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\begin{document}

\title{Identification of novel miRNA targets in CHO cell lines and
characterization of their impact on protein N-glycosylation}



\author[1]{Ankita Singh}%
\author[1]{Yuzhou Fan}%
\author[1]{Selgin Cakal}%
\author[1]{Thomas Amann}%
\author[1]{Anders Hansen}%
\author[2]{Nicole Borth}%
\author[3]{Gyun Min Lee}%
\author[4]{Helene Faustrup Kildegaard}%
\author[1]{Mikael Andersen}%
\affil[1]{Technical University of Denmark}%
\affil[2]{BOKU}%
\affil[3]{KAIST}%
\affil[4]{Novo Nordisk AS Gobal Research}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\selectlanguage{english}
\begin{abstract}
CHO cell lines are a workhorse for the production of pharmaceutical
proteins, but show some limitations in the variability and stability of
N-glycosylation profiles. One promising approach to addressing this at
the required systems-level is miRNA, which can regulate a large number
of genes and have predictable targets. Herein, we first identified de
novo 656 potential miRNAs in the CHO genome based on a combination of
literature, database searching, and miRNA sequencing. We further
sequenced mRNA from the same cultures, and used a combination of
mRNA-miRNA correlation analysis, target prediction and literature
searches to find miRNAs potentially targeting N-glycosylation. Our ten
best miRNA candidates were subjected to miRNA overexpression, knockdown,
or knock-out in CHO cell lines. Out of the ten candidates, four
(miR-128, miR-34c, miR-30b, and miR-449a) showed positive effects on
N-glycosylation and could be applied directly for CHO cell engineering.
The fact that 40\% of the screened targets had a desired effect, and the
prediction of 656 miRNAs illustrates the massive potential of miRNA
engineering in CHO.%
\end{abstract}%



\sloppy


Yuzhou Fan\textsuperscript{1,2,3}*, Ankita
Singh\textsuperscript{1,2,4}*\selectlanguage{ngerman}\textsuperscript{§}, Martin
Seifert\textsuperscript{1}, Junrui Li\textsuperscript{2,10}, Louise
Kjeldgaard Lyhne\textsuperscript{2,5}, Selgin Deniz
Cakal\textsuperscript{2}, Gerald Klanert\textsuperscript{2,6-7}, Thomas
Amann\textsuperscript{1,2,9}, Anders Holmgaard
Hansen\textsuperscript{1}, Nicole Borth\textsuperscript{6-7}, Gyun Min
Lee\textsuperscript{8}, Helene Faustrup Kildegaard\textsuperscript{1,5},
Mikael Rørdam Andersen\textsuperscript{2,9§}

\emph{\textsuperscript{1}Novo Nordisk Foundation Center for
Biosustainability, Technical University of Denmark, 2800, Kgs. Lyngby,
Denmark}

\emph{\textsuperscript{2}Department of Biotechnology and Biomedicine,
Technical University of Denmark, 2800, Kgs. Lyngby, Denmark}

\emph{\textsuperscript{3}Current affiliation: CMC Biologics,
Vandtårnsvej 83, 2860 Søborg, Denmark}

\emph{\textsuperscript{4}Current affiliation: Department of Biomedicine,
Aarhus University 8000 Aarhus Denmark}

\emph{\textsuperscript{5}Current affiliation: Novo Nordisk, Novo Nordisk
Park, 2880 Måløv, Denmark}

\emph{\textsuperscript{6}ACIB GmbH, Austrian Center of Industrial
Biotechnology , Graz, Austria,}

\emph{\textsuperscript{7}Department of Biotechnology, BOKU ---
University of Natural Resources and Life Sciences Vienna, Muthgasse 18,
1190 Vienna, Austria.}

\emph{\textsuperscript{8}Department of Biological Sciences, KAIST, 291
Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea.}

\emph{\textsuperscript{9}Current affiliation: Novozymes A/S, Bagsværd,
Denmark}

\emph{\textsuperscript{10}Sahlgrenska Cancer Center, Zhuhai, Guangdong,
China}

*Shared first authors. \textsuperscript{§}Corresponding author.

\subsection*{Abstract}

{\label{abstract}}

CHO cell lines are a workhorse for the production of pharmaceutical
proteins, but show some limitations in the variability and stability of
N-glycosylation profiles. One promising approach to addressing this at
the required systems-level is miRNA, which can regulate a large number
of genes and have predictable targets. Herein, we first
identified\emph{de novo} 656 potential miRNAs in the CHO genome based on
a combination of literature, database searching, and miRNA sequencing.
We further sequenced mRNA from the same cultures, and used a combination
of mRNA-miRNA correlation analysis, target prediction and literature
searches to find miRNAs potentially targeting N-glycosylation. Our ten
best miRNA candidates were subjected to miRNA overexpression, knockdown,
or knock-out in CHO cell lines. Out of the ten candidates, four
(miR-128, miR-34c, miR-30b, and miR-449a) showed positive effects on
N-glycosylation and could be applied directly for CHO cell engineering.
The fact that 40\% of the screened targets had a desired effect, and the
prediction of 656 miRNAs illustrates the massive potential of miRNA
engineering in CHO.

\subsection*{}

{\label{section}}

\subsection*{Key words:}

{\label{key-words}}

CHO cell, Stable microRNA engineering, Crispr/Cas9, N-glycosylation,
Differential equation, Correlation analysis, miRNA.

\subsection*{Introduction}

{\label{introduction}}

Recombinant therapeutic proteins provide innovative and novel medical
treatment possibilities for many difficult-to-treat diseases. Sales have
been estimated to be 140 billion USD in 2013 (Walsh 2014) and are
continually growing. Chinese hamster ovary (CHO) cells have, as the most
predominant expression platforms for recombinant therapeutic proteins,
for several decades shown high adaptability and robustness in industrial
scale applications and great capability of producing proteins with
complex folding and human-like N-glycosylation(Wong et al. 2010; Werner,
Kopp, and Schlueter 2007). In particular, CHO cell factories with better
and more controlled production of desired N-glycosylation structures are
of interest as the glycosylation pattern has a major role in defining
the efficacy of the produced protein. This drives a need to study,
understand and engineer the glycosylation pattern produced by a given
CHO cell line.

Recently developed CRISPR/Cas9 technologies facilitate precise genome
editing in CHO cells with significantly reduced cost and delivery time
(Ronda et al. 2014; Cong et al. 2013). Thus, better guidance and tools
are now available for selecting and engineering different targets in CHO
cells and enables rational design and development of CHO cell lines with
much faster pace and higher throughput (J. Y. Kim, Kim, and Lee 2012;
Kildegaard et al. 2013; Hackl et al. 2012; Sanchez et al. 2014; North et
al. 2010; Sealover et al. 2013; Maszczak-Seneczko et al. 2011; Z. Yang
et al. 2015). In tandem, glycoengineering studies have shown promising
results in improving and controlling the quality of therapeutic
proteins(Amann et al. 2019) . Even though CHO is considered a superior
host for the production of therapeutic proteins, it does not typically
have all the reactions of the human N-glycan pathway (Zhang et al.
2016).

Among the targets for CHO cell engineering, microRNAs (miRNAs) have
emerged as a potent and promising target in the context of improving
cells for production of recombinant therapeutic proteins at the
phenotype level (Jadhav et al. 2013). This is because miRNA is able to
regulate complex gene networks that control various cellular processes
(Lam et al. 2015), have predictable targets in the genome, and introduce
no translational burden to the host cells. miRNAs are single-stranded
small non-coding RNAs (19 to 25 nucleotides) that are found in a wide
range of higher eukaryotes (Mack 2007). They are able to regulate the
expressions of multiple genes at the post-transcriptional level by
completely or partially complementary targeting 3'-UTR of the target
mRNAs (Hackl et al. 2012; Jadhav et al. 2013). So far, miRNA's have been
identified to impact cell cycle progression (Jadhav et al. 2013),
apoptosis (Cimmino et al. 2005), metabolism (Gao et al. 2009; Csibi et
al. 2013), cell proliferation (He et al. 2007), various cancer related
pathways (such as the MAPK pathway (Masliah-Planchon, Garinet, and
Pasmant 2016)) in mouse and humans, while miRNA studies in CHO cells are
still in an early stage (Hackl et al. 2011, 2012; Jadhav et al. 2014;
Gammell et al. 2007; Clarke et al. 2012; Strotbek et al. 2013; Hammond
et al. 2012). However, as miRNAs are highly conserved across species
(Pasquinelli et al. 2000), knowledge of miRNAs from other mammals in
association with various phenotypic effects -- especially findings from
the highly prolific field of human cancer biology -- may be translated
into CHO cells. Furthermore, the role of miRNA in regards to N-glycan
metabolism and protein quality needs to be explored more. Additionally,
the field of N-glycosylation engineering in CHO cells has been
extensively studied and heavily patented (Z. Yang et al. 2015), but
almost no studies have investigated into miRNAs that modulate
N-glycosylation in CHO cells so far. Therefore, to fill the gaps in
miRNA engineering in CHO cells for industrial applications, more efforts
on stable microRNA engineering for improving CHO cell phenotypes are
needed.

