A quantitative framework reveals traditional laboratory growth is a highly accurate model of human oral infection

Bacterial behavior and virulence during human infection is difficult to study and largely unknown, as our vast knowledge of infection microbiology is primarily derived from studies using in vitro and animal models. Here, we characterize the physiology of Porphyromonas gingivalis, a periodontal pathogen, in its native environment using 93 published metatranscriptomic datasets from periodontally healthy and diseased individuals. P. gingivalis transcripts were more abundant in samples from periodontally diseased patients but only above 0.1% relative abundance in one-third of diseased samples. During human infection, P. gingivalis highly expressed genes encoding virulence factors such as fimbriae and gingipains (proteases) and genes involved in growth and metabolism, indicating that P. gingivalis is actively growing during disease. A quantitative framework for assessing the accuracy of model systems showed that 96% of P. gingivalis genes were expressed similarly in periodontitis and in vitro midlogarithmic growth, while significantly fewer genes were expressed similarly in periodontitis and in vitro stationary phase cultures (72%) or in a murine abscess infection model (85%). This high conservation in gene expression between periodontitis and logarithmic laboratory growth is driven by overall low variance in P. gingivalis gene expression, relative to other pathogens including Pseudomonas aeruginosa and Staphylococcus aureus. Together, this study presents strong evidence for the use of simple test tube growth as the gold standard model for studying P. gingivalis biology, providing biological relevance for the thousands of laboratory experiments performed with logarithmic phase P. gingivalis. Furthermore, this work highlights the need to quantitatively assess the accuracy of model systems.

The Vulnerability of the Developing Brain: Analysis of Highly Expressed Genes in Infant C57BL/6 Mouse Hippocampus in Relation to Phenotypic Annotation Derived From Mutational Studies

The hippocampus has been shown to have a major role in learning and memory, but also to participate in the regulation of emotions. However, its specific role(s) in memory is still unclear. Hippocampal damage or dysfunction mainly results in memory issues, especially in the declarative memory but, in animal studies, has also shown to lead to hyperactivity and difficulty in inhibiting responses previously taught. The brain structure is affected in neuropathological disorders, such as Alzheimer’s, epilepsy, and schizophrenia, and also by depression and stress. The hippocampus structure is far from mature at birth and undergoes substantial development throughout infant and juvenile life. The aim of this study was to survey genes highly expressed throughout the postnatal period in mouse hippocampus and which have also been linked to an abnormal phenotype through mutational studies to achieve a greater understanding about hippocampal functions during postnatal development. Publicly available gene expression data from C57BL/6 mouse hippocampus was analyzed; from a total of 5 time points (at postnatal day 1, 10, 15, 21, and 30), 547 genes highly expressed in all of these time points were selected for analysis. Highly expressed genes are considered to be of potential biological importance and appear to be multifunctional, and hence any dysfunction in such a gene will most likely have a large impact on the development of abilities during the postnatal and juvenile period. Phenotypic annotation data downloaded from Mouse Genomic Informatics database were analyzed for these genes, and the results showed that many of them are important for proper embryo development and infant survival, proper growth, and increase in body size, as well as for voluntary movement functions, motor coordination, and balance. The results also indicated an association with seizures that have primarily been characterized by uncontrolled motor activity and the development of proper grooming abilities. The complete list of genes and their phenotypic annotation data have been compiled in a file for easy access.

A spatial stochastic model reveals the role of supercoiling in transcription regulation

In Escherichia coli, translocation of RNA polymerase (RNAP) during transcription introduces supercoiling to DNA, which influences the initiation and elongation behaviors of RNAP. To quantify the role of supercoiling in transcription regulation, we develop a spatially resolved supercoiling model of transcription, describing RNAP-supercoiling interactions, topoisomerase activities, stochastic topological domain formation, and supercoiling diffusion in all transcription stages. This model establishes that transcription-induced supercoiling mediates the cooperation of co-transcribing RNAP molecules in highly expressed genes. It reveals that supercoiling transmits RNAP-accessible information through DNA and enables different RNAP molecules to communicate within and between genes. It thus predicts that a topological domain could serve as a transcription regulator, generating substantial transcription bursting and coordinating communications between adjacent genes in the domain. The model provides a quantitative platform for further theoretical and experimental investigations of how genome organization impacts transcription.

