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\title{Molecular tools for synthetic biology in plants: a first generation open
bioinformatics workshop.}


\author{Ron Shigeta}
\affil{Affiliation not available}

  
\author{Niranjana Nagarajan}
\affil{Affiliation not available}
  
\author{Bryce}
\affil{Affiliation not available}
  
\author{Jamey}
\affil{Affiliation not available}
  
\author{Scott Fay}
\affil{Affiliation not available}
  
\author{Awaiting Activation}
\affil{Affiliation not available}
  
\author{Corey Hudson}
\affil{Affiliation not available}
  



\date{\today}

\maketitle

\subsection{Abstract}\label{abstract}

Synthetic biology has had profound effects on human life. It has
provided more effective anti-malarial medicine, cheaper insulin, new
useful bio-materials, and greener biofuels. However, much remains to be
learned in order to synthesize proteins more efficiently. To explore the
potential of the DIY biology movement to engage in meaningful synthetic
biology bioinformatics research, we developed a bioinformatics workshop
to study determinants of protein expression levels in plants. We
extracted possible ribosome binding and translation initiation sequences
and looked for correlations with experimentally determined protein
levels, using publicly available data sets for the widely studied plants
\emph{Oryza sativa} and \emph{Arabidopsis thaliana}. The working group
was open to the public and met every other week for 3 hours, typically
starting with a short, relevant presentation followed by hands-on data
work. We aim to develop, experimentally validate, and publish our
consensus sequences, anticipating that our work will be useful for plant
synthetic biology research. We hope our experience will serve as a model
for future community projects that serve the dual purpose of educating
curious members of the public while also generating useful scientific
results.


\textbf{Authors} Ron Shigeta, Niranjana Nagarajan, Shriram Bharath,
Wilifred Tang, Tony Hecht, Alex Alekseyenko, Bryce Wolfe, Corey Hudson,
Jamey Kain, Urvish Parikh, Scott Fay, Kyle Taylor

Loosely sponsored by Counter Culture Labs and Berkeley Bio Labs

Please address correspondence to ron@berkeleybiolabs.com

\textbf{Introduction}

Advances in sequencing technology have produced an avalanche of
biological data over the past 12 years. The bottleneck in discovery has
consequently shifted from data generation to data analysis, suggesting
that much data is not used to its full potential \cite{Lockhart_Winzeler_2000}.

Crowdsourcing is one technique to gain more insight from existing
biological data. Putting the diverse eyes and hands of the general
public to the purpose of bioinformatics is not new \cite{Good_Su_2013}
\cite{ld_Allison_Bonneau_et_al__2012}; examples include protein \cite{lane2012milliseconds} and RNA
folding {[}http://eterna.cmu.edu/web/{]}, and both paid (Ingenuity\selectlanguage{ngerman}®
Systems, www.ingenuity.com) and unpaid \cite{hingamp2008metagenome} curation of
literature.

Rather than approach a problem strictly as professionals, we developed
an Open Source DIY workshop where scientists and the public worked
together to tackle a synthetic biology project resulting in a
publishable outcome. The problem to be solved would need data from
completely open sources and not require difficult analysis. A modest
goal was set to do a survey of plant translation initiation motifs,
aiming to create an open source parts list for controlling translation
in metabolic engineering and synthetic biology. Working meetings were
posted through Counter Culture Labs and Berkeley Bio Labs (groups with
\textgreater{}100 members each) on meetup.com and met every week or two
over three months.

Plants offer many advantages as systems to do fine-tuned biological
engineering {[}e.g., modification to enhance production of economically
valuable terpinoid \cite{moses2013bioengineering}, modification of lignin
biosynthesis to expedite biofuel synthesis \cite{li2008improvement}{]}. There
is a paucity of published information, however, on how to control sets
of genes working in concert. Use of small sequence motifs as ribosome
binding site parts for synthetic biology has been proposed in bacteria
{[} \cite{Salis_Mirsky_Voigt_2009} see also:
http://parts.igem.org/Ribosome\_Binding\_Sites/Prokaryotic/Constitutive/Anderson.{]}
and similar parts have been produced for yeast {[}parts.igem.org{]}.
Estimates for RBS parts in prokaryotic systems show that the translation
level of a gene can be shifted by greater than an order of magnitude,
indicating their potential utility in synthetic biology projects.
Generating an estimate of the regulatory power of plant translation
initiation motifs was thus seen as a useful goal for our project.