In this study, we search for and discover miRNA candidates for stable
CHO cell engineering to achieve desired N-glycosylation structures. We
do this by identifying targets through a hybrid strategy of targeted
literature and database searching, couple with mRNA and miRNA
sequencing, target prediction and differential gene and miRNA expression
analysis. Based on this, we overexpress (OE), knock-down (KD) and knock
out (K/O) miRNA targets using CRISPR-based technologies and a
CHO-adapted miRNA expression system, finding several miRNAs with effects
on N-glycosylation.

\subsection*{Materials and Methods}

{\label{materials-and-methods}}

\subsubsection*{CHO cell lines}

{\label{cho-cell-lines}}

Six CHO suspension~cell lines were used: Three producer cell lines,
namely CHO-EPO (erythropoietin producer), CHO-CS13-1.00 (rituximab high
producer), and CHO-CS13-0.02 (rituximab low producer), and three
non-producer cell lines (CHO-K1, CHO-DG44, and CHO-S) were used. The
CS13-0.02 and CS13-1.00 cell lines were generated in a previous study by
insertion of chimeric antibody genes into a CHO DG44 parental cell line,
and then selected under different MTX concentrations (0.02 \selectlanguage{greek}μ\selectlanguage{english}M and 1.00
\selectlanguage{greek}μ\selectlanguage{english}M, respectively) (S. J. Kim et al. 1998). DG44-EPO is a MTX amplified
stable EPO producer which was provided as a kind gift from Prof. Gyun
Min Lee. CHO-K1 and CHO-DG44 are serum-free suspension cell lines, which
have been adapted in-house from ATCC adherent CHO-K1 and CHO-DG44 cell
lines. CHO-S was acquired from Thermo Fisher Scientific, Waltham, MA,
USA.

The growth medium for CS13-0.02, CS13-1.00 and DG44-EPO was PowerCHO-2
chemically defined, serum-free medium (Lonza, Switzerland), with 4 mM of
glutamine and 0.2\% of anti-clumping agent (ThermoFisher Scientific)
added. CHO-DG44 cell line was grown in the same growth medium with a
10\% HT supplement (10 mM of sodium hypoxanthine and 1.6 mM of
thymidine, ThermoFisher Scientific). The growth medium for CHO-K1 and
CHO-S was CD CHO-2 chemically defined, serum-free medium (Lonza, Visp,
Switzerland), with 8 mM of glutamine and 0.2\% of anti-clumping agent
(ThermoFisher Scientific) added.

\subsubsection*{Bioreactor cultivations}

{\label{bioreactor-cultivations}}

All cells were maintained in duplicates in their corresponding growth
media in shake flasks at 37@\selectlanguage{english}C, 5\% CO2, 150 rpm prior to the batch
process in bioreactor.

During batch process, cell lines were grown in their corresponding
growth medium in duplicates in 2x 1.0 L Eppendorf DASGIP bioreactor
systems at 37\selectlanguage{ngerman}°C and 150 rpm with an initial seeding density of
4×10\textsuperscript{5} viable cells/mL and a working volume of 410 mL.
The pH and dissolved oxygen (DO) was monitored and controlled online
during the cultivation process. The pH was set to 7.2 with a deviation
of 0.02, where 2M sodium carbonate and CO\textsubscript{2} gas flow was
used to adjust the pH. DO was kept at 50\% of air saturation mixed of
air, O\textsubscript{2} and CO\textsubscript{2} with a gas flow of 0.6
L/h. The bioreactor cultivations were last for 7 days. Daily sampling
was carried out for monitoring cell growth (Nucleocounter NC-200,
ChemoMetec A/S, Allerød, Denmark), and extracellular metabolites
(BioProfile 400 chemistry analyzer, Nova Biomedical,Waltham,USA).
Samplings of cells for RNA analysis (transcriptomics and miRNAs) was
taken on day 4 of the cultivation.

\subsubsection*{RNA purification and next-generation
sequencing}

{\label{rna-purification-and-next-generation-sequencing}}

RNA was extracted 5 × 10\textsuperscript{7} cells using miRNeasy Mini
Kit (Qiagen, Germany) for total RNA preparation following the
manufacturer's instructions. RNA integrity was evaluated and confirmed
using an Agilent 2100 Bioanalyzer with total RNA nano chips (Agilent
Technologies, Santa Clara, Ca, USA). RNA concentration was measured
using Qubit 2.0 (ThermoFisher Scientific). Multiplexed cDNA library and
miRNA library generation were carried out by BGI (China) using the same
total RNA samples by the TruSeq RNA Sample Preparation Kit v2 (Illumina,
Inc., San Diego, CA) and TruSeq Small RNA Sample Preparation Kit
(Illumina), respectively. Next-generation sequencing of both
transcriptome and miRNAs were performed by BGI using a Illumina Hiseq
4000 system for paired-end sequencing.

\subsubsection*{mRNA-seq quality control, alignment, and generation of
read
counts}

{\label{mrna-seq-quality-control-alignment-and-generation-of-read-counts}}

The Chinese hamster genome published previously (Lewis et al. 2013) was
downloaded from NCBI (accession number PRJNA239316). An initial quality
check was carried out using FastQC v0.11.5
(http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) in
non-interactive mode. Quality trimming of the poor Illumina reads was
performed based on the FAstQC evaluation using Trimmomatic v0.36
(Bolger, Lohse, and Usadel 2014), adopting a paired-end mode as a
sequencing mode. Alignment of all trimmed paired-end reads to the
Chinese hamster genome, was achieved using a STAR v2.5.2b aligner (Dobin
et al. 2013), choosing genomeGenerate mode as a runMode to generates
genome indices using a genome FASTA file and GTF files which are then
used to align the mRNA reads. Files obtained after alignment were
converted into a binary version using SAMtools (Li et al. 2009). To
calculate read counts in the form of logCPM, all of the aligned reads as
well as a GFF file of the Chinese hamster genome were fed into the HTSeq
v0.8.0 (Anders, Pyl, and Huber 2015) package under intersection strict
mode. Output from the HTSeq package were loaded into RStudio v0.99.1261
(https://www.rstudio.com/), with the integrated development environment
running on R v3.3.3 (R Development Core Team 2003) for performing
further downstream analysis.

\subsubsection*{Gene expression analysis (contrast matrix + DE
analysis)}

{\label{gene-expression-analysis-contrast-matrix-de-analysis}}

Through an examination of the logCPM values, we found that a large
proportion of the genes within individual samples were unexpressed.
Hence, we chose to enforce a cutoff of CPM of 1 or above in at least two
samples (Law et al. 2016) as it distinguished expressed genes from
unexpressed genes for most of the dataset (Supplementary Table 1). Next,
the genes were normalized by using TMM algorithms (Law et al. 2016;
Robinson and Oshlack 2010) and the \emph{calcNormFcator} function of
edgeR (McCarthy, Chen, and Smyth 2012). Subsequently, to establish the
mean variance relationship in the data to calculate weights based on the
calculated mean variance, we used the relationship function
\emph{voom}(Law et al. 2014) from the\emph{limma (Smyth, n.d.)}package.
Transformation of the RNA-Seq data for linear modeling was performed
using the \emph{lmFit} function of \emph{limma} . Consequently,
differentially expressed (DE) genes were determined with the\emph{limma}
\emph{lmFit} function. Finally, differential expression analysis was
performed using an empirical Bayes model (\emph{eBayes} ). Moderated
\emph{t} -statistics and the log-odds ratio was calculated using a
design matrix and a contrast matrix. Summaries of the gene sets were
laid out by various functions such as \emph{toptable}
and\emph{volcanoplot (Su et al. 2017)} to finally determine the highly
differentially expressed genes in each of the contrasts.

\subsubsection*{miRNA expression
analysis}

{\label{mirna-expression-analysis}}

The quality of fastq files was evaluated using FASTQC version 0.11.2.
Sequencing adapters were trimmed using cutadapt version 1.7.1. miRNAs
were search against miRBase (Release 21) for \emph{Cricetulus griseus}
,\emph{Rattus norvegicus} , \emph{Homo sapiens} and \emph{Mus
musculus}using CLC genomic workbench (version 7.5) with standard
settings regarding mature length variants in order to obtaining the read
counts of identified microRNAs. All of the gene-specific predicted miRNA
targets of human and mouse were fetched from the miRWalk 2.0 database
(Dweep, Gretz, and Sticht 2014). Gene and official gene symbols were
kept as an input identifier and 3' UTR as an input parameter.

\subsubsection*{}

{\label{section-1}}

\subsubsection*{Correlation analysis}

{\label{correlation-analysis}}

Unsupervised correlation analysis between expression levels of gene and
miRNA was performed by comparing the expression value of genes and
counts all the predicted miRNA. Pearson and Spearman correlation was
performed using the \emph{cor} function of R.

