We thank Benjamin Knowles, Yan Wei Lim, Andreas Haas, and members of the Viral Dark Matter consortium for their help and constructive input on this manuscript. This research is funded by the National Science Foundation (DEB-1046413) and is part of the Dimensions: Shedding Light on Viral Dark Matter project.

<ol>
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The authors have nothing to disclose.

Here, we present phenomic approaches for the functional characterization of putative phage genes. Techniques include a developed assay capable of monitoring host anabolic metabolism, the Multi-phenotype Assay Plates (MAPs), in addition to the established method of metabolomics, capable of measuring effects to catabolic metabolism. We provided additional tools to manage the large data sets resulting from these technologies, allowing for high throughput processing and analysis24. Lastly, through the comparison of an annotated phage capsid protein, phage thioredoxin, two putative metabolic phage genes, and the average experimental response we propose various strategies to interpret both datasets and gene classes, with emphasis on identification of phenotypic trends and identification of outliers.
As mentioned, both approaches quantitatively measure only half of host metabolism. To interpret the relative function of any of the novel proteins under investigation, data from both methods is required to provide evidence of function. While this is not a focus of our current manuscript, data outputs from each phenomic method is put through combinatory analyses that focus on clustering techniques such as random forest and principal component analysis. Furthermore, hypotheses resulting from the combined analysis must be subsequently validated by traditional genetic methodologies.
Finally, the methods presented are heavily influenced by bacterial physiology and therefore follow the same standards. When undertaking either method, considerations need to be made to ensure independent, clonal groups are experimented with; contamination is prevented; a single variable is being tested; and appropriate controls are being ran simultaneously. Failure to account for these points will result in unclear results, similar to any physiological assay.
Multi-phenotype Assay Plates (MAPs)
The development of MAPs provides a high throughput and adaptable assay compared to technologies currently available (Figure 5A and Tables 1,2). The assay uses supplies, equipment, and fundamental techniques available in all microbiology labs. The incorporation of a computational pipeline, PMAnalyzer24, for subsequent data processing and analysis ensures rapid data interpretation. In addition, both experimental and analytical aspects of the approach can be readily adjusted or tuned for customized purposes. For example, if a large proportion of the data fails to pass filtering outlined in section 4, one can manually sift through the growth curves to identify issues. If the problem arises due to stringent filter parameters, adjustments to the script can be made. Alternatively, if problems are associated with the experimental process (i.e., prolonged condensation; improper transferring of bacterial cells, etc.) then additional replicates can be readily repeated.
As described in Cuevas et al.24, the PMAnalyzer is a single bash program written as a wrapper script that executes the parsing and analysis scripts as a cohesive, automated pipeline. All scripts are freely accessible from a Git repository at 25 by taking the median value for each time point across triplicate data, and subsequently parameterizes the logistic curve to obtain the lag time, maximum growth rate, asymptote, and a novel term, Growth Level. The median value was chosen over the mean in our study to reduce the effect of large outliers, however, the script can be readily adapted to calculate the mean of replicate data. Due to reduced variation (SE) seen across replicate data (Figure 2A) we maintained the use of the median in the PMAnalyzer for fitting a logistic curve. Additionally, the cut off for growth in this study (GL &#8805; 0.4) was determined by comparing how data separated across Growth Level and maximum growth rate (Figure 1A,B). Depending on the instruments and model system used this term may vary, requiring redefinition of this cut off value.
A major advantage of our assay is the ability to compare phenotypes using a single parameter characterizing overall microbial growth, which we define as Growth Level (GL). GL is a harmonic mean, and therefore mitigates the effects of large outliers in the data. The use of a harmonic mean with shifted logistic-fitted values to provide a summary of growth was arrived at through trial and error. Other methods attempted to differentiate growth included: time it took to reach specific curve parameters (half &#181;max, &#181;max, and carrying capacity), the coefficient of determination (R2), and combinations of the R2 multiplied by specific curve parameters. Using a harmonic mean with shifted logistic-fit values for the GL provided the greatest range in evaluating growth, thus it became the method of choice. One consideration to note is that dynamic growth curve patterns have the potential of being lost when using a single parameter or a fitted model. For instance, the individual curve parameters of the logistic curve and the GL are incapable of representing biphasic growth. In a single carbon environment, this effect on growth implies mediation of the viral protein on either conversion of the substrate or shift in substrate utilization. Additional effects potentially lost when not considering multiple growth parameters include: prolonged lag time, proposing an increased burden of viral machinery or products; rapidly accelerating exponential phase, suggesting viral proteins coupled to host energy production pathways; or higher levels of biomass formation, implying viral support in host nutrient uptake and anabolism (data not shown). Thus, plotting nascent growth curves (Figure 2A,B) provides information regarding trends over time whereas the GL takes into account the major variables of the logistic model, providing a single quantitative number to represent overall success of a clone.
