Galperin 2010 Understanding The Genome

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Trends Biotechnol. Author manuscript; available in PMC 2011 March 29.
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Trends Biotechnol. 2010 August ; 28(8): 398–406. doi:10.1016/j.tibtech.2010.05.006.
From complete genome sequence to

“complete“ understanding?
Michael Y. Galperin and Eugene V. Koonin

National Center for Biotechnology Information, National Library of Medicine, National Institutes of
Health, Bethesda, Maryland 20894, USA
Abstract
The rapidly accumulating genome sequence data allow researchers to address fundamental
biological questions that were not even asked just a few years ago. A major problem in genomics
is the widening gap between the rapid progress in genome sequencing and the comparatively slow
progress in the functional characterization of sequenced genomes. Here we discuss two key
questions of genome biology: whether we need more genomes, and how deep is our understanding
of biology based on genomic analysis. We argue that overly specific annotations of gene functions
are often less useful than the more generic, but also more robust, functional assignments based on
protein family classification. We also discuss problems in understanding the functions of the
remaining “conserved hypothetical” genes.
Introduction
The year 2010 marks the 15th anniversary of the publication of the 1,830,138-base genome
of the bacterium Haemophilus influenzae Rd Kw20 - the first cellular life form to have its
entire genome sequenced [1]. Aided by the tremendous progress in sequencing technology,
genome sequencing is advancing at an ever-increasing pace. By the end of 2009, 1052
genomes representing 720 individual species (636 bacteria, 61 archaea, and 23 eukaryotes)
were completely sequenced, deposited in the public nucleotide sequence databases
(GenBank\EMBL\DDBJ) and made freely available over the internet. Many more genomes
were at various stages of sequencing and assembly, including almost 100 eukaryotic
genomes whose preliminary descriptions have been published [2]. Thanks to the advent of
the new generation of sequencing technologies, the costs of genome sequencing have
dropped so much that the projects to sequence the entire human microbiome
(http://nihroadmap.nih.gov/hmp/, [3]) and to generate ~5,000 reference genomes for every
major prokaryotic lineage (the Genomic Encyclopedia of Bacteria and Archaea:
http://www.jgi.doe.gov/programs/GEBA/, [4]) have become realistic. Given these
remarkable advances, it seems timely to address two lingering questions: ‘How many more
genomes do we need?’ and ‘How deep is our understanding of biology derived from genome
analysis?’
Don’t we already have enough genomes?

An interesting, perhaps provocative question is whether a sufficient number of genomes
have already been sequenced. Most biologists subscribe to “the more the merrier“ view [4],
but others have argued that microbial genomics has already reached the stage of diminishing
returns, such that each new genome yields information of progressively decreasing utility
Corresponding author: Galperin, M. (galperin@ncbi.nlm.nih.gov).

Galperin and Koonin Page 2
[5,6]. There seems to be some substance to this claim; for example, it is unlikely that we
ever see a single bacterial chromosome that is much longer than 13,033,779 nucleotides (as
in the myxobacterium Sorangium cellulosum). On the other end of the spectrum,
intracellular cycada symbiont Candidatus Hodgkinia cicadicola, with its 143,795-bp

genome, could be considered a cellular organelle rather than an independent organism or, at
best, a bacterium far on its way to become an organelle [7], so there is hardly any room for
further genome reduction of cellular life forms. With respect to other common parameters,
such as G+C content, the number of encoded proteins, and metabolic and signaling
complexity [8], the extremes might already have been reached, or will be in the near future.
Perhaps more importantly, the set of highly conserved genes (that is, those represented in the
majority of genomes) is clearly approaching saturation [9]. Similarly, in structural genomics
projects, the chances of discovering a new protein fold or even a new superfamily are
progressively dropping.
Nevertheless, genome sequencing is here to stay, and there are several compelling reasons
for that. First of all, the value of the sequence information is in the eye of the beholder.
Many biologists still passionately argue for sequencing their own favorite organism, strain
or isolate, no matter how many close relatives already have been sequenced. Indeed, not
having a genome sequence for an experimental model is increasingly - and for good reasons
- perceived as being stuck in the "dark ages". The availability of the genome sequence
allows researchers to easily clone and express any gene, create microarrays to analyze gene
expression, and reconstruct the metabolic and signaling networks. Having genomic
sequences from closely related organisms opens the door to the quantitative study of
mutational patterns, selective regimes, adaptations to ecological factors and, in the case of
microbial pathogens, virulence determinants. Potentially even more important is the
possibility to identify genes and traits that are not present in the given genome - a task that
clearly requires a complete genome sequence.
Secondly, the available genome collection, despite its rapid expansion, still barely scratches
the surface of the real biological diversity. The availability of genomic data already led to a
revolution in systematics, especially with regard to bacteria and archaea, having put this
field on a solid evolutionary footing and giving rise to the new discipline of phylogenomics
[10,11]. Still, judging from the metagenomic data, as many as 90% of the microbial species
on Earth remain uncultivated [12,13], which complicates reconstruction of the global carbon
and nitrogen cycles. Genome analysis has already led to several important advances in these
areas. Thus, the genome of the marine α-proteobacterium SAR11 (now renamed Candidatus
Pelagibacter ubique), apparently the most abundant organism on this planet, opened our
eyes to a peculiar role of bacteriorhodopsin-mediated photosynthesis as an auxiliary energy
source in the extremely streamlined metabolism of this bacterium [14]. The genome
sequence of the deep-sea proteobacterium Idiomarina loihiensis revealed mostly proteolytic,