\subsubsection*{}

{\label{section-2}}

\subsubsection*{Generation of stable miRNA knock-out (KO) and knock down
(KD) cell
lines}

{\label{generation-of-stable-mirna-knock-out-ko-and-knock-down-kd-cell-lines}}

500 bp upstream and 500 bp downstream sequences of target miRNAs were
selected from the CHO genome sequences to identify sgRNA targeting sites
and design sgRNAs using the ZiFiT tool
(\emph{www.zifit.partners.org/ZiFiT/}). The top sgRNA hits against the
upstream and downstream sequences with the lowest off-target events were
selected (Supplementary Table 2). The selected sgRNA was cloned into the
px458 plasmid co-expressing eGFP (Addgene) by restriction with Bbs1 and
conventional ligation. For each miRNA target, one plasmid with the
upstream sgRNA (up-sgRNA) and one plasmid with the downstream sgRNA
(dw-sgRNA) were generated. All plasmids were verified by sequencing and
purified using NucleoBond Xtra Midi EF (Macherey-Nagel, Düren, Germany),
according to manufacturer's instructions.

miRNA KD/KO was performed in CHO-S using CRISPR/Cas9. CHO-S cells were
maintained as suspension culture in CD CHO2 medium (Lonza) with 8 mM of
glutamine (Thermo Fisher Scientific) in 250ml shake flask with 120 rpm
(Infors, USA) at 37 °C and 5\% CO2. Cells were seeded at 0.5 ×
10\textsuperscript{6} cells/ml in 6 well plate (NUNC, Denmark) 1 day
before transfection with 3 mL cell culture per well. Transfection of
CHO-S cells were carried out by using 3.8 \selectlanguage{greek}μ\selectlanguage{english}g of total plasmid DNA (50 \%
up-sgRNA + 50 \% dw-sgRNA) together with FreeStyleTM MAX reagent as
described previously (Grav et al. 2015). Transfection with 3.8 \selectlanguage{greek}µ\selectlanguage{english}g
pmaxGFP\selectlanguage{ngerman}® vector without sgRNAs (Lonza, Basel, Switzerland) was applied
as control for determining transfection efficiencies. Transfection with
3.8 \selectlanguage{greek}µ\selectlanguage{english}g px458 plasmid was carried out for generation of mock cell lines.
Two days after transfection, cells were subject to FACS in order to
perform single cell sorting of fluorescent-positive cells as reported
(Grav et al. 2015). 14 days after single cell sorting, the colonies were
expanded into 96-well flat-bottom plates (BD Biosciences). The wells
with confluent cells were split, and replicated plates were subject to
PCR analysis of genomic DNA when close to confluent. Genomic DNA was
extracted from the cell pellets using QuickExtract DNA extraction
solution (Epicentre Illumina, WI) (J. S. Lee et al. 2015). PCR was
performed as described in (J. S. Lee et al. 2015) in order to select
colonies that contains KO/KD of the targeted miRNA. PCR primers used for
verifying miRNA KO/KD were designed to amplify from about 150 bp
upstream of the up-sgRNA targeting sites and about 150 bp downstream of
the dw-sgRNA targeting sites (Supplementary Table 3). Selected KO/KD
cell lines were further expanded, tested, and found free of mycoplasma
prior to storing at -180\selectlanguage{ngerman}°C.

\subsubsection*{Generation stable miRNA overexpression cell
pools}

{\label{generation-stable-mirna-overexpression-cell-pools}}

Seven miRNA overexpression (OE) plasmids were designed as described
previously with minor modifications (Klanert et al. 2014). Each target
sequence contains eGFP sequences linked with the stem-loop sequence of a
miRNA with 50 bp upstream and 50 bp downstream flanking sequence from
the CHO genome. Each target sequence (Supplementary table 4) was
synthesized at GenScript\selectlanguage{english} (China), and cloned into HindIII and BamHI
restriction sites of pcDNA3.1/Hygro(+) plasmid (Addgene) to generate
seven miRNA OE plasmids. All plasmids were purified using NucleoBond
Xtra Midi EF (Macherey-Nagel), according to manufacturer's instructions.

CHO-S cells were maintained in 6 well plates (as described above for the
miRNA KD/KO procedure) prior to transfection. Transfections with each OE
plasmid DNA was done in the 6 well plates using
FreeStyle\textsuperscript{TM} MAX reagent. Negative controls and mock
cell pools were generated by transfecting cells with nucleotide-free
water and pcDNA3.1/Hygro(+) based plasmid with a target sequence
containing the eGFP sequence but no regulatory miRNA sequence,
respectively. Transfections were carried out in duplicates with each
plasmid. All cell cultures were kept in 6 well plates at 37degC, 5\% CO2
after transfection. Transfection efficiency is determined by relative
percentage of GFP cells in each culture comparing to the negative
control 24 hours post transfection using Celigo Imaging Cell Cytometer
(Nexcelom Bioscience, Lawrence, MA).

Two days after transfection, the transfected cells were transferred to a
125mL shake flask and were selected in 10mL of selection medium (CD CHO
medium with 8 mM Gln, 0.2\% AcA and 400\selectlanguage{greek}µ\selectlanguage{english}g/mL hygromycin B). During
selection, cell culture was maintained at 37\selectlanguage{ngerman}°C with 5\% CO2 at 120 rpm,
and medium was changed every 3--4 days. Cell growth was monitored using
the NucleoCounter NC-200 every 2-3 days. After 14 days of selection,
stable OE cell pools were generated when viability of each miRNA OE cell
culture and mock cell culture are all above 94\%, while viability of
negative control cell culture is 0\%. All stable cell pools were further
expanded, tested, and found free of mycoplasma prior storing in the cell
bank at -180°C.

\subsubsection*{Characterization of miRNA KO/KD cell lines or OE cell
pools in batch
culture}

{\label{characterization-of-mirna-kokd-cell-lines-or-oe-cell-pools-in-batch-culture}}

For each KO/KD or OE, two representative engineering cell lines/pools
were selected and further evaluated with a 6-day 50mL batch culture in
CD CHO-2 medium with 8 mM of glutamine and 0.2\% of anti clumping agent,
at 37\selectlanguage{english}@C, 150 rpm, 5\% CO2 in duplicated 250 mL shake flasks. Daily
sampling was carried out for monitoring cell growth (Nucleocounter
NC-200), and extracellular metabolites (BioProfile 400 chemistry
analyzer). 15 mL supernatant were harvested on day 4, and 10x
concentrated using Amicon Ultra-15 (Merck, Darmstadt, Germany) for
N-Glycoprofiling of secretome.

\subsubsection*{Quantitation of mature
miRNA}

{\label{quantitation-of-mature-mirna}}

5x10\textsuperscript{6} cells were sampled for quantitation of mature
miRNA on day 2 of the cultivation. RNA extraction was performed using a
Trizol-based method as reported by (Klanert et al. 2014), and
quantitation of mature miRNA were performed by quantitative real-time
PCR (RT-qPCR) using miScript\selectlanguage{ngerman}® SYBR® Green PCR Kit together with miScript
Primer Assays (Supplementary Table 5). Synthesis was carried out using
5x miScript HiFlex Buffer and miScript Reverse Transcriptase Mix
according to manufacturer's instructions. Reaction mixtures contained
12.5 \selectlanguage{greek}µ\selectlanguage{english}L 2x QuantiTect SYBR Green PCR Master Mix, 2.5 \selectlanguage{greek}µ\selectlanguage{english}L 10x miScript
Universal Primers, 2.5 \selectlanguage{greek}µ\selectlanguage{english}L 10x miScript Primer Assay, 5 \selectlanguage{greek}µ\selectlanguage{english}L RNase-free
water and 2.5 \selectlanguage{greek}µ\selectlanguage{english}L cDNA template. Amplification was executed with the
following conditions: 95@\selectlanguage{english}C for 15 min; 40\selectlanguage{ngerman}×: 94\selectlanguage{english}@C for 15 s, 55@\selectlanguage{english}C for 30 s
and 70@\selectlanguage{english}C for 30s. Each PCR reaction had 4 replicates. Primer specificity
was verified by a melting curve analysis of the PCR products with a
temperature gradient of 0.2@\selectlanguage{english}C/s from 65@\selectlanguage{english}C to 95@\selectlanguage{english}C. The expression levels
of each mature miRNA relative to a house-keeping miRNA (cgr-miR-185-5p)
were determined using the 2--\selectlanguage{greek}\textsuperscript{ΔΔCT}\selectlanguage{english}method(Klanert et al.
2014).

\subsubsection*{Quantitation of target gene
expression}

{\label{quantitation-of-target-gene-expression}}

Quantitative real-time PCR (RT-qPCR) was performed on these genes using
miScript\selectlanguage{ngerman}® SYBR® Green PCR Kit (Integrated DNA Technologies, Coralville,
IA) together with corresponding primers (Supplementary Table 6),
according to manufacturer's instructions. Each primer pairs were passed
linearity check (\emph{r\textsuperscript{2}} \textgreater{}0.98) using
4-fold serial dilutions of cDNA samples over 5 grades as well as
amplification efficiency check (90\% and 110\%). Amplification was
executed with the following conditions: 95\selectlanguage{english}@C for 15 min; 40\selectlanguage{ngerman}×: 94\selectlanguage{english}@C for
15 s, 52@\selectlanguage{english}C for 30 s and 70@\selectlanguage{english}C for 30s. Each PCR reaction had 4
replicates, and every PCR plate included template controls. Primer
specificity was verified by a melting curve analysis. GAPDH was used as
a housekeeping gene control and the relative fold change of the gene
expression was calculated by the 2\selectlanguage{greek}\textsuperscript{--ΔΔCT}\selectlanguage{english} method
(Klanert et al. 2014).

