Biomedical Engineering Reference
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forefront of the bioprocessing industry, up until recently, a significant disadvantage
and obstacle to undertaking cellular function work with CHO cells was the lack
of genomic resources- outside expressed sequence tags (ESTs) (Kantardjieff et al.
2009
) which rendered engineering high producing cell lines more difficult. With
the latest advances in sequencing and the publication of the CHO-K1 genome (see
www.chogenome.org) this limitation is now being removed. At the time of writing
this review a draft genome sequence and annotation of CHO-K1 sequence has been
released publicly by Xu et al. (
2011
) and other sequencing efforts are also being
undertaken internationally. Although the genome of CHO cell lines in other labora-
tories may diverge due to chromosomal rearrangements this resource remains a very
important source of information and essential to allow the full utilisation and under-
standing of microRNA regulated gene expression in CHO cells. RNA sequencing in
the future will further facilitate this field of research.
The production of complex biotherapeutics in a specific host also demands a good
understanding of the dynamics that control the biology of the cell. As subtle environ-
mental fluctuations are known to impact on the cell networks, it is crucial to develop
methods to monitor the molecular changes that occur under specific conditions. The
era of “omics” has therefore shed light on regulation at the gene, protein or metabolite
levels (Clarke et al.
2011
;Lietal.
2010
, Melville et al.
2011
; Nissom et al.
2006
;Yee
et al.
2008
) but these studies only covered 10-15 % of the transcriptome and therefore
a large amount of information remains missing or not investigated. In parallel to the
various transcriptomic and proteomic studies that have been on-going in the field of
CHO cell recombinant protein expression, the widespread impact of microRNAs has
now been recognised and highlights a need for accurate, high-throughput techniques
to quantify the abundance of each type of microRNA. As suggested previously in this
chapter, deep sequencing refers to a ground-breaking method that generates millions
of short RNA reads and allows the identification of microRNAs. However, it can
be difficult to discriminate true microRNAs from fragments of other transcripts and
short RNAs, especially when they are under-represented in the cell. The validation
of a novel microRNA follows a set of rigorous rules such as the threshold for reads
(
>
10), sequences that flank the mature microRNA and lack of overlap with other
annotated transcripts (Kozomara and Griffiths-Jones
2010
).
As yet, only a handful of research groups have concentrated their efforts on
identifying the miRNA population in CHO sub-types and across different culture
conditions. For example, a study by Johnson et al (
2010
) isolated 350 miRs and
classified them by running a BLAST alignment with known RNAs from the mIR-
Base database. Their work at the time emphasised two important points: first, most
microRNAs found in hamster are highly homologous to their human or mouse coun-
terpart and secondly that a large number of miRNAs are more prone to fluctuations in
their levels as a consequence of small environmental changes. Cgr-let-7f appears to
be the most abundant microRNA in CHO cells and its levels were comparable across
the conditions investigated in the reported study. This is not altogether surprising as
the let-7f family is largely conserved. A similar approach was adopted by Hackl et al
(
2011
) who mapped 387 mature microRNAs to an artificial sequence consisting of
all microRNAs hairpins. Finally, Lin et al. (
2011
) compared the levels of miRNA
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