in contrast to the expected saccharolytic, metabolism [15], indicating that the marine habitat
of this bacterium contains enough dissolved protein to support a peptide-based diet. The
genomes of recently discovered anammox bacteria have yielded valuable insights into the
evolution of the global nitrogen cycle and the biochemical reactions that convert nitrate and
nitrite into nitrogen gas [16]. This list of unexpected discoveries with biogeochemical
implications could be easily extended.
Thirdly, hidden sampling biases in genome sequencing are becoming apparent. For example,
starting with Mycoplasma genitalium in 1995, more than 20 mollicute genomes have been
sequenced, none of which encoded a single environmental sensor [17]. However, the
perception that mollicutes have no signal transduction systems was shattered upon the
completion of the (slightly larger) genome of the soil mollicute Acholeplasma laidlawii,
which encodes two sensory histidine kinases, three response regulators, an adenylate

cyclase, and at least 15 proteins involved in c-di-GMP-mediated regulation

(http://www.ncbi.nlm.nih.gov/Complete_Genomes/SignalCensus.html).
Fourthly, although obtaining complete genome sequences from every major lineage [4]
would certainly be a dramatic step forward, a single representative genome is by no means
sufficient to assess the true biological diversity of a taxon. As a case in point, the sequencing
of several genomes from the cyanobacterium Prochlorococcus marinus - a widespread
inhabitant of ocean surface waters - was originally aimed at establishing the principal
differences between “high-light” and “low-light” ecotypes [18]. However, different strains
of P. marinus proved to have vastly different gene repertoires, indicative of high rates of
gene acquisition and loss by these organisms. These findings have shown that: (i) the core
set of genes shared by all P. marinus isolates is very limited – and shrinking; and (ii) the P.
marinus pan-genome, that is the sum total of genes represented in at least one P. marinus
strain, is extremely large – and expanding [19]. This crucial yet unexpected development
puts into question the very rationale for assigning organisms with dramatically different
genome contents – but (nearly) identical 16S rRNA sequences – to the same “species” (such
as P. marinus or Escherichia coli) and puts the study of pan-genomes to the forefront of
genomic research.
Finally, there remains the crucial issue of using genome sequencing to improve human
health. For obvious reasons, the first sequenced genomes were mostly those of common
bacterial pathogens. Then the human genome and representative genomes from popular
model organisms emerged. As sequencing costs continue to decrease, the use of genomic
data for fighting disease becomes more and more attractive. For many bacterial pathogens,
multiple strains have been sequenced, often providing clues to the virulence factors, host
specificity and drug resistance. Some biologists advocate developing a system of constant
genome-based monitoring of various points on the globe, hoping to catch new emerging
pathogens before they cause a new epidemic. Such an effort is already well underway for
influenza viruses [20,21]. The human cancer genome projects aims at sequencing thousands
of tissue samples from various tumors, in hopes of delineating the whole spectrum of
mutations that could contribute to cancer [22]. Although this approach has been criticized
[6], the perspective of obtaining the full list of potentially oncogenic mutations – thereby
achieving a “complete understanding” of the causes of cancer – is certainly too attractive to
pass.
What part of the genome do you not understand?

