The real success of the microarray studies came, however, when we discovered a medium supplement that dramatically enhanced
protein production for a chemically defined medium. This particular medium was characterized by high titers when monitored
over time. However, at a particular stage during the fermentation process, protein yields suddenly dropped off. We carefully
designed a time course microarray experiment, and mapped the resulting differentially expressed genes to their associated
metabolic pathways. To our surprise, analyses of interconverting pathways led to the identification of a particular amino
acid (see Figure 2). The ability to map differentially expressed genes to their associated pathways clearly made it possible
to "zoom" in" and identify a key component that led to medium optimization and that targeted a truly essential amino acid.
We also used microarrays to survey the dynamics of gene expression in media with varying productivities, as well as to examine
process conditions that enhanced productivity. The results identified genes within the cells cultured in media yielding high
product concentrations/titers that are related to growth and cell division and were expressed at significantly higher levels
compared to those cells grown in media yielding lower titers. This enabled the cells to remain viable longer at the end of
cultivation, when the cell concentration is highest, thus allowing more product molecules to accumulate.
Figure 2: Mapping differentially expressed genes onto their associated metabolic pathways led to the identification of a truly
essential amino acid directly involved with increased protein titers.
Despite the utility and versatility of DNA microarray technology, it only provides for a qualitative picture of the overall
transcriptome and only reveals the activity of genes for which probes are present on the array. Furthermore, based on many
recent research reports for both prokaryotic and eukaryotic organisms, we know that cell physiology, as well as many functional
cellular and biological processes, including cell cycle progression and induction and suppression of apoptosis, are not entirely
dependant on the level of gene expression, but rather controlled by upstream regulatory regions and the increasingly important
"non-coding" regions of the genome (e.g., microRNAs/small RNAs) (4,5). This is where NGG comes to the forefront.
One of the key technologies in NGG is Next Generation Sequencing (NGS). Recent technological advances in DNA sequencing have
dramatically improved overall throughput and quality and have led to the development of methods to characterize whole transcriptomes
of entire cell populations in a way that was never before possible (6–8). RNA–sequencing (mRNA–seq) involves the direct sequencing
of complementary DNAs (cDNAs) using high throughput, massively parallel NGS technologies (Illumina's Genome Analyzer IIx;
Illumina's HiSeq2000, Roche's 454 FLX system, to name a few), followed by mapping of the resulting sequencing reads to a reference
genome (see Figure 3 for a detailed RNA–seq work-flow diagram).
Figure 3: A detailed breakdown of a typical mRNA-seq work flow. This work flow is for a bacterial species for which comprehensive
genome information is available. Partially adapted from Wilhelm and Landry (2009).