When considering the different responses contributed by structural and metabolic genes in the MAPs, it is observed that the different substrate classes in question provide the greatest evidence for protein function. For example, metabolic proteins are often associated with acquisition of limiting nutrients, which are unspecific to host central metabolism16,32. Preliminary MAP experiments reveal that clones harboring putative metabolic phage genes have an increased lag phase when grown on central metabolism carbon sources (Figure 2A). Conversely, clones carrying putative structural genes, which require large proportions of host energy and dNTP pools, result in a false positive response on growth for central and amino acid metabolism carbon substrates. This is likely due to the accumulation of insoluble proteins resulting in host filamentation and/or inclusion bodies, as observed via microscopy (Figure 2A and data not shown). While further analysis is required to validate these preliminary results, the MAPs are capable of retrieving phenotypic responses that correlate to hypothesized functions of specific phage gene classes.
In addition to the elucidation of unknown viral proteins, the MAPs are a novel resource to investigate the functional and metabolic diversity of an individual bacterium or a community of bacteria. MAP components are designed for easy alteration to support the growth of a range of bacteria; including marine, auxotrophic, and anaerobic microbes. To facilitate these efforts the defined basal and pre-growth media require additional or adjusted chemical species before a different bacterial genus can be supported in the MAPs. One note in this use of the MAPs is to maintain defined media, prohibiting the use of ingredients such as tryptone, yeast extract and peptone.
Metabolomics
The field of metabolomics is dependent on metabolite databases, which include isolated metabolites identified by mass spectrometry. The core facility chosen here has one of the largest metabolomics databases. Interestingly, more than half of the metabolites resulting from our experimentations were unidentifiable (~65%), while others had never before been recorded in our host, Escherichia coli (examples include: Indole 3 acetic acid33, salicylic acid34, and dihydroabietic acid35). This fact could be attributed to either a strong bias of the database towards plant metabolites, or the specific proteins under investigation. Regardless, the result is a limited number of known metabolites available for data representation and analysis. In the future, multiple metabolomics methods using various databases would allow for greater metabolite coverage.
Presently, both known and unknown metabolites are used when comparing and contrasting our novel viral proteins. Using this approach, we hypothesize that clones harboring functionally similar proteins will share an increased similarity in their complete metabolomic profile. Preliminary metabolomics analysis revealed that while structural and metabolic genes do not clearly separate from one another, those genes exhibiting similar effects on the host when overexpressed do correlate (Figure 6). For example, the annotated Capsid gene clusters closely with the putative metabolic genes highlighted in this study, EDT2440 and EDT2441. Investigations using a publicly available transmembrane topology and signal peptide predictor program showed evidence that both putative metabolic genes harbor a single transmembrane domain. Interestingly 5 out of the 9 clones in the first cluster group (most left portion of the dendrogram) have predicted transmembrane domains using the same topology program. Further investigations are needed, however, it is likely that the metabolites present during the overexpression of these clones are associated with cellular stress response resulting from membrane or structural burdens. This evidence supports that while the metabolomics data possesses an increased amount of noise, the method is capable of highlighting signals that differentiate general effects of genes, both within and across a gene class. To determine whether the method is capable of extracting out specific information of gene function, metabolites were grouped into specific metabolic pathways. The hypothesis being, if a clone affects metabolites specific to a single pathway, then the overexpressed gene is active in that pathway. Prior to the establishment of our metabolomics quality assurance pipeline, preliminary data revealed that over and underrepresented metabolites were typically &#8220;unknown&#8221;, providing little information on the pathways they are associated with (data not shown). Preprocessed metabolomics data, however, reveals that the majority of the metabolite profiles are similar and only a select number of unknown and known metabolite abundances vary across clones, for instance putrescine and uracil (Figure 6). To provide greater resolution of protein function efforts are being made to experimentally compare the novel phage genes against known phage genes, which can be used to fill in the &#8220;holes&#8221; of metabolite based functional characterization. Using this technique, the assigned function of known viral genes provides a reference for the function of the unknown genes. Nonetheless, the limiting factor of metabolomic analysis is the size and relevance of the database. To correct for these limitations, metabolomic databases relatable to this research need to be developed; such as a database of metabolites and their abundances specific to the ASKA collection of E. coli clones in which a single ORF is overexpressed36. Evidence for the need of such databases was provided in 2013 when researchers at the Lawerence Berkeley National Laboratory compiled the first comprehensive database of metabolites specific to entire mutant libraries of model bacteria37. This research provided novel insight into genes required for utilization of specific metabolites, revealing the clear connection between phenotype and genotype.
When considering metabolomics as a tool, it is important to define the processing regime followed at the core facility. An artifact of most experimental procedures is the day-to-day variance associated with the instruments of use. To date all GC-MS analysis implements the use of internal standards that are included in each analytical run; however, addition of project specific internal samples ran each day of experimentation removes additional variance. These considerations must be addressed early to avoid normalization problems and biases. Another solution is to process all samples at a core facility on the same machine and as a single batch, an option available at any core facility.
The various tools both introduced and re-explored in this manuscript provide novel means to screen and characterize functionally unknown phage genes. The simplicity and adaptability of the experimental techniques with the streamline use of computational pipelines assures these methods are applicable to a broad range of research endeavors and fields. Our goal is that the phenomic approaches presented here will aid further investigations of novel phage proteins in addition to systems that are equally functionally undefined.