In most eukaryotic plant genes the 5' cap of the mRNA transcript acts as
the ribosome binding site and the Kozak sequence acts as the signal for
translation initiation. Due to the bacterial origins of the chloroplast,
transcripts of genes encoded within the chloroplast genome contain
distinct consensus sequences in comparison to transcripts from the
nucleus. Instead of the 5' cap there is a short motif called the
Shine-Delgarno sequence where the ribosome binds and then initiates
translation, generally 8 nucleotides downstream, though this distance
varies. Although there has been some experimental work on ribosome
binding sites and Kozak sequences in plants {[}refs, perhaps Lutcke et
al EMBO J 1987{]}, genomic-scale surveys have not been performed.

Here we use publicly available, combined RNA- and protein expression
data for both nuclear and chloroplast genes to estimate the power of the
ribosome binding and translation initiation sequence motifs to initiate
translation. These are initial results; experimental confirmation of the
motifs will follow.

\textbf{Methods}

\textbf{Plant Genome Survey and motif extraction.} A broad survey of the
translation initiation motifs from both the TAIR10 \emph{Arabidopsis
thaliana} genome build \cite{eyer_Muller_Ploetz_et_al__2007} and the IRGSP 1.0 Japanese
Rice Genome \cite{rtz_Tanaka_Wu_Zhou_et_al__2013} was carried out. In order to capture
translation initiation motifs as well as possible leader peptide
sequences, the Gene description GFF files were used to extract the 25
bases before and 18 bases after the start codon of each gene for each
genome build. The terms ``CDS'' or ``mRNA'' were used to extract protein
coding regions. With the data from the rice genome, we were unable to
separate coding sequences in all three possible reading frames,
therefore we excluded codign sequences that did not initiate with the
canonical ``ATG'' start codon.

\textbf{Chloroplast survey and motif extraction.} Because of the small
number of genes in the chloroplast, a broad collection of motifs were
also extracted for chloroplasts. The GenBank chromosome sequences were
scraped from the Choloroplast DB webpage \cite{Cui_2006} and used to
extract motifs using BioPython \cite{cock2009biopython}. This yielded 11810
initiation motifs from 109 organisms, which gave good consistency in the
start codon with translation initiation generally occurring 8
nucleotides downstream of the ribosome binding site, as expected.

\textbf{Transcriptome Data} As we could find no publicly available
matched proteome / transcriptome datasets, we obtained 10 arrays each
from arabidopsis leaf and rice leaf. All replicate arrays for noon-time
leaf expression in adult plants were obtained from Gene Expression
Omnibus \cite{ippy_Sherman_Holko_et_al__2012}, via GEOSearch \cite{vis_Stephens_Meltzer_Chen_2008}.

Table {[}1{]} lists the data sets that were chosen in the Workshop
session.\selectlanguage{english}
\begin{longtable}[]{@{}lcr@{}}
\toprule
Tables & Are & Cool\tabularnewline
\midrule
\endhead
col 3 is & right-aligned & \$1600\tabularnewline
col 2 is & centered & \$12\tabularnewline
zebra stripes & are neat & \$1\tabularnewline
\bottomrule
\end{longtable}

Overall 10 arrays including replicates of four separate measurements
were downloaded as Affymetrix Arabidopsis Genome ATH1 and Rice Genome
Array CEL files. These were scaled using the MAS5 algorithm
\cite{lim2007comparative} using the `affy' Biocoductor library
\cite{affy2004} in R \cite{R_Core_Team_2013}. The resulting data frame was
reduced to mean, median and standard deviation estimates for each probe
set for Rice and Arabidopsis each. The results were that the mean
measurement standard deviation was 71 \% and the median differed by 19\%
from the mean, indicating a reasonable sample variance that was
satisfactory where doubling of intensities is considered significant.

\textbf{Proteome Data} The Rice Proteome Project has a comprehensive set
of quantitative proteome estimates from 2D SDS PAGE gel including
different stages of the plant growth, portions of the plant as well as
an organelle survey. Quantitation from gel densitometry, MASCOT scores
and uniprot associations were downloaded as tables \cite{i_Tozawa_Yoshinaga_et_al__2004}.

