Integration of absolute multi-omics reveals dynamic protein-to-RNA ratios and metabolic interplay within mixed-domain microbiomes.

While the field of microbiology has adapted to the study of complex microbiomes via modern meta-omics techniques, we have not updated our basic knowledge regarding the quantitative levels of DNA, RNA and protein molecules within a microbial cell, which ultimately control cellular function. Here we report the temporal measurements of absolute RNA and protein levels per gene within a mixed bacterial-archaeal consortium. Our analysis of this data reveals an absolute protein-to-RNA ratio of 102-104 for bacterial populations and 103-105 for an archaeon, which is more comparable to Eukaryotic representatives' humans and yeast. Furthermore, we use the linearity between the metaproteome and metatranscriptome over time to identify core functional guilds, hence using a fundamental biological feature (i.e., RNA/protein levels) to highlight phenotypical complementarity. Our findings show that upgrading multi-omic toolkits with traditional absolute measurements unlocks the scaling of core biological questions to dynamic and complex microbiomes, creating a deeper insight into inter-organismal relationships that drive the greater community function.

Photoperiodic control of seasonal growth is mediated by ABA acting on cell-cell communication.

In temperate and boreal ecosystems, seasonal cycles of growth and dormancy allow perennial plants to adapt to winter conditions. We show, in hybrid aspen trees, that photoperiodic regulation of dormancy is mechanistically distinct from autumnal growth cessation. Dormancy sets in when symplastic intercellular communication through plasmodesmata is blocked by a process dependent on the phytohormone abscisic acid. The communication blockage prevents growth-promoting signals from accessing the meristem. Thus, precocious growth is disallowed during dormancy. The dormant period, which supports robust survival of the aspen tree in winter, is due to loss of access to growth-promoting signals.

Differential gene expression in the olfactory bulb following exposure to the olfactory toxicant 2,6-dichlorophenyl methylsulphone and its 2,5-dichlorinated isomer in mice.

2,6-Dichlorophenyl methylsulphone and a number of structurally related chemicals are CYP-activated toxicants in the olfactory mucosa in mice and rats. This toxicity involves both the olfactory neuroepithelium and its subepithelial nerves. In addition, 2,6-dichlorophenyl methylsulphone induces glial acidic fibrillary protein expression (Gfap, a biomarker for gliosis) in the olfactory bulb, as well as long-lasting learning deficits and changes in spontaneous behavior in mice and rats. So far the 2,5-dichlorinated isomer has not been reported to cause toxicity in the olfactory system, although it gives rise to transient changes in spontaneous behavior. In the present study we used 15k cDNA gene arrays and real-time RT-PCR to determine 2,6-dichlorophenyl methylsulphone-induced effects on gene expression in the olfactory bulb in mice. Seven days following a single ip dose of 2,6-dichlorophenyl methylsulphone, 56 genes were found to be differentially expressed in the olfactory bulb. Forty-one of these genes clustered into specific processes regulating, for instance, cell differentiation, cell migration and apoptosis. The genes selected for real-time RT-PCR were chosen to cover the range of B-values in the cDNA array analysis. Altered expression of Gfap, mt-Rnr2, Ncor1 and Olfml3 was confirmed. The expression of these genes was measured also in mice dosed with 2,5-dichlorophenyl methylsulphone, and mt-Rnr2 and Olfml3 were found to be altered also by this isomer. Combined with previous data, the results support the possibility that the persistent neurotoxicity induced by 2,6-dichlorophenyl methylsulphone in mice represents both an indirect and a direct effect on the brain. The 2,5-dichlorinated isomer, negative with regard to CYP-catalyzed toxicity in the olfactory mucosa, may prove useful to resolve this issue.

[New knowledge derived from measurement of gene expression with the DNA microarray method]. / Ny kunnskap fra måling av genuttrykk gjennom DNA-mikromatrise.

BACKGROUND: The cDNA microarray method offers the first possibility of obtaining a global understanding of biological processes in living organisms, by simultaneous read-outs of tens of thousands of mRNAs. Initial experiments suggest that genes with similar function have similar expression patterns. MATERIAL AND METHODS: Understanding this level of biological complexity will, however, require completely new approaches to data analysis. Computer science methods, such as data mining and knowledge discovery, can synthesize interpretable if-then rules that model the relation between gene expressions and functions and use the rules to classify unknown genes. The huge body of existing biological and medical knowledge makes it necessary to develop methods for extracting knowledge from such repositories. RESULTS: Models of relations between gene expressions and gene functions in a data set from a publicly available source are synthesized semiautomatically and applied to classify unknown genes. Encouraging results have been achieved. The method is applied in the analysis of data from our microarray system which has recently become operational. INTERPRETATION: The principles are of general importance and will be used to evaluate a wide range of complex data sets like decision support in clinical medicine, for situations in which physicians need to handle a large volume of data for each patient.

Predicting gene function from gene expressions and ontologies.

We introduce a methodology for inducing predictive rule models for functional classification of gene expressions from microarray hybridisation experiments. The basic learning method is the rough set framework for rule induction. The methodology is different from the commonly used unsupervised clustering approaches in that it exploits background knowledge of gene function in a supervised manner. Genes are annotated using Ashburner's Gene Ontology and the functional classes used for learning are mined from these annotations. From the original expression data, we extract a set of biologically meaningful features that are used for learning. A rule model is induced from the data described in terms of these features. Its predictive quality is fine-turned via cross-validation on subsets of the known genes prior to classification of unknown genes. The predictive and descriptive quality of such a rule model is demonstrated on the fibroblast serum response data previously analysed by Iyer et. al. Our analysis shows that the rules are capable of representing the complex relationship between gene expressions and function, and that it is possible to put forward high quality hypotheses about the function of unknown genes.