Viruses that infect Bacteria (a.k.a. bacteriophage or phage) are estimated to exist at more than 1031 virus like particles (VLPs) globally and outnumber all other organisms in an environment1,2. The first metagenomic study investigating the viral communities associated with marine environments focused on quantifying the diversity seen within the viral fraction3. Additionally, Breitbart and colleagues found that over 65% of the viral community sequences shared no homology to any sequences available in public databases. Subsequent metagenomic studies found similar evidence: metagenomes from marine sediments in San Diego, California contain 75% unknown viral sequences4; metagenomes from hypersaline lakes of the Salton Sea contain 98% unknown viral sequences5; and coral&#8211;associated metagenomes contain 95 - 98% unknown viral sequences6. This accumulation of unannotated information has resulted in phage genetic material being &#8220;the dark matter of the biological universe&#8221;7.
Genomic characterization of phage relies on identifying sequence similarity through comparison against existing nucleic acid and protein databases. Because phage-encoded genetic information is predominantly unknown, homology-based methods are ineffective. Within their genome, phages typically encode three major gene types: transcription and replication genes, metabolic genes, and structural genes. The transcription and replication genes (class I/II genes8) include polymerases, primases, endo/exo-nucleases, and kinases. These genes are highly conserved due to their importance in phage infection, transcribing and replicating phage genetic material. Phage polymerases are readily identified using traditional sequence homology methods due to their global conservation9 and have been shown to serve as effective phylogenetic markers10. In contrast, phage metabolic and structural genes (class II/III genes8) are increasingly divergent and often annotated as hypothetical genes.
Phage metabolic genes affect the metabolic capacity of the host and are not necessarily required for viral replication. These genes, often referred to as auxiliary metabolic genes11 (AMGs), appear to modulate host metabolism and allow optimal progression of infection and success of virion maturation. AMGs have been associated with the utilization and uptake of limiting nutrients or in energy production pathways. Some examples include photosystem genes found in the genomes of various cyanophage12-16, genes connected to and regulated by phosphate metabolism17,18, and utilization of the pentose phosphate pathway for phage dNTP biosynthesis18,19. In comparison, structural genes are among the mid to late genes produced during infection and vary across different phage-host systems. The production of structural proteins are dependent on the availability of viral dNTP, and energy pools for their transcription, translation, and assembly8. The capsid and tail fiber structural proteins are deemed as the most divergent of all viral protein-encoding genes and are required for successful virion production. Their divergence is typically attributed to the active role they play in shaping virus-host coevolution20. Divergent proteins, regardless of the gene class, are readily overlooked when using traditional homology and sequence alignment techniques. An effort to correct for the limitations seen with strict sequence comparisons has resulted in bioinformatics tools capable of using sequence characteristics to determine association, such as artificial neural nets21. Artificial neural nets (ANNs) allow for the prediction of structural and metabolic genes, however, require downstream experimental validation to directly characterize gene function.
The objective of this manuscript is to provide phenomic protocols capable of monitoring both catabolic and anabolic metabolism of a host bacterium during the expression of a novel phage gene, functionally predicted through ANNs. The field of phenomics, the biology associated with cellular phenotypes, is well established in systems biology to aid in the investigation of proteins with unknown or pleiotropic function. Phenomic tools are used to link phenotypic information to genotypic information. We hypothesize for putative phage genes that their function(s) can be determined through observing host physiological effects during phage gene expression. To investigate this hypothesis, two quantitative methods were chosen. Multi-phenotype Assay Plates (MAPs) were used to monitor host substrate utilization and the subsequent biomass formation while metabolomics measured host metabolite diversity and relative abundance during growth in specific environmental conditions. Putative structural and metabolic proteins were overexpressed in Escherichia coli and representative results from both experiments are compared. Numerous visual techniques and high throughput processing pipelines are presented to facilitate experimental replication. Lastly, the reproducibility and accuracy of the presented methods are discussed in the context of expected physiological effects for an annotated capsid protein and phage metabolic protein, thioredoxin, plus two putative AMGs.