Context-Dependent Mutation Dynamics, Not Selection, Explains the Codon Usage Bias of Most Angiosperm Chloroplast Genes

Two competing proposals about the degree to which selection affects codon usage of angiosperm chloroplast genes are examined. The first, based on observations that codon usage does not match expectations under the naïve assumption that base composition will be identical at all neutral sites, is that selection plays a significant role. The second is that codon usage is determined almost solely by mutation bias and drift, with selection influencing only one or two highly expressed genes, in particular psbA. First it is shown that, as a result of an influence of neighboring base composition on mutation dynamics, compositional biases are expected to be widely divergent at different sites in the absence of selection. The observed mutation properties are then used to predict expected neutral codon usage biases and to show that observed deviations from the naïve expectations are in fact expected given the context-dependent mutational dynamics. It is also shown that there is a match between the observed and expected codon usage when context effects are taken into consideration, with psbA being a notable exception. Overall, the data support the model that selection is not a widespread factor affecting the codon usage of angiosperm chloroplast genes and highlight the need to have an accurate model of mutational dynamics.

Transcriptomic and Metabolomic Profiling Reveals the Effect of LED Light Quality on Fruit Ripening and Anthocyanin Accumulation in Cabernet Sauvignon Grape

Different light qualities have various impacts on the formation of fruit quality. The present study explored the influence of different visible light spectra (red, green, blue, and white) on the formation of quality traits and their metabolic pathways in grape berries. We found that blue light and red light had different effects on the berries. Compared with white light, blue light significantly increased the anthocyanins (malvidin-3-O-glucoside and peonidin-3-O-glucoside), volatile substances (alcohols and phenols), and soluble sugars (glucose and fructose), reduced the organic acids (citric acid and malic acid), whereas red light achieved the opposite effect. Transcriptomics and metabolomics analyses revealed that 2707, 2547, 2145, and 2583 differentially expressed genes (DEGs) and (221, 19), (254, 22), (189, 17), and (234, 80) significantly changed metabolites (SCMs) were filtered in the dark vs. blue light, green light, red light, and white light, respectively. According to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, most of the DEGs identified were involved in photosynthesis and biosynthesis of flavonoids and flavonols. Using weighted gene co-expression network analysis (WGCNA) of 23410 highly expressed genes, two modules significantly related to anthocyanins and soluble sugars were screened out. The anthocyanins accumulation is significantly associated with increased expression of transcription factors (VvHY5, VvMYB90, VvMYB86) and anthocyanin structural genes (VvC4H, Vv4CL, VvCHS3, VvCHI1, VvCHI2, VvDFR), while significantly negatively correlated with VvPIF4. VvISA1, VvISA2, VvAMY1, VvCWINV, VvβGLU12, and VvFK12 were all related to starch and sucrose metabolism. These findings help elucidate the characteristics of different light qualities on the formation of plant traits and can inform the use of supplemental light in the field and after harvest to improve the overall quality of fruit.

Microtubule-associated IQD9 guides cellulose synthase velocity to shape seed mucilage

Arabidopsis seeds release large capsules of mucilaginous polysaccharides, which are shaped by an intricate network of cellulosic microfibrils. Cellulose synthase complexes is guided by the microtubule cytoskeleton, but it is unclear which proteins mediate this process in the seed coat epidermis (SCE). Using reverse genetics, we identified IQ67 DOMAIN 9 (IQD9) and KINESIN LIGHT CHAIN-RELATED 1 (KLCR1) as two highly expressed genes during seed development and comprehensively characterized their roles for cell wall polysaccharide biosynthesis and cortical microtubule (MT) organization. Mutations in IQD9 as well as in KLCR1 lead to compact mucilage capsules with aberrant cellulose distribution, which can be rescued by transgene complementation. Double mutant analyses revealed that their closest paralogs (IQD10 and KLCR2, respectively) are not required for mucilage biosynthesis. IQD9 physically interacts with KLCR1 and localizes to cortical MTs to maintain their organization in SCE cells. Similar to the previously identified TONNEAU1 (TON1) RECRUITING MOTIF 4 (TRM4) protein, IQD9 is required to maintain the velocity of cellulose synthases. Our results demonstrate that IQD9, KLCR1 and TRM4 are MT-associated proteins that are required for seed mucilage architecture. This study provides the first direct evidence that members of the IQD, KLCR and TRM families have overlapping roles in guiding the distribution of cell wall polysaccharides. Therefore, SCE cells provide an attractive system to further decipher the complex genetic regulation of polarized cellulose deposition.