Only a few hundred measurements were found from multiple sources for
Arabidopsis, which did not cover the organelles explicitly and less than
10\% of the known leaf proteome. As the data proved to be inadequate for
this study, the Arabidopsis survey had to be set aside.

Uniprot identifiers were mapped to rice probe set identifiers. Many of
the Uniprot identifiers were directly mappable to probe set (236 out of
554 identifiers: split among 123 chloroplast, 235 mature leaf, and 196
seedling leaf probes), using the Rice Coexpression Database
\cite{Sato_2013}. The remaining Uniprot identifiers were manually
mapped, BLASTP searching Uniprot sequence against the \emph{Oryza
sativa} Nipponbare reference genome \cite{Kawahara_2013}.

\textbf{Translation Initiation Estimation for motifs} The
interrelationship between Rice Gene, Protein in the Proteome set, and
the MicroArray Probe set required several data sources. The Probe Set
Annotation data for the Rice IVT Expression Array was extracted from the
Probe Set Annotation CSV file provided by Affymetrix
\cite{Liu_2003}. Because UniProt accessions drift over time, the
Rice Proteome, which was generated circa 2004, had no protein accessions
which were currently in uniprot. Data relationships to gene names and
probe sets were assembled through multiple processes. Reviewing archival
Rice Genome Array annotations were able to find about 50\% of the probe
set mappings we needed and the rest were recovered manually by searches
of uniprot.org and if necessary, BLAST alignment of nucleotide sequences
against the Rice Genome at MSU.edu.

For genes which had proteome protein concentration estimates, the
translational coefficient, \(\Theta\), for a given gene was
estimated as the ratio of the protein to the mean RNA concentration as
estimated by the microarray intensity.

\(\Theta\) = {[}Protein{]}/{[}mRNA{]} \{1\}

In order to reduce the influence of outliers, the mRNA concentration was
takes as the median of the microarray values from the 10 data sets. This
reduced the range of the values by 2 logs compared to taking the mean
microarray probe set intensity.

\textbf{Results}

\textbf{Genome Surveys} Surveys of the nuclear chromosomes of
\emph{Arabidopsis thaliana} and \emph{Oryza sativa japonica} yielded
thousands of sequence motifs. A conventional logo survey
\cite{Crooks_2004} shows the expected Kozak sequence in the nuclear
genes (see figure 1). In the case of chloroplast chromosome, since the
Shine-Delgarno sequence does not have a fixed location with respect to
the start codon \cite{Hirose_2004}, the weblogo does not show any
appreciable signal {[} Figure 2{]}\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Chr1-ATG-CDS/Chr1-ATG-CDS}
\caption{{Figure 1: Sequence Logo of Chromosome 1 of Oryza sativa japonica, derived from 2134 sequences, restricted to those initiating with an ATG codon. This logo shows a canonical Kozak motif surrounding the initiating ATG.  The X-axis represents the nucleotide position 20 bases upstream and 20 bases downstream of the ATG initiation codon. Some information in the wobble bases (third position) shows in the coding portion of the sequence. The other chromosomes were similar.%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/ChrC/ChrC}
\caption{{Figure 2: Sequence logo of Chloroplast translation initiation motifs.  The X-axis represents the nucleotide position 20 bases upstream and 20 bases downstream of the ATG initiation codon. Bias in the wobble base of the codons is much more pronounced in this logo since only 81 sequences were available to analyze in Arabidopsis cholorplasts.%
}}
\end{center}
\end{figure}

\textbf{Translation Initiation Estimates}

The relative Power estimates for the proteome to transcript ratio range over 12 powers of natural log (see figure 3 below), which is 165,000.  The average value is -2.4 with an assymetrical distribution, with a greater range for enhancements to protein production ( \(\Theta\) > 1 ).