\subsubsection*{}

{\label{section-3}}

\subsubsection*{N-Glycoprofiling of secreted
proteins}

{\label{n-glycoprofiling-of-secreted-proteins}}

Sample preparation for N-glycoprofiling was carried out using GlycoWorks
RapiFluor-MS N-Glycan Kit (Waters, Milford, MA) according to the
manufacturer's instructions. Labeled N-Glycans were analyzed by a LC-MS
system using a Thermo Ultimate 3000 HPLC with fluorescence detector
coupled to a Thermo Velos Pro Iontrap MS, as described previously (Grav
et al. 2015). Relative amounts of the glycans was measured by
integrating the areas under the normalized fluorescence spectrum peaks
with Thermo Xcalibur software (Thermo Fisher Scientific, Waltham, MA).

\subsection*{Results}

{\label{results}}

\subsubsection*{Identification of miRNAs in the Chinese hamster
genome}

{\label{identification-of-mirnas-in-the-chinese-hamster-genome}}

Our first step was to identify as many miRNAs in the CHO genome as
possible. However, current annotation of miRNAs in CHO genome is very
much incomplete, and so, we decided to do \emph{de novo} prediction,
partially based on sequence analysis (described here) and partially on
miRNA sequencing (See below). First we examined the Chinese hamster,
human, and mouse genomes for predicted and identified miRNAs. In total,
656 miRNAs were found to align to the Chinese hamster, human and/or
mouse genome. Of these, 277 were uniquely (sequence unique) mapping to
the hamster genome, 353 to human. Further, 4 miRNAs were identical in
human, mouse and hamster, 4 in human and hamster, 6 miRNA in human and
mouse, and 12 in mouse and hamster. Out of the 656 miRNAs, 489 were
predicted with the help of miRWalk to regulate one or more reactions of
the N-glycan pathway (Supplementary Table 1).

\subsubsection*{Identification of genes in CHO protein
N-glycosylation}

{\label{identification-of-genes-in-cho-protein-n-glycosylation}}

First, we wanted to identify CHO genes in the N-glycosylation pathway as
well as miRNAs putatively targeting these genes. To this end, a list of
75 genes assigned to the N-glycosylation pathway was collected from the
KEGG pathway database (Kanehisa et al. 2019)). To further sophisticate
this list, genes were grouped in categories such as mannosylation,
galactosylation, fucosylation etc. based on literature mining.
Furthermore, genes were classified on the basis of whether up- or
down-regulation would be desirable in order to have mature
N-glycosylation (Supplementary Table 7).

\subsubsection*{}

{\label{section-4}}

\subsubsection*{Generation of mRNA and miRNA expression
profiles}

{\label{generation-of-mrna-and-mirna-expression-profiles}}

In order to experimentally determine the expression patterns of CHO
miRNAs and to be able to correlate these to gene expression patterns for
genes of interest, we set up a mRNA/miRNA profiling experiment. We
selected a panel of three non-producing cell lines and three
protein-producing cell lines, and cultivated them in biological
duplicates in batch bioreactors. CHO-S, CHO DG44 and CHO-K1 are all
non-expressing cell lines; CS13-0.03 is an IgG low-producer, CS13-1.00
is an IgG high-producer, and DG44-EPO produces Erythropoietin (EPO).
Culture performance of the cell lines that are used for transcriptomics
and miRNAseq analysis are shown in Figure 1, all details are available
in Supplementary Data File 1. As can be seen, replicates are consistent,
but there are differences between the lines. CHO-S and DG44-EPO grow
faster and grow to higher VCD than other cell lines. Glucose was
depleted around day 5 for all cell lines except the DG44 cell line.
CHO-K1 produce the most lactate and consume the most glucose during the
growth phase (day 0-4) compared to other cell lines, indicating
inefficient carbon flow from glycolysis to support cell growth. CHO-K1
and CHO-S also show relatively high glutamine consumption levels and
high NH4\textsuperscript{+} production levels along the cell culture
comparing to other cell lines. This may be due to their growth medium
used is different from other the cell lines.

RNA samples were taken on day 4, purified, and sent for mRNA and miRNA
sequencing.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1} - Comparison of 6 cell lines used for RNAseq in
bioreactor batch culture in duplicates. All RNA samples were taken on
day 4 of cultivation. Error bars are showing standard deviation of
duplicate culture.

\subsubsection*{RNA-seq data quality control, alignment, and
normalization}

{\label{rna-seq-data-quality-control-alignment-and-normalization}}

All reads were subjected to quality control and trimming. Trimmed mRNA
and miRNA reads were aligned to the Chinese hamster genome and read
counts (logCPM) were calculated. Post-alignment, data was normalized for
comparability between samples. To evaluate the normalization, library
sizes of the sample sets were compared before and after normalization,
providing adjusted library size pre- and post-normalization. As no
problems with the aligned and normalized data were apparent, this was
used for further analysis. Data on the alignment counts are available
for the glycosylation genes in Supplementary Table 8, and for the mRNA
in Supplementary Table 9. As a further aid, the miRNA has been sorted
according to predicted gene targets.

\subsubsection*{Differential expression (DE) analysis of mRNA and
miRNAs}

{\label{differential-expression-de-analysis-of-mrna-and-mirnas}}

In order to understand the general differences in the transcriptomic
landscape and the miRNAs, we performed DE analysis across various
combinations of producer and parental cell lines using a standard
pipeline.

Since the expression profiles and N-glycan profile of the cell lines
varies with the protein of interest (e.g. erythropoietin, rituximab) and
the cell line, we defined four different contrasts for comparison and
analyzed up- and downregulated genes involved in glycosylation as well
as the set of 806 miRNAs potentially targeting glycosylation genes.
Significantly differentially expressed gene sets (adj.p-value
\textless{}= 0.05) were counted across various contrasts (Supplementary
Table 10).

In a first comparison, we examined the effect of EPO-production by
comparing the DG44 parental cell line with the DG44 EPO producer. Here,
24 glycosylation genes, and 258 glycosylation-targeting miRNA's (in the
following just denoted glyco-genes and glyco-miRNAs) were found to be
up-regulated, 36 glyco-genes, 106 glyco-miRNA's to be down-regulated
leaving 15 glyco-genes, 442 glyco-miRNA's not significantly
differentially expressed (See Supplementary Table 11 for details on
genes and Supplementary Table 12 for details on miRNA).

In a similar comparison for IgG-production between the CS13-1.00 high
producer and the DG44 parental cell line, 18 glyco-genes, 71
glyco-miRNA's and 28 glyco-genes, 350 glyco-miRNA's were found to be up
and down-regulated respectively (genes in Supplementary Table 13, miRNA
in Supplementary Table 14).

As a third comparison, we examined the difference between the CS13-1.00
high producer and the CS13-0.02 low producer cell lines were performed
separately to identify traits specific to high production versus low. As
expected, the significantly changed genes and miRNAs were much lower in
this comparison, than the producer/non-producer comparisons above. Here,
we saw 11 glyco-genes, 40 glyco-miRNA's and 11 glyco-genes, 29
glyco-miRNA's to be up- and down-regulated respectively (genes in
Supplementary Table 15, miRNA in Supplementary Table 16).

As a fourth comparison aimed at identifying common traits for producer
cells (irrespective of their high or low producer status), we compared
all producer cell lines (i.e. DG44-EPO producer along with CS13 high and
low producer) with all parental cell lines (i.e. DD44-WT, CHOS-P and
CHO-K1). This comparison yielded an interesting set of 19 glyco-genes,
104 glyco-miRNA's and 37 glyco-genes, 324 glyco-miRNA's to be up- and
down-regulated respectively (genes in Supplementary Table 17, miRNA in
Supplementary Table 18). This is a surprisingly high number of miRNA's,
more than half of the miRNAs in the cell are differentially expressed
comparing producers and non-producers.

\subsubsection*{Unsupervised linking of miRNA to mRNA: Correlation
analysis}

{\label{unsupervised-linking-of-mirna-to-mrna-correlation-analysis}}

While the literature study gave links to which miRNAs and genes may be
linked, and the miRwalk made a sequence-based analysis, we wanted to
further the analysis by incorporating an unsupervised approach to the
linking of miRNA to mRNA. In general, an unsupervised statistical
correlation analysis of two related datasets allows linking across
datasets in an un-biased way. Hence, we employed a correlation analysis
of all miRNAs in the set to miRNA's belonging to the N-glycan pathway.
We chose to use two well-established correlation co-efficients: Pearson
and Spearman. Pearson correlation allowed us to identify linearized
relationships and Spearman correlation measures the ranked strength and
direction of association between the expression variables of gene and
miRNA's. Here, cell line-specific bias was minimized by averaging the
expression values of each mRNA across 6 cell lines and correlating it
with the expression value of its corresponding miRNA. We thus identified
a list of miRNAs with a negative expression correlation by selecting
only those gene-miRNA combinations that were negatively correlated
(Supplementary Table 19). Similarly, positive correlations were
identified. As an example, miR-6715b-5p, miR-30b-3p and miR-335-3p was
found to repress eight and seven gene targets out of eight, seven and
fifteen in total.