With sequenced genomes being released almost every day, how well do we understand the
functions of the genes in each new one? The answer to this question will depend on the
exact meaning of the word “understanding” (as well as “function”). Modern dictionaries
associate “understanding” with such terms as “appreciation”, “comprehension”,
“explanation”, “insight”, “interpretation”, “knowledge”, and “mastery”. Accordingly,
understanding a genome starts from the “knowledge” of the nucleotide sequence and the
sequences of encoded proteins and RNAs, and includes “interpretation” of their functions,
“insight” into their complex interactions, and “explanation” of the evolutionary history that
shaped each particular genome. This leads to the “comprehension” of the potential activity
of each component of the cell, which must be tempered by the “appreciation” that proteins
often have additional (e.g. moonlighting [23,24]) function. Finally, this understanding can
be extended into “mastery” – the ability to modify the genome for certain (e.g.
biotechnological) applications. Therefore, the problem of understanding the genome can be
rephrased as follows: how good is the “parts list” that is compiled for each genome in the
form of functional annotation of the predicted protein-coding and RNA-coding genes?

Obviously, this list is never complete. Almost 10 years ago, Peer Bork described the “70%
hurdle”: on average, for approximately one-third of the genes in any given genome, the
functions could not be predicted through traditional methods of genome analysis; perhaps
even worse, the accuracy of functional prediction was only ~70% for the remaining genes
[25]. Bork warned that hopes to cross this 70% barrier and achieve a better understanding of
the functional content of genomes with the help of high-throughput analytical methods
would be tempered by the fact that these methods themselves have high error rates and are
most effective when used in concert [25]. Looking back, Bork’s sobering prediction was
right on target. High-throughput analyses of gene and protein expression, protein-protein
interactions, and ligand binding led to a dramatic increase in the amount of data pertaining
to any given gene in model genomes [26]. However, as illustrated in Box 1, accumulation of
such data does not necessarily translate into clarity regarding gene function, at least not
immediately, and not without much work.
Another important issue here is the definition of “function”, particularly as it applies to

(semi)-automated genome annotations. For a limited set of essential genes, the notion of
function seems quite straightforward: the function is what the gene product needs to do to
allow cell growth. Operationally, if a gene is knocked-out, the cell dies, and the cause of
death can be assumed to be the function of the gene in question. For non-essential genes the
picture is more complicated as the functions of many, if not most, proteins are inherently
multifaceted and complicated. For example, a single oxidoreductase would use a range of
substrates and a variety of electron acceptors, making a precise functional assignment

difficult, if not impossible. Should the function be assigned on the basis of the substrate with
the lowest KM or the highest Vmax, or the one that is most likely to be physiologically
relevant? This problem becomes particularly severe for high-throughput enzyme assays,
which helped assign general biochemical functions to products of previously
uncharacterized genes, but were often unable to pinpoint the natural substrates for the
respective enzymes [27,28]. Furthermore, many proteins, particularly in eukaryotes, lack
any (known) enzymatic activity and appear to function exclusively in protein-protein
interactions. It could be argued that the “understanding” of a protein function should, at the
very least, include knowledge of (i) biochemical activity, if any (i.e. the nature of the
catalyzed reaction and the range of utilized substrates and products); (ii) the biological
process (pathway, stress response, cell cycle) for which this activity is (most) important; and
(iii) the evolutionary aspects, such as phyletic distribution, level of sequence conservation,
and frequency of mutation, gene loss and/or non-orthologous gene displacement.
Owing to the paucity of experimental data, this information is rarely available in its entirety,
and functional assignments for the majority of the genes are based solely on the sequence
similarity of their products to experimentally characterized proteins in a handful of model
organisms such as E. coli, Bacillus subtilis, yeast, Dictyostelium, Drosophila,

Caenorhabditis elegans, zebrafish, or mouse. Automatic transfer of functional annotation
often leads to confusion when, for example, the product of a widespread prokaryotic gene
(ytaB in B. subtilis) is often annotated as “mitochondrial benzodiazepine receptor” (it is hard
to imagine why B. subtilis and hundreds of other bacteria and archaea would need a receptor
for Valium, even apart from the fact that they have no mitochondria). Alternative
annotations for the products of this gene family in various organisms include “tryptophan-
rich sensory protein TspO”, “carotenoid biosynthetic protein CrtK” and “18 kD translocator
protein”. However, despite these discordant annotations, there is little doubt that all
members of the TspO/MBR protein family (Pfam family PF03073 [29]) are very similar and
perform closely related – and important – functions, which remain to be uncovered [30]. In
our opinion, a more productive route towards sensible functional annotation is to replace
annotation of individual proteins (particularly, those from poorly studied organisms) with
annotation of protein families. In essence, this approach substitutes protein classification