<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
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			<td>0.22&#181;m Sterivex Filter</td>
			<td>Fisher Scientific</td>
			<td>SVGP01050</td>
			<td>Millipore</td>
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			<td>0.22&#181;m Millex Filters</td>
			<td>Fisher Scientific</td>
			<td>SLGV033RS</td>
			<td>Millipore</td>
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			<td>0.22&#181;m SteriCap Filter</td>
			<td>Fisher Scientific</td>
			<td>SCGPS02RE</td>
			<td>Millipore</td>
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			<td>0.22 &#181;m Omnipore membrane filters</td>
			<td>Millipore</td>
			<td>JHWP02500</td>
			<td>Millipore</td>
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			<td>96 well micro-titer plates&#160;</td>
			<td>VWR</td>
			<td>82050-764</td>
			<td>Standard F-Bottom 96 well Microplates</td>
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			<td>2 mL 96 well plate</td>
			<td>Fisher Scientific</td>
			<td></td>
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			<td>Adhesive Seal Plate Film</td>
			<td>Sigma-Aldrich</td>
			<td>Z369667</td>
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			<td>2 L Nalgene square bottles</td>
			<td>Cole Parmer</td>
			<td>T-06040-70</td>
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			<td>125 mL Nalgene square bottles</td>
			<td>Cole Parmer</td>
			<td>T-06040-50</td>
			<td></td>
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			<td>1/4inch Panel Mount Lock Nut, black nylon&#160;</td>
			<td>Cole Parmer</td>
			<td>EW-45509-04</td>
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			<td>Female Luer Thread Style Panel Mount to 200 Series Barb&#160; 1/16inch&#160;</td>
			<td>Cole Parmer</td>
			<td>EW-45500-30</td>
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			<td>Female Luer Thread Style Panel Mount to 200 Series Barb, 1/8inch</td>
			<td>Cole Parmer</td>
			<td>EW-45500-34</td>
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			<td>Male Luer Integral Lock Ring to 500 Series Barb, 1/16inch ID tubing</td>
			<td>Cole Parmer</td>
			<td>EW-45505-31</td>
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			<td>Male Luer with Lock Ring x Female Luer Coupler</td>
			<td>Cole Parmer</td>
			<td>T-45508-80</td>
			<td></td>
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			<td>Barbed Bulkhead Fittings 1/4inch OD&#160;</td>
			<td>Fisher Scientific</td>
			<td>6149-0002</td>
			<td></td>
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			<td>Sanipure Tubing 1/16inch ID x 1/8inch OD&#160;</td>
			<td>SaniPure</td>
			<td>AR400002</td>
			<td></td>
		</tr>
		<tr>
			<td>Sanipure Tubing 1/4inch OD x 1/8inch ID&#160;</td>
			<td>SaniPure</td>
			<td>AR400007</td>
			<td></td>
		</tr>
		<tr>
			<td>Variable Flow Mini Pump (Peristaltic pump)</td>
			<td>Fisher Scientific</td>
			<td>13-876-1</td>
			<td></td>
		</tr>
		<tr>
			<td>Magnetic Stirrer</td>
			<td>Velp Scientifica</td>
			<td>F203A0160</td>
			<td></td>
		</tr>
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			<td>Forceps</td>
			<td>Fisher Scientific</td>
			<td>14-512-141</td>
			<td>&#160;Millipore* Filter Forceps&#160;</td>
		</tr>
		<tr>
			<td>Multi-plate spectrophotometer plate reader</td>
			<td>Molecular Devices Analyst GT</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Filter manifold</td>
			<td>Fisher Scientific</td>
			<td>&#160;XX10 025 02</td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Software:</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Python version 2.7.5</td>
			<td></td>
			<td></td>
			<td><a href="http://www.python.org/">http://www.python.org/</a></td>
		</tr>
		<tr>
			<td>PyLab module</td>
			<td></td>
			<td></td>
			<td><a href="http://wiki.scipy.org/PyLab">http://wiki.scipy.org/PyLab</a></td>
		</tr>
		<tr>
			<td>R version 3.0.1&#160;</td>
			<td></td>
			<td></td>
			<td><a href="http://www.r-project.org/">http://www.r-project.org/</a></td>
		</tr>
		<tr>
			<td>reshape2 library</td>
			<td></td>
			<td></td>
			<td><a href="http://had.co.nz/reshape">http://had.co.nz/reshape</a></td>
		</tr>
		<tr>
			<td>ggplot2 library</td>
			<td></td>
			<td></td>
			<td><a href="http://ggplot2.org/">http://ggplot2.org/</a></td>
		</tr>
		<tr>
			<td>Gene Composer</td>
			<td>PSI Tech Portal</td>
			<td></td>
			<td>http://www.genecomposer.net</td>
		</tr>
		<tr>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Services:</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>West Coast Metabolomics Center</td>
			<td>UC Davis</td>
			<td></td>
			<td><a href="http://metabolomics.ucdavis.edu/">http://metabolomics.ucdavis.edu</a></td>
		</tr>
		<tr>
			<td>DNA 2.0</td>
			<td></td>
			<td></td>
			<td>https://www.dna20.com</td>
		</tr>
	</tbody>
</table>