\subsubsection*{}

{\label{section-5}}

\subsubsection*{Supervised linking of miRNA to
mRNA}

{\label{supervised-linking-of-mirna-to-mrna}}

As an addition to the unsupervised approach above, we wanted to include
a supervised approach as well for linking genes to miRNAs. In order to
do this, we made a correlation analysis based on the direction of
statistically significant changes in the comparison of the EPO producers
to the parental cells, rather than the absolute RNA-seq counts (as used
in the un-supervised approached). As negative correlations, we
identified miRNA targets that were either up-regulated in the DE of gene
and down regulated in the DE of miRNA or vice versa. This gave us one
list of miRNAs (Supplementary Table 19). The final table (Supplementary
Table 19) containing all of the positive and negative correlations from
both the supervised and un-supervised analysis.

\subsubsection*{}

{\label{section-6}}

\subsubsection*{Target selection}

{\label{target-selection}}

Taking the perspective that miRNA negatively inhibit and transnationally
repress mRNA, we only wanted to take those targets that were negatively
regulated in the supervised and/or unsupervised analysis. This gives us
a filter for miRNA which supposedly works more directly on the
transcript, rather than identify miRNAs targeting transcriptional
repressors/activators. The list of miRNAs with negative correlations in
both the supervised and unsupervised approaches can be seen in
Supplementary Table 19. Combining these two types of correlation with
the miRwalk analysis, the miRNA expression analysis, the miRNA DE
analysis, and literature analysis, we selected a set of 10 miRNAs for
modification; three for KO/KD, and seven for OE studies. The overview of
the analysis is found in Table 1.

\textbf{Table 1 -} miRNAs engineered in this study and their
hypothetical effect on CHO cell phenotypes\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{miRNA}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{Known effect of miRNAs on various mammalian cells}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{Predicted/validated glycosylation gene targets in miRWalk2.0
database and their possible impact on N-glycosylation in human
cells}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{miRNA engineering in this study}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{Reference}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
miR-26b\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Regulate cell proliferation and apoptosis\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Gmds (8/12), Fpgt (6/12): regulate biosynthesis of GDP-fucose, and thus
fucosylation\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
cgr-miR-26b OV\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
(43-46) \& miRwalk 2.0\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
miR-30a\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Regulate cell growth and productivity in CHO cells\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
B4galt1(8/12), SLC35A5(6/12), SLC35A3(6/12), SLC35C1(8/12).\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
cgr-miR-30a OV\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
(47, 48) \& miRwalk 2.0\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
miR-30b\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Regulate cell growth and productivity in CHO cells and changing
apoptosis, O-glycosylation in human cancer cells\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Fut8 (9/12): regulate fucosylation; Gale(7/12), B4galt1(8/12): regulate
galactosylation; SLC35A2(7/12): regulate transport of UDP-galactose and
thus galactosylation; SLC35A3(7/12): regulate transport of UDP-GlcNAc,
and thus antennarity; SLC35A4(8/12), SLC35A5(6/12): ): regulate
transport of both UDP-galactose and UDP-GlcNAc, and thus galactosylation
and antennarity; SLC35C1(8/12): regulate transport of GDP-fucose, and
thus fucosylation\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
cgr-miR-30b OV\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
(47-50) \& miRwalk 2.0\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
miR-34a\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Regulate cell proliferation and apoptosis\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Mgat1 (7/12), Mgat4a (10/12), Mgat4b (6/12) , Mgat5b (8/12): regulate
antennarity; Fut8 (10/12): regulate fucosylation; B4galt1(8/12):
regulate galactosylation\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
cgr-miR-34a OV\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
(51-54) \& miRwalk 2.0\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
miR-34c\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Regulate cell proliferation apoptosis, and DNA replication\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Mgat1 (8/12), Mgat4a (10/12): regulate antennarity; Fut8 (9/12):
regulate fucosylation St3gal1(8/12): regulate sialylation\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
cgr-miR-34c KO\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
(55-60) \& miRwalk 2.0\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
miR-122\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Mgat4a (9/12): regulate antennarity Fut8 (8/12): regulate
fucosylation\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
cgr-miR-122 OV\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
miRwalk 2.0\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
miR-128\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Regulate cell proliferation, apoptosis, self-renewal, and
differentiation\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Mgat1 (7/12), Mgat4b (8/12): regulate antennarity\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
cgr-miR-128 KO\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
(61-65) \& miRwalk 2.0\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
miR-181a\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Regulate cell proliferation, apoptosis and differentiation\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
B4galt1(9/12): regulate galactosylation\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
cgr-miR-181a and b KO in combination\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
(66-77) \& miRwalk 2.0\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
miR-181b\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
St3gal1(8/12) : regulate sialylation B4galt1(9/12): regulate
galactosylation\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
miR449a\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Regulate cell proliferation and apoptosis\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Mgat4a (10/12): regulate antennarity Fut8 (10/12): regulate
fucosylation\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
putative-cgr-miR449a OV\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
(78-81) \& miRwalk 2.0\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\# The score describes amounts of databases that predict the
corresponding gene target of a miRNA out of 12 selected database in
miRWalk.

These targets were examined further below.

\subsubsection*{KO/KD cell lines}

{\label{kokd-cell-lines}}

In order to test the effect of reduced/loss-of-function miRNAs for three
specific miRNAs -- miR-128, miR-34C, and miR-181, which all have
potential targets in glycosylation -- and evaluate possible correlation
between selected miRNAs and N-glycosylation, we set up a CRISPR-based
engineering strategy.

For each target, two plasmids were generated, each expressing one
specific guide RNA (gRNA) targeting the upstream and downstream region
of the miRNA expression site in the CHO genome (up-sgRNA and dw-sgRNA
respectively).

For each target, up-sgRNA and dw-sgRNA plasmids were co-transfected into
CHO-S cells, and subsequently single cell sorting and screened. As shown
in Figure 2A , KO/KD of miRNA was first verified at the molecular level
by genomic DNA PCR. It was possible to isolate both miR-128, miR-34C,
and miR-181 KO/KD clones which showed a clean deletion (KO). For some
miR-128 and miR-34c clonal cell lines, both wt bands and KO bands were
found in the gel. This indicates that the deletion may only have
happened in one allele in these cell lines, thus making them KD rather
than KO, and suggesting that these exist in more than one allele.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2 - Characterization of KO/KD cell lines in CHO-S.} A:
Out-out PCR of miRNA KO/KD in CHO-S. B: Quantification of mature miRNA
by qPCR.

To further validate the miRNA KO/KD cell lines we conducted quantitative
real-time PCR of corresponding miRNAs (Figure 2B). The miRNA qPCR
results correlate well with the results from out-out PCR, which
indicates that miR-128 E7, and miR-181ab E9 are to be considered full
miR KO cell lines, but the miR-34c D12 seems to have some miR-34
expression active, despite the negative gDNA PCR result, possibly due to
a copy somewhere else in the genome. It should thus only be considered a
KD.

\subsubsection*{Glycoprofiling of miRNA KO/KD cell
lines}

{\label{glycoprofiling-of-mirna-kokd-cell-lines}}

To functionally characterize the miRNA KO/KD effects, we examined
N-glycosylation in the clones. Batch cultures were run in shake flasks,
and N-glycosylation profile of secretomes was made from sampling day 4
of the batch culture. Figure 3 shows that miR-128 and miR-34c KO/KD
increases the levels of tri- and tetra- antennary structure of N-glycans
of the secreted proteins in CHO cells in comparison to the mock cell
lines (px458 F6 and px458 H12). On the other hand, miRNA-181 KO cell
lines demonstrate no change comparing to mock cell lines. This result
indicates that miR-128 and miR-34c may regulate tri- and tetra-
antennary levels in N-glycans by either regulating N-acetylglucosamine
(GlcNAc) transferases that is responsible for addition of the GlcNAc
unit onto \selectlanguage{greek}β \selectlanguage{english}1,4 (Mgat4a, Mgat4b), and \selectlanguage{greek}β \selectlanguage{english}1,6 branches (Mgat5), or by
regulating UPD-GlcNAc transporters (SLC35A3, SLC35A4, SLC35A5), which is
responsible of transporting UPD-GlcNAc from cytosol into Golgi
apparatus, as a building block for further GlcNAc transferase reactions.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3} - N-glycan characterization of miRNA KO/KD cell lines.
Statistically significant difference (*, p\textless{}0.05) antennary
structures among secreted proteins from the mock (px458 F6 and px458
H12) and KD/KO (miR-128 C3, miR-128 E7, miR-34c D12, and miR-34c F3)
cell lines based on N-glycosylation profiling. Details are found in
Supplementary Figure 1.

In a qPCR analysis of glycosylation gene expression in the culture
(Figure 5), it seems that miR-128 KD may regulate Mgat4b and Mgat5 by
increasing their expression levels. In addition, miR-34c KD may regulate
the expression levels of Mgat4a and Mgat4b, and possible also Mgat5. On
the other hand, SLC35A3 and SLC35A5 seems not to be directly targeted by
mir-128 and miR-34c, as expression levels of SLC35A3 and SLC35A5 in the
corresponding miR KD cell lines are similar to the mock cell line.