(something that we generally know how to do) for specific protein annotation, which except
possibly for a handful of obvious cases, will remain questionable until each protein is
experimentally characterized, even when predictions appear entirely plausible and supported
by high similarity to experimentally characterized homologs and/or operon structure. As

experimental assays increasingly lag behind the avalanche of genomic data, such
experimental validation of predicted protein function becomes progressively less likely. By
contrast, protein family classification is becoming increasingly robust. As an example,
recognition of a Fis-type or a winged helix-turn-helix domain allows the recognition of a
protein as a DNA-binding transcriptional regulator although the regulated genes (operons)
may be difficult to predict [31]. Likewise, numerous membrane proteins are reliably
recognized as transporters, whereas the range of their substrates often remains uncertain.
The notable success of protein family databases, such as Pfam, InterPro, COGs and CDD
[29,32–34], is probably due not only to the fact that these were – and still are –
comprehensive collections with many useful features. It could be argued that another key to
their success lies in the abandonment of the elusive goal of annotating every single sequence
and instead concentrating on the common traits of protein families. In doing so, these
databases provided a reasonably robust common framework for annotating the entire protein
sets encoded in newly sequenced genomes. Thus, annotation pipelines used at most genome
sequencing projects now include comparison against at least one of the available protein
family databases.
It is important to note that family assignment is only the first step towards understanding,
which, as discussed above, requires knowledge of both the biochemical activity of the
protein and the cellular process in which the protein is involved. As an example, the
sequence-based prediction that the conserved bacterial protein Era is a GTPase was a good
first step in its characterization, and recognition of its involvement in translation was another
step forward. However, “true understanding” of the role of this GTPase in the translation
process – and its proper functional annotation – came only after an experimental study that
revealed the participation of Era in processing and maturation of 16S rRNA [35].
“Conserved hypothetical” and “putative uncharacterized” genes: when and

how will their functions become known?
Even in the relatively well-studied model organisms, the great majority of genes have never
been experimentally characterized. E. coli K-12 and yeast Saccharomyces cerevisiae appear
to be the only organisms for which at least 50% of the genes have been studied
experimentally [36–38]. Despite the best efforts of experimental and computational
biologists, a substantial – and constantly growing, given the acceleration of genome
sequencing – number of deduced proteins have no known function (Figure 1). This is hardly
surprising in case of lineage-specific genes that are found, for example, only in Vibrio or
Burkholderia - bacterial lineages that are extensively sampled by genome sequencing, but do
not include well-characterized model organisms. However, some genes that are widespread
among bacteria, archaea and/or eukaryotes still remain without functional annotation [39].
The protein products of these genes have been variously referred to as “hypothetical”,
“conserved hypothetical”, “uncharacterized” or even “putative uncharacterized” (as of May
1, 2010, 3,118,564 proteins in UniProt were annotated this way [40]). Several lists of
“conserved hypothetical” proteins have been compiled, including Domains of Unknown
Function (DUFs) in Pfam, R- and S-COGs in the COG database, and Uncharacterized
Protein Families (UPFs) in UniProtKB\Swiss-Prot [29,33,40]. These lists have been
extensively used to guide structural genomics efforts, which resulted in structural (albeit
usually not functional) characterization of many such proteins [41,42].

To highlight the distinction between the “hypothetical” genes whose functions remained
completely unknown and those that could be assigned a general biochemical function (e.g. a
methyltransferase, an oxidoreductase, a transcriptional regulator or a membrane transporter),
we denoted the former category of genes “unknown unknown” and the latter category
“known unknown” [39]. The “known unknown” category includes also genes of unknown
biochemical function that have (partially) known cellular function, such as a “cell division
protein” or a “stress response protein”. In purely operational terms, there are more or less
clear ways of establishing function for “known unknown” genes, but not for “unknown
unknowns”.
Six years ago we analyzed widely conserved “hypothetical” genes and compiled the “top
10” lists of “known unknown” and “unknown unknown” genes [39]. A re-examination of
these lists shows that, despite mounting observations, nearly half of those genes still remain
without an assigned function (Tables 1 and 2). Some of the genes in the two lists have been
experimentally characterized, and in a few cases the function has been established [43]. In
eukaryotes, products of some, albeit not all, of these widely conserved genes appear to be
targeted to mitochondria [44–48]. In two instances, mutations in these genes were linked to
mitochondrial diseases, such as hereditary paraganglioma [44] and the late-onset Leigh
syndrome [