Hepatic Gene Expression During the Perinatal Transition in the Rat

During the immediate postnatal (PN) period, the liver, with its role in energy metabolism and macromolecule synthesis, plays a central role in the perinatal transition. Using RNA microarrays and several complementary computational analyses, we characterized changes in hepatic gene expression in the rat across a developmental period starting with the late gestation fetus (embryonic day 21), and including 30 min PN, 4 h PN, 12 h PN, 1 day PN, and 1 week after birth. Following subtle changes in gene expression at the earliest PN time point, there were marked changes that occurred between 4 and 12 h after birth. These reflected changes in multiple metabolic pathways, with expression of enzymes involved in glycolysis and cholesterol synthesis showing the greatest change. Over 50% of nuclear-encoded mitochondrial genes changed in the first 7 days of PN life, with 25% changing within the first 24 h. We also observed changes coinciding with a transient period of synchronous hepatocyte proliferation that we had observed previously, which occurs during the first PN week. Analysis for upstream regulators of gene expression indicated multiple initiating factors, including cell stress, hormones, and cytokines. Also implicated were multiple canonical transcription factor networks. We conclude that changes in gene expression during the early phases of the perinatal transition involve a complex, choreographed network of signaling pathways that respond to a variety of environmental stimuli. This transcriptomic response during the immediate PN period reflects a complex metabolic adaptive response that incorporates a panoply of signaling pathways and transcriptional regulators.

Inferring Molecular Processes Heterogeneity from Transcriptional Data

RNA microarrays and RNA-seq are nowadays standard technologies to study the transcriptional activity of cells. Most studies focus on tracking transcriptional changes caused by specific experimental conditions. Information referring to genes up- and downregulation is evaluated analyzing the behaviour of relatively large population of cells by averaging its properties. However, even assuming perfect sample homogeneity, different subpopulations of cells can exhibit diverse transcriptomic profiles, as they may follow different regulatory/signaling pathways. The purpose of this study is to provide a novel methodological scheme to account for possible internal, functional heterogeneity in homogeneous cell lines, including cancer ones. We propose a novel computational method to infer the proportion between subpopulations of cells that manifest various functional behaviour in a given sample. Our method was validated using two datasets from RNA microarray experiments. Both experiments aimed to examine cell viability in specific experimental conditions. The presented methodology can be easily extended to RNA-seq data as well as other molecular processes. Moreover, it complements standard tools to indicate most important networks from transcriptomic data and in particular could be useful in the analysis of cancer cell lines affected by biologically active compounds or drugs.

The Future of Biotechnology

Rational cell engineering aims to increase the quality and quantity of the manufactured products by developing improved cell lines. Previously the selection for improvements has been carried out on a trial and error basis: cells that produced high levels of the desired product were positively selected and kept, while low–producing lines were discarded. Little or no understanding of the underlying biological and molecular processes was applied in this selection. However, with the increasing availability of new tools a more informed process is now possible. Most of these tools developed in the past decade aim to understand the biological make–up of cells by employing high–throughput (HT) approaches. For example, the HT of DNA sequencing allows the analysis of the genome of a specific cell line (genomics), RNA microarrays give information about the different types of mRNAs in a cell (transcriptomics), HT analysis of the entire protein contents shows the individual protein species present in the cell (proteomics). The information that these –omics approaches provide can be very useful for a detailed understanding of the cell factories and hence their improvement for the manufacturing of desired products.