\subsubsection*{Construction and validation of miRNA over-expression (OE)
cell
pools}

{\label{construction-and-validation-of-mirna-over-expression-oe-cell-pools}}

In order to test more miRNA targets, we constructed seven cell pools
(Table 1), each overexpressing one miRNA. We tested the expression with
qPCR, and all miRNA OE cell pools show relative overexpression of
corresponding miRNAs (Figure 4A). The levels of the relative fold change
are correlated well with the level of corresponding of endogenous miRNAs
expressed in the original CHO-S cell lines. The miRNAs that has lower
expression levels in original CHO-S cell lines, for example mir-122 and
miR30a, shows higher fold change when overexpressing the corresponding
miRNAs.

\subsubsection*{Glycoprofiling of OE cell
pools}

{\label{glycoprofiling-of-oe-cell-pools}}

The next step was to explore the phenotype of the miRNA overexpression.
We did this by glycoprofiling the secreted protein from the seven pools
and comparing them to a mock (Figure 4B). It is clear, that only
overexpressing miR-30b and miR-449a gave detectable changes in N-glycan
profiles, namely reduced levels of tri- and tetra- antennary structures,
and increase mono- and bi- antennary structures. This indicates that
miR-30b and miR-449a may also target genes that regulate GlcNAc
transferase reactions for addition GlcNac onto \selectlanguage{greek}β \selectlanguage{english}1,4 (Mgat4a, Mgat4b)
and \selectlanguage{greek}β \selectlanguage{english}1,6 branches (Mgat 5), and/or target UPD-GlcNAc tansporters
(SLC35A3, SLC35A4, SLC35A5), or possibly enzymes working earlier in the
N-glycan pathway. According to qPCR results of possible target genes
(Figure 6), we found that miR-30b levels correlate inversely with the
levels of Mgat4a, SLC35A3 and SLC35A5. Thus, miR-30b seems to target
Mgat4a, SLC35A3 and SLC35A5 mRNAs. Therefore, miR-30b may change
antennary structures of N-glycan by simultaneously regulate GlcNAc
transferase reactions and the availability of UDP-GlcNAc inside Golgi
apparatus. miR-449a, on the other hand, may only target Mgat4a and
reduce the levels of \selectlanguage{greek}β \selectlanguage{english}1,4 branched N-glycans.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 4.} - Characterization of miRNA OE cell pools. (A)
Quantification of mature miRNA by qPCR. (B) Glycoprofiling of miRNA OE
cell pools (Details are found in Supplementary Table 20 and
Supplementary Figure 2)\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Figure 5} - qPCR analysis for possible target glycosylation
genes of miR-34c and miR-128 KD cell lines.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\textbf{Figure 6} - qPCR analysis for possible target glycosylation
genes of miR-30b OE (B1), miR-449a OE (G1) and mock cell pool.

\subsection*{Discussion}

{\label{discussion}}

Along with our increased understanding of miRNAs and miRNA correlations,
it has become feasible to make at least rough predictions of the targets
of miRNAs. Furthermore, due to the ability of the miRNA to degrade or to
silence multiple mRNAs in a highly specific and potent way, makes miRNA
a highly interesting target for cell engineering; in particular of
complex traits such as N-glycosylation, which involves a large number of
enzymes working in concert. Hence, in this study we have tried to adopt
a composite prediction and experimental strategy to closer examine on
how miRNA regulating the N-glycan pathway in CHO cells

In this study, the strategy -- in addition to sequence-based prediction
-- was to use expression data of mRNA and miRNA to link miRNA with mRNA
based on correlation analysis. As we were interested in picking only
genes which are inhibited by miRNA, we have filtered for only those
mRNA-miRNA combinations that were negatively co-expressed. In
combination with a pathway analysis, to filter for N-glycan pathways
such as fucosylation, mannosylation, sialylation, galactosylation and
branching of N-glycan, we could focus on relevant genes. In the end,
this combination of approaches gave a list of likely miRNA-mRNA sets.
Furthermore, the experimental approach and analysis of mRNA and miRNA
sequencing has generated a lot of new knowledge on which miRNAs are
active in these cell lines, and provide a long list of hypotheses on
co-expressed genes. In total, we saw that four out of our ten miRNA
targets had an effect on N-glycosylation in the tested conditions,
showing that this field has an enormous potential for future
applications.

For instance, in our combined analysis, we saw that miR-128 should
theoretically combine with Alg9 and Man2a1, based on that mRNA
expression of Alg9 and Man2a1 was down-regulated and miR-128 was
up-regulated in EPO-producing cell lines as compared to parental cell
lines. Suggesting knock-out of miR-128 in EPO producing cell lines would
ideally increase the expression level of Alg9 and Man2a1, increasing
alpha 1,3 mannosyltransferase and mannosidase alpha class2 member1
activities. However, this was not the phenotype that was seen directly,
but instead, we saw a shift towards more mature (tri- and tetra-
antennary) N-glycans. This fitted well with the miRwalk predictions,
predicting binding to Mgat1 and Mgat4b (Glc-NAc transferases, involved
in increasing antennarity). A similar effect was found for miR-34c.
Here, Alg1 and B4galt2 were found in combination with miR-34c, where
expression levels supported an inverse correlation, and we noticed that
even though expression level of miR-34c was down regulated; it was
significantly expressed in EPO producing cell lines suggesting complete
functional knock-out of miR-34c to see increase in the activity of alpha
1,3 mannosyltransferase and beta1,4 galactosyltransferase. Furthermore,
Mgat1 and Mgat4 and St3gal1 (sialyltransferases) were predicted by
miRwalk to be targets (Table 1). In this cases as well, KD of the miRNA
increased the maturation. It thus seems - based on the prediction of the
functional targets - that increasing availability of early
N-glycosylation pathway enzymes improves maturation of the N-glycans.
This is very important for proteins such as EPO, as mature antennae and
sialylation is critical for serum half-life of EPO(Tsuda et al. 1990).
Similarly, for antibodies, mannose residues can increase antibody
clearance rates (Kaufman et al. 1991; K. H. Lee et al. 2019; Fabian
2010; J. Yang et al. 2015). Further, sialylation of native and
recombinant EPO increases its\emph{in vitro} biological activity,
possibly by increasing its affinity to receptor on target cells(Kaufman
et al. 1991). It was previously shown that making more sites available
for adding sialic acid by overexpressing B4gal1, also in tandem with
St3gal1(Kaufman et al. 1991; K. H. Lee et al. 2019). Terminal N-linked
glycosylation is directly related to N-linked sialylation. Thus, the
prediction that miR-128 and miR-34c inhibits early maturation enzymes
and Mgat1 and Mgat4, fits well with increased number of antennae, and
will be beneficial for certain recombinant protein production, e.g. EPO
production.

For the overexpression studies, only miR30b and miR-449a out of the
seven tested miRNAs gave a phenotype, again increased maturation of the
N-glycans. This was interesting, as these miRNAs did not have predicted
targets in the correlation analysis, but were predicted by miRwalk to
have a large number of gene targets in glycosylation.

In general, our study also shows that it is hard to predict the
phenotype of a miRNA perturbation solely based on sequence analysis,
even when supplemented with miRNA- and mRNA-seq. One reason for this may
be that miRNAs show as little complementarity as 2-8nt to their gene
targets, making them hard to identify using sequence alignment.
Furthermore previous studies(Kaufman et al. 1991; K. H. Lee et al. 2019;
Fabian 2010) have shown that higher complementarity with the mRNA with
their targets degrade the transcripts and lower complementarity leads to
translational repression , thus making both the prediction more
complicated, and the regulation even more complex. Even so, we managed
to find a desirable phenotype in 40\% of our predicted targets. Due to
limited capacity, we were only able to test 10 targets, but the full
data set (Supplementary Table 19) contains even more interesting future
targets, for examples, miRNAs with high correlation with FUT8
fucosyltransferase, galactotransferases B4galT1-8, sialylases St3gal1-6,
and sialic acid transporters SLC35A1-A5.

\subsubsection*{\texorpdfstring{\emph{Conclusion}}{Conclusion}}

{\label{conclusion}}

We have adopted a bifurcated comparative approach using both
computational and experimental elements to identify miRNAs with an
impact on N-glycan maturation. In total, 656 miRNAs were predicted, ten
were experimentally characterized and four were found to have beneficial
phenotypes for N-glycosylation. The present study is one among few
studies aiming to assess the impact of overall miRNAs on N-glycan
pathway of various CHO cell lines. The data generated from miRNA- and
mRNA-seq is an asset for the scientific community as it provides the
valuable understanding of some of the useful expression targets of the
genes. Finally, identification of CHO-specific miRNAs presented in the
study might serve as a tool to improve cell line engineering effort to
increase the overall productive robustness of the cell lines.

\subsection*{}

{\label{section-7}}

\textbf{Author contributions}

Yuzhou Fan -- design overall studies for both wet lab and dry lab,
developed protocols, conduct experiments, coordinate with RNAseq
experiment, process miRNA dry-lab data, analyze wet lab data, write
manuscript.

Ankita Singh -- design methods for data analysis, conduct RNAseq data
analysis and miRNA-mRNA correlation data analysis, write manuscript.

