways represented in the overlap between the three domains and case–control status, but we identified multiple genes that demonstrated a consistent pattern of expression across several of the phenotypes. Patterns of gene expression correlation were generally similar to the correlation of clinical phenotypes in the PFT and symptom domains but not the systemic features.

Gene Expression Correlation Analysis Reveals MYC-NAC Regulatory Network in Cotton Pigment Gland Development

Plant NAC (NAM, ATAF1/2, and CUC2) family is involved in various development processes including Programmed Cell Death (PCD) associated development. However, the relationship between NAC family and PCD-associated cotton pigment gland development is largely unknown. In this study, we identified 150, 153 and 299 NAC genes in newly updated genome sequences of G. arboreum, G. raimondii and G. hirsutum, respectively. All NAC genes were divided into 8 groups by the phylogenetic analysis and most of them were conserved during cotton evolution. Using the vital regulator of gland formation GhMYC2-like as bait, expression correlation analysis screened out 6 NAC genes which were low-expressed in glandless cotton and high-expressed in glanded cotton. These 6 NAC genes acted downstream of GhMYC2-like and were induced by MeJA. Silencing CGF1(Cotton Gland Formation1), another MYC-coding gene, caused almost glandless phenotype and down-regulated expression of GhMYC2-like and the 6 NAC genes, indicating a MYC-NAC regulatory network in gland development. In addition, predicted regulatory mechanism showed that the 6 NAC genes were possibly regulated by light, various phytohormones and transcription factors as well as miRNAs. The interaction network and DNA binding sites of the 6 NAC transcription factors were also predicted. These results laid the foundation for further study of gland-related genes and gland development regulatory network.

A novel bioinformatics approach to reveal the role of circadian oscillations in AD development

Altered circadian gene expression may contribute to Alzheimer’s disease (AD) progression. Unfortunately, sampling the central nervous system (CNS) at multiple time points is not feasible. Moreover, there are no AD-related time-series transcriptome datasets available for studying these circadian patterns and their impacts on AD development. In this study, we introduce a novel computational platform, Event-driven Sample Ordering for Circadian Variation Detection (ESOCVD), to reveal rhythmic patterns of gene expression of AD using untimed transcriptome datasets. ESOCVD was applied to 20 untimed gene expression profiles of 16 brain regions from approximately 3000 AD patients in public transcriptome databases. Our analysis revealed five types of circadian alteration patterns in ~2,000 circadian genes in different brain regions of AD patients. Further analyses of additional databases confirmed that our analytical platform can be applied to identify the evolutionary dynamics of circadian variation during the process of AD development. Through the gene expression correlation analysis for our 8 circadian genes identified from AMP-AD MSBB cohorts, we identified stage-specifically enriched biological processes with anticipated context. Gene expression analysis of AD mouse brain tissues further substantiated the predictions of the ESOCVD model. In summary, ESOCVD is highly versatile in bridging circadian research and precision medicine.

Multiple lineages, same molecular basis: task specialization is commonly regulated across all eusocial bee groups

A striking feature of advanced insect societies is the existence of workers that forgo reproduction. Two broad types of workers exist in eusocial bees: nurses which care for their young siblings and the queen, and foragers who guard the nest and forage for food. Comparisons between this two worker subcastes have been performed in honeybees, but data from other bees are scarce. To understand whether similar molecular mechanisms are involved in nurse-forager differences across distinct species, we compared gene expression and DNA methylation profiles between nurses and foragers of the buff-tailed bumblebee Bombus terrestris and of the stingless bee Tetragonisca angustula. These datasets were then discussed comparatively to previous findings on honeybees. Our analyses revealed that although the expression pattern of genes is often species-specific, many of the biological processes and molecular pathways involved are common. Moreover, DNA methylation and gene expression correlation were dependent on the nucleotide context.

Identification of the associated expression patterns as potential predictive markers for cancer prognosis

Dysregulated gene expression can develop as a consequence of uncontrolled alterations of tumor cells. Analysis of these abnormal alterations will improve our understanding of the tumor development and reveal the corresponding clinical associations. It is well known that multiple genetic abnormalities could be observed in the same tumor, however, the interactions between those abnormal events are rarely analyzed. To address this problem, we constructed a novel gene expression correlation network by integrating the transcriptomes of 5,001 cancer patients from 22 cancer types. We investigated how the change of associated expression pattern (AEP), which describe certain associations between gene expression, could affect the cancer patient’s prognosis. Consequently, we identified an AEP composed of mitosis-related gene expressions, which is significantly correlated with overall survival in most cancer types. In particular, the AEPs could present the association between gene expressions and show distinct effects on prognosis prediction for cancer patients, suggesting that AEP analysis is indispensable to uncover the complex interactions of abnormal gene expressions in tumor development.

Identification of Long Non-Coding RNA-Associated Competing Endogenous RNA Network in the Differentiation of Chicken Preadipocytes

Emerging evidence indicates that long noncoding RNAs (lncRNAs) play important roles in the regulation of cell differentiation by acting as competing endogenous RNA (ceRNA). However, the regulatory mechanisms of lncRNA and the lncRNA-associated ceRNA network involved in adipogenic differentiation of chicken preadipocytes remain elusive. Here, we first constructed the chicken preadipocyte in vitro induction model. Then, we identified differentially expressed lncRNAs (DELs), miRNAs (DEMis), and mRNAs (DEMs) between differentiated and undifferentiated preadipocytes. Furthermore, we constructed the lncRNA associated ceRNA network by gene expression correlation analysis and target prediction of DELs, DEMis, and DEMs. Finally, we determined twelve candidate lncRNA-miRNA-mRNA interactions from the lncRNA associated ceRNA network. Eight out of the twelve interactions were validated by RT-qPCR, indicating their potential role in the regulation of chicken preadipocytes differentiation. Among the eight interactions, TCONS_00026544-gga-miR-128-1-5p-RASD1, TCONS_00055280-gga-miR-135a-5p-JAM3, TCONS_00055280-gga-miR-135a-5p-GPR133, TCONS_00055280-gga-miR-135a-5p-CLDN1, and TCONS_00055280-gga-miR-135a-5p-TMEM123 may promote adipogenic differentiation of chicken preadipocytes while TCONS_00057272-gga-miR-146a-3p-FOXO6, TCONS_00057242-gga-miR-6615-3p-FOXO6, and TCONS_00057242-gga-miR-6615-3p-ENSGALT00000043224 have the opposite effects. Our results not only provide novel insights into ceRNA roles of lncRNAs in chicken preadipocytes differentiation and but also contribute to a better understanding of chicken fat deposition.