Association Analysis between Introns and mRNAs in Caenorhabditis elegans Genes with Different Expression Levels

Background: Introns are ubiquitous in pre-mRNA but are often overlooked. They also play an important role in the regulation of gene expression. Objective and Method : We mainly use the improved Smith-Waterman local alignment approach to compare the optimal matching regions between introns and mRNA sequences in Caenorhabditis elegans (C. elegans) genes with high and low expression. Results We found that the relative matching frequency distributions of all genes lie exactly between highly and lowly expressed genes, indicating that introns in highly and lowly expressed genes have different biological functions. Highly expressed genes have higher matching strengths on mRNA sequences than genes expressed at lower levels; the remarkably matched regions appear in UTR regions, particularly in the 3'UTR. The optimal matching frequency distributions have obvious differences in functional regions of the translation initiation and termination sites in highly and lowly expressed genes. The mRNA sequences with CpG islands tend to have stronger relative matching frequency distributions, especially in highly expressed genes. Additionally, the sequence characteristics of the optimal matched segments are consistent with those of the miRNAs, and they are considered a type of functional RNA segment. Conclusion: Introns in highly and lowly expressed genes contribute to the recognition translation initiation sites and translation termination sites. Moreover, our results suggest that the potential matching relationships between introns and mRNA sequences in highly and lowly expressed genes are significantly different and indicate that the matching strength correlates with the ability of introns to enhance gene expression.

Brains in Metamorphosis: Temporal Transcriptome Dynamics in Hatchery-Reared Flatfishes

Metamorphosis is a captivating process of change during which the morphology of the larva is completely reshaped to face the new challenges of adult life. In the case of fish, this process initiated in the brain has traditionally been considered to be a critical rearing point and despite the pioneering molecular work carried out in other flatfishes, the underlying molecular basis is still relatively poorly characterized. Turbot brain transcriptome of three developmental stages (pre-metamorphic, climax of metamorphosis and post-metamorphic) were analyzed to study the gene expression dynamics throughout the metamorphic process. A total of 1570 genes were differentially expressed in the three developmental stages and we found a specific pattern of gene expression at each stage. Unexpectedly, at the climax stage of metamorphosis, we found highly expressed genes related to the immune response, while the biological pathway enrichment analysis in pre-metamorphic and post-metamorphic were related to cell differentiation and oxygen carrier activity, respectively. In addition, our results confirm the importance of thyroid stimulating hormone, increasing its expression during metamorphosis. Based on our findings, we assume that immune system activation during the climax of metamorphosis stage could be related to processes of larval tissue inflammation, resorption and replacement, as occurs in other vertebrates.

Exploring the associations between transcript levels and fluxes in constraint-based models of metabolism

Background Several computational methods have been developed that integrate transcriptomics data with genome-scale metabolic reconstructions to increase accuracy of inferences of intracellular metabolic flux distributions. Even though existing methods use transcript abundances as a proxy for enzyme activity, each method uses a different hypothesis and assumptions. Most methods implicitly assume a proportionality between transcript levels and flux through the corresponding function, although these proportionality constant(s) are often not explicitly mentioned nor discussed in any of the published methods. E-Flux is one such method and, in this algorithm, flux bounds are related to expression data, so that reactions associated with highly expressed genes are allowed to carry higher flux values. Results Here, we extended E-Flux and systematically evaluated the impact of an assumed proportionality constant on model predictions. We used data from published experiments with Escherichia coli and Saccharomyces cerevisiae and we compared the predictions of the algorithm to measured extracellular and intracellular fluxes. Conclusion We showed that detailed modelling using a proportionality constant can greatly impact the outcome of the analysis. This increases accuracy and allows for extraction of better physiological information.