Martin Seifert -- conduct experiment to generate KO cell lines

Junrui Li1 -- conduct bioreactor experiment to generate RNAs for
sequencing

Louise Kjeldgaard Lyhne - conduct experiment to generate OV cell pools
and characterized OV cell pools

Selgin Deniz Cakal - conduct experiment to generate OV cell pools and
characterized OV cell pools

Gerald Klanert -- support with original protocol development for miRNA
OV and KO studies

Thomas Amann - support and conduct N-glycosylation sample preparation.

Anders Holmgaard Hansen - support with N-glycoprofiling by running LC-MS
and provide raw data files.

Nicole Borth -- support with methods and experience of miRNA engineering
in general

Gyun Min Lee -- supported with cell lines

Helene Faustrup Kildegaard -- overall design and support to miRNA KO
study and support with cell lines, manuscript editing

Mikael R\selectlanguage{ngerman}ørdam Andersen -- overall design and support for whole study,
manuscript writing.

\textbf{Refernces}

Amann, Thomas, Anders Holmgaard Hansen, Stefan Kol, Henning Gram Hansen,
Johnny Arnsdorf, Saranya Nallapareddy, Bjørn Voldborg, Gyun Min Lee,
Mikael Rørdam Andersen, and Helene Faustrup Kildegaard. 2019.
``Glyco-Engineered CHO Cell Lines Producing Alpha-1-Antitrypsin and C1
Esterase Inhibitor with Fully Humanized N-Glycosylation Profiles.''
\emph{Metabolic Engineering}52 (March): 143--52.

Anders, Simon, Paul Theodor Pyl, and Wolfgang Huber. 2015. ``HTSeq--a
Python Framework to Work with High-Throughput Sequencing Data.''
\emph{Bioinformatics} 31 (2): 166--69.

Bolger, Anthony M., Marc Lohse, and Bjoern Usadel. 2014. ``Trimmomatic:
A Flexible Trimmer for Illumina Sequence Data.'' \emph{Bioinformatics}
30 (15): 2114--20.

Cimmino, Amelia, George Adrian Calin, Muller Fabbri, Marilena V. Iorio,
Manuela Ferracin, Masayoshi Shimizu, Sylwia E. Wojcik, et al. 2005.
``miR-15 and miR-16 Induce Apoptosis by Targeting BCL2.''
\emph{Proceedings of the National Academy of Sciences of the United
States of America} 102 (39): 13944--49.

Clarke, Colin, Michael Henry, Padraig Doolan, Shane Kelly, Sinead
Aherne, Noelia Sanchez, Paul Kelly, et al. 2012. ``Integrated miRNA,
mRNA and Protein Expression Analysis Reveals the Role of
Post-Transcriptional Regulation in Controlling CHO Cell Growth Rate.''
\emph{BMC Genomics} 13 (November): 656.

Cong, Le, F. Ann Ran, David Cox, Shuailiang Lin, Robert Barretto, Naomi
Habib, Patrick D. Hsu, et al. 2013. ``Multiplex Genome Engineering Using
CRISPR/Cas Systems.''\emph{Science} 339 (6121): 819--23.

Csibi, Alfred, Sarah-Maria Fendt, Chenggang Li, George Poulogiannis,
Andrew Y. Choo, Douglas J. Chapski, Seung Min Jeong, et al. 2013. ``The
mTORC1 Pathway Stimulates Glutamine Metabolism and Cell Proliferation by
Repressing SIRT4.''\emph{Cell} 153 (4): 840--54.

Dobin, Alexander, Carrie A. Davis, Felix Schlesinger, Jorg Drenkow,
Chris Zaleski, Sonali Jha, Philippe Batut, Mark Chaisson, and Thomas R.
Gingeras. 2013. ``STAR: Ultrafast Universal RNA-Seq Aligner.''
\emph{Bioinformatics} 29 (1): 15--21.

Dweep, Harsh, Norbert Gretz, and Carsten Sticht. 2014. ``miRWalk
Database for miRNA--Target Interactions.'' In \emph{Methods in Molecular
Biology}, 289--305.

Fabian, Dirk. 2010. ``I-CAMP 2010 Australia Monday June 21 Dirk Fabian
Professional Networking SPIE Webcast.'' \emph{SciVee}.
https://doi.org/10.4016/18748.01.

Gammell, Patrick, Niall Barron, Niraj Kumar, and Martin Clynes. 2007.
``Initial Identification of Low Temperature and Culture Stage Induction
of miRNA Expression in Suspension CHO-K1 Cells.'' \emph{Journal of
Biotechnology} 130 (3): 213--18.

Gao, Ping, Irina Tchernyshyov, Tsung-Cheng Chang, Yun-Sil Lee, Kayoko
Kita, Takafumi Ochi, Karen I. Zeller, et al. 2009. ``C-Myc Suppression
of miR-23a/b Enhances Mitochondrial Glutaminase Expression and Glutamine
Metabolism.'' \emph{Nature} 458 (7239): 762--65.

Grav, Lise Marie, Jae Seong Lee, Signe Gerling, Thomas Beuchert
Kallehauge, Anders Holmgaard Hansen, Stefan Kol, Gyun Min Lee, Lasse
Ebdrup Pedersen, and Helene Faustrup Kildegaard. 2015. ``One-Step
Generation of Triple Knockout CHO Cell Lines Using CRISPR/Cas9 and
Fluorescent Enrichment.''\emph{Biotechnology Journal} 10 (9): 1446--56.

Hackl, Matthias, Vaibhav Jadhav, Tobias Jakobi, Oliver Rupp, Karina
Brinkrolf, Alexander Goesmann, Alfred Pühler, Thomas Noll, Nicole Borth,
and Johannes Grillari. 2012. ``Computational Identification of microRNA
Gene Loci and Precursor microRNA Sequences in CHO Cell Lines.''
\emph{Journal of Biotechnology} 158 (3): 151--55.

Hackl, Matthias, Tobias Jakobi, Jochen Blom, Daniel Doppmeier, Karina
Brinkrolf, Rafael Szczepanowski, Stephan H. Bernhart, et al. 2011.
``Next-Generation Sequencing of the Chinese Hamster Ovary microRNA
Transcriptome: Identification, Annotation and Profiling of microRNAs as
Targets for Cellular Engineering.'' \emph{Journal of Biotechnology} 153
(1-2): 62--75.

Hammond, Stephanie, Jeffrey C. Swanberg, Shawn W. Polson, and Kelvin H.
Lee. 2012. ``Profiling Conserved microRNA Expression in Recombinant CHO
Cell Lines Using Illumina Sequencing.'' \emph{Biotechnology and
Bioengineering} 109 (6): 1371--75.

He, Lin, Xingyue He, Lee P. Lim, Elisa de Stanchina, Zhenyu Xuan, Yu
Liang, Wen Xue, et al. 2007. ``A microRNA Component of the p53 Tumour
Suppressor Network.''\emph{Nature} 447 (7148): 1130--34.

Jadhav, Vaibhav, Matthias Hackl, Aliaksandr Druz, Smriti Shridhar,
Cheng-Yu Chung, Kelley M. Heffner, David P. Kreil, et al. 2013. ``CHO
microRNA Engineering Is Growing up: Recent Successes and Future
Challenges.''\emph{Biotechnology Advances} 31 (8): 1501--13.

Jadhav, Vaibhav, Matthias Hackl, Gerald Klanert, Juan A. Hernandez Bort,
Renate Kunert, Johannes Grillari, and Nicole Borth. 2014. ``Stable
Overexpression of miR-17 Enhances Recombinant Protein Production of CHO
Cells.'' \emph{Journal of Biotechnology} 175 (April): 38--44.

Kanehisa, Minoru, Yoko Sato, Miho Furumichi, Kanae Morishima, and Mao
Tanabe. 2019. ``New Approach for Understanding Genome Variations in
KEGG.'' \emph{Nucleic Acids Research} 47 (D1): D590--95.

Kaufman, R. J., M. V. Davies, L. C. Wasley, and D. Michnick. 1991.
``Improved Vectors for Stable Expression of Foreign Genes in Mammalian
Cells by Use of the Untranslated Leader Sequence from EMC Virus.''
\emph{Nucleic Acids Research} 19 (16): 4485--90.

Kildegaard, Helene Faustrup, Deniz Baycin-Hizal, Nathan E. Lewis, and
Michael J. Betenbaugh. 2013. ``The Emerging CHO Systems Biology Era:
Harnessing the 'Omics Revolution for Biotechnology.'' \emph{Current
Opinion in Biotechnology} 24 (6): 1102--7.

Kim, Jee Yon, Yeon-Gu Kim, and Gyun Min Lee. 2012. ``CHO Cells in
Biotechnology for Production of Recombinant Proteins: Current State and
Further Potential.''\emph{Applied Microbiology and Biotechnology} 93
(3): 917--30.

Kim, S. J., N. S. Kim, C. J. Ryu, H. J. Hong, and G. M. Lee. 1998.
``Characterization of Chimeric Antibody Producing CHO Cells in the
Course of Dihydrofolate Reductase-Mediated Gene Amplification and Their
Stability in the Absence of Selective Pressure.'' \emph{Biotechnology
and Bioengineering} 58 (1): 73--84.