The correlation between mRNA and protein available in the cell turned out to be poor - the mRNA and proteome scores had a correlation of 0.12, which implies that there are likely several factors that are influencing both of these numbers that go into \(\Theta\), indicating that the model is too simple.\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/chloroplast.MedianPower.hist/chloroplast.MedianPower.hist}
\caption{{Figure 3: Histogram of relative chloroplast protein to mRNA abundance ratio (Transcript Initiation Power).  Using the Median value of the microarray the ratio varied 59,000 fold.%
}}
\end{center}
\end{figure}

\textbf{Next Steps}

Though the workshop has performed some novel analyses, this is a
preliminary work. It's clear the estimate of transcript initiation has a
tremendous amount of uncertainty associated with it. Microarray probe
sets are not distinctly comparable with each other as the specific
sequences of the probes vary in their target affinity.

An abundance of cell processes can affect the actual amount of protein
produced compared to the mRNA reported by a microarray. Just a few of
these may include nonsense mediated decay, inhibitory RNA,
post-translational editing, protein sorting amongst cellular
compartments, secondary structure in the mRNA.

Still for the largest and smallest \(\Theta\) values, the values
determined might give some correlation with strong and weak translation.
We will next test the leader sequences the largest Transcription
Initiation Power \emph{in vivo} in collaboration with the glowing plant
project. To this end we'll be taking the motifs for the 10 largest and
some smaller \(\Theta\) motifs and installing the sequence into
a plasmid that can be validated in a plant cell by quantitation of
florescence from a GFP vs a control construct with its current
constitutive motif sequence. When their relative strengths have been
determined, the parts themselves will be placed in the golden Braid
public repository \cite{olo_Blanca_Granell_Orzaez_2013}.

The collection of motifs will also enable us to examine chloroplast
Shine-Delgarno sequences and their relative effects on translation.

\textbf{Open Workshop}

One of us (RS) initiated the Open Workshop as an experiment to bring
together the populations of curious laymen, experienced wet biologists,
and software engineering talent in the East Bay area, and all three of
these groups were represented in the attendees of the workshop. In
addition, several working bioinformaticians contributed.

The project was structured to give an introduction and purpose to
looking at a variety of publicly available biological data. The first
five meetings each were spent on a category of biological data:
chromosomal sequences; individual open reading frames; microarray data;
quantitative proteomics data in 2D Polyacrylamide Gel Electrophoresis;
and quantitative proteome data from Gas Chromatography/ Mass
Spectroscopy. In each of these sessions, data was gathered from public
sources and participants worked. Attendance ranged from 25 to 30
participants. As a public-scientific interface event, hands on work with
data and computers was quite engaging and several useful scripts were
written to process the data in python and R.

The following two months of biweekly data analysis sessions were less
fully attended, with an average of 2-4 participants. Possible reasons
for this decline include lack of understanding of the subject matter or
technical skills needed to fully participate, inability to commit to an
extended project, and unclear direction or incentives to continue. The
more open ended nature of data interpretation and analysis is also a
difficult process to relate to an introductory courss; it was difficult
for newcomers to biology to attach to these tasks.

Future workshops may be structured into beginner, intermediate and
advanced levels that would be more accessible to participants from
diverse educational backgrounds and will likely be shorter in length to
reduce attrition. Another idea is to take on a project with the sole
goal of doing that project, rather than anticipating publishable
results. As an experiment, the workshop did succeed in bringing together
a range of talent and covered a broad set of biological data.

Slides for these sessions are available at
http://boundaryconditions.org/biology.html. When we have completed
screening out parts, scripts, data collected and analysis for this
project will be made available here:
https://bitbucket.org/ronbo/glowingplantparts

The authors would like to thank SudoRoom, a tech Maker space in downtown
Oakland, CA, for physically hosting the workshop. We would also like to
thank the many other individuals who came to the workshop at one time or
another. We didn't get everyone's full name, but thanks include: Felicia
Betancourt, Ryan Behthencourt, Jack Cunha, Ruchira Datta, Timon
D'Essarviard-Aatenhejm, Cristina Deptula, A Dangerfield, N Lynne Fix,
Brian Gordon, Carl Gorringe, Louis Huang, Rajat Jain, Matt Jungert,
Patrick O'Connor, Marcus Owens, Ken Ozburn, Barry Levine, Thomas Levine,
Troy Massey, Ahnon Milman, Anthony Repetto, Johan Sosa, Nick Steigmann,
Sasha Tocryani, Joseph Walsh, Heather Wilson and Kate Wright.

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