phage phenomics: physiological approaches to characterize novel viral proteins

Current investigations into phage-host interactions are dependent on extrapolating knowledge from (meta)genomes. Interestingly, 60 - 95% of all phage sequences share no homology to current annotated proteins. As a result, a large proportion of phage genes are annotated as hypothetical. This reality heavily affects the annotation of both structural and auxiliary metabolic genes. Here we present phenomic methods designed to capture the physiological response(s) of a selected host during expression of one of these unknown phage genes. Multi-phenotype Assay Plates (MAPs) are used to monitor the diversity of host substrate utilization and subsequent biomass formation, while metabolomics provides bi-product analysis by monitoring metabolite abundance and diversity. Both tools are used simultaneously to provide a phenotypic profile associated with expression of a single putative phage open reading frame (ORF). Representative results for both methods are compared, highlighting the phenotypic profile differences of a host carrying either putative structural or metabolic phage genes. In addition, the visualization techniques and high throughput computational pipelines that facilitated experimental analysis are presented.

Watch this Scientific Journal Video about Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins at JoVE.com

Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins

Here, we present phenomic approaches for the functional characterization of putative phage genes. Techniques include a developed assay capable of monitoring host anabolic metabolism, the Multi-phenotype Assay Plates (MAPs), in addition to the established method of metabolomics, capable of measuring effects to catabolic metabolism.

Current investigations into phage-host interactions are dependent on extrapolating knowledge from (meta)genomes. Interestingly, 60 - 95% of all phage ...

phage-phenomics-physiological-approaches-to-characterize-novel-viral

Research

JoVE Journal

Immunology and Infection

12.0K Views.  San Diego State University. Here, we present phenomic approaches for the functional characterization of putative phage genes. Techniques include a developed assay capable of monitoring host anabolic metabolism, the Multi-phenotype Assay Plates (MAPs), in addition to the established method of metabolomics, capable of measuring effects to catabolic metabolism.