Klanert, Gerald, Vaibhav Jadhav, Konstantina Chanoumidou, Johannes
Grillari, Nicole Borth, and Matthias Hackl. 2014. ``Endogenous microRNA
Clusters Outperform Chimeric Sequence Clusters in Chinese Hamster Ovary
Cells.'' \emph{Biotechnology Journal} 9 (4): 538--44.

Lam, Jenny K. W., Michael Y. T. Chow, Yu Zhang, and Susan W. S. Leung.
2015. ``siRNA Versus miRNA as Therapeutics for Gene Silencing.''
\emph{Molecular Therapy. Nucleic Acids} 4 (September): e252.

Law, Charity W., Monther Alhamdoosh, Shian Su, Gordon K. Smyth, and
Matthew E. Ritchie. 2016. ``RNA-Seq Analysis Is Easy as 1-2-3 with
Limma, Glimma and edgeR.''\emph{F1000Research} 5: 1408.

Law, Charity W., Yunshun Chen, Wei Shi, and Gordon K. Smyth. 2014.
``Voom: Precision Weights Unlock Linear Model Analysis Tools for RNA-Seq
Read Counts.''\emph{Genome Biology} 15 (2): R29.

Lee, Jae Seong, Thomas Beuchert Kallehauge, Lasse Ebdrup Pedersen, and
Helene Faustrup Kildegaard. 2015. ``Site-Specific Integration in CHO
Cells Mediated by CRISPR/Cas9 and Homology-Directed DNA Repair
Pathway.'' \emph{Scientific Reports} 5 (1).
https://doi.org/10.1038/srep08572.

Lee, Kyoung Hwa, Soon Young Park, Su Jin Jeong, Da Hyun Jung, Jie Hyun
Kim, Seok Hoon Jeong, Il Mo Kang, and Young Goo Song. 2019. ``Can
Aminoglycosides Be Used as a New Treatment for ? Activity of Recently
Isolated.'' \emph{Infection \& Chemotherapy} 51 (1): 10--20.

Lewis, Nathan E., Xin Liu, Yuxiang Li, Harish Nagarajan, George
Yerganian, Edward O'Brien, Aarash Bordbar, et al. 2013. ``Genomic
Landscapes of Chinese Hamster Ovary Cell Lines as Revealed by the
Cricetulus Griseus Draft Genome.'' \emph{Nature Biotechnology} 31 (8):
759--65.

Li, H., B. Handsaker, A. Wysoker, T. Fennell, J. Ruan, N. Homer, G.
Marth, G. Abecasis, R. Durbin, and 1000 Genome Project Data Processing
Subgroup. 2009. ``The Sequence Alignment/Map Format and SAMtools.''
\emph{Bioinformatics} 25 (16): 2078--79.

Mack, George S. 2007. ``MicroRNA Gets down to Business.'' \emph{Nature
Biotechnology} 25 (6): 631--38.

Masliah-Planchon, Julien, Simon Garinet, and Eric Pasmant. 2016.
``RAS-MAPK Pathway Epigenetic Activation in Cancer: miRNAs in Action.''
\emph{Oncotarget} 7 (25): 38892--907.

Maszczak-Seneczko, Dorota, Teresa Olczak, Piotr Jakimowicz, and Mariusz
Olczak. 2011. ``Overexpression of UDP-GlcNAc Transporter Partially
Corrects Galactosylation Defect Caused by UDP-Gal Transporter
Mutation.''\emph{FEBS Letters} 585 (19): 3090--94.

McCarthy, Davis J., Yunshun Chen, and Gordon K. Smyth. 2012.
``Differential Expression Analysis of Multifactor RNA-Seq Experiments
with Respect to Biological Variation.''\emph{Nucleic Acids Research} 40
(10): 4288--97.

North, Simon J., Hung-Hsiang Huang, Subha Sundaram, Jihye Jang-Lee, A.
Tony Etienne, Alana Trollope, Sara Chalabi, Anne Dell, Pamela Stanley,
and Stuart M. Haslam. 2010. ``Glycomics Profiling of Chinese Hamster
Ovary Cell Glycosylation Mutants Reveals N-Glycans of a Novel Size and
Complexity.'' \emph{The Journal of Biological Chemistry} 285 (8):
5759--75.

Pasquinelli, A. E., B. J. Reinhart, F. Slack, M. Q. Martindale, M. I.
Kuroda, B. Maller, D. C. Hayward, et al. 2000. ``Conservation of the
Sequence and Temporal Expression of Let-7 Heterochronic Regulatory
RNA.'' \emph{Nature} 408 (6808): 86--89.

R Development Core Team. 2003. \emph{The R Reference Manual: Base
Package}. Network Theory.

Robinson, Mark D., and Alicia Oshlack. 2010. ``A Scaling Normalization
Method for Differential Expression Analysis of RNA-Seq Data.''
\emph{Genome Biology} 11 (3): R25.

Ronda, Carlotta, Lasse Ebdrup Pedersen, Henning Gram Hansen, Thomas
Beuchert Kallehauge, Michael J. Betenbaugh, Alex Toftgaard Nielsen, and
Helene Faustrup Kildegaard. 2014. ``Accelerating Genome Editing in CHO
Cells Using CRISPR Cas9 and CRISPy, a Web-Based Target Finding Tool.''
\emph{Biotechnology and Bioengineering} 111 (8): 1604--16.

Sanchez, Noelia, Paul Kelly, Clair Gallagher, Nga T. Lao, Colin Clarke,
Martin Clynes, and Niall Barron. 2014. ``CHO Cell Culture Longevity and
Recombinant Protein Yield Are Enhanced by Depletion of miR-7 Activity
via Sponge Decoy Vectors.''\emph{Biotechnology Journal} 9 (3): 396--404.

Sealover, Natalie R., Angela M. Davis, Jeanne K. Brooks, Henry J.
George, Kevin J. Kayser, and Nan Lin. 2013. ``Engineering Chinese
Hamster Ovary (CHO) Cells for Producing Recombinant Proteins with Simple
Glycoforms by Zinc-Finger Nuclease (ZFN)-Mediated Gene Knockout of
Mannosyl (alpha-1,3-)-Glycoprotein
Beta-1,2-N-Acetylglucosaminyltransferase (Mgat1).'' \emph{Journal of
Biotechnology} 167 (1): 24--32.

Smyth, G. K. n.d. ``Limma: Linear Models for Microarray Data.'' In
\emph{Statistics for Biology and Health}, 397--420.

Strotbek, Michaela, Lore Florin, Jennifer Koenitzer, Anne Tolstrup,
Hitto Kaufmann, Angelika Hausser, and Monilola A. Olayioye. 2013.
``Stable microRNA Expression Enhances Therapeutic Antibody Productivity
of Chinese Hamster Ovary Cells.'' \emph{Metabolic Engineering} 20
(November): 157--66.

Su, Shian, Charity W. Law, Casey Ah-Cann, Marie-Liesse Asselin-Labat,
Marnie E. Blewitt, and Matthew E. Ritchie. 2017. ``Glimma: Interactive
Graphics for Gene Expression Analysis.'' \emph{Bioinformatics} 33 (13):
2050--52.

Tsuda, E., G. Kawanishi, M. Ueda, S. Masuda, and R. Sasaki. 1990. ``The
Role of Carbohydrate in Recombinant Human Erythropoietin.''
\emph{European Journal of Biochemistry / FEBS} 188 (2): 405--11.

Walsh, Gary. 2014. ``Biopharmaceutical Benchmarks 2014.'' \emph{Nature
Biotechnology} 32 (10): 992--1000.

Werner, Rolf G., Kristina Kopp, and Michael Schlueter. 2007.
``Glycosylation of Therapeutic Proteins in Different Production
Systems.'' \emph{Acta Paediatrica} 96: 17--22.

Wong, Danny Chee Furng, Niki Soo Ching Wong, John Soo Yang Goh, Lee May
May, and Miranda Gek Sim Yap. 2010. ``Profiling of N-Glycosylation Gene
Expression in CHO Cell Fed-Batch Cultures.'' \emph{Biotechnology and
Bioengineering} 107 (3): 516--28.

Yang, Jane, Ronya Primack, Mike Frohn, Wei Wang, Peng Luan, Marc W.
Retter, and Gregory C. Flynn. 2015. ``Impact of Glycation on Antibody
Clearance.'' \emph{The AAPS Journal} 17 (1): 237--44.

Yang, Zhang, Shengjun Wang, Adnan Halim, Morten Alder Schulz, Morten
Frodin, Shamim H. Rahman, Malene B. Vester-Christensen, et al. 2015.
``Engineered CHO Cells for Production of Diverse, Homogeneous
Glycoproteins.'' \emph{Nature Biotechnology} 33 (8): 842--44.

Zhang, Peiqing, Susanto Woen, Tianhua Wang, Brian Liau, Sophie Zhao,
Chen Chen, Yuansheng Yang, et al. 2016. ``Challenges of Glycosylation
Analysis and Control: An Integrated Approach to Producing Optimal and
Consistent Therapeutic Drugs.''\emph{Drug Discovery Today} 21 (5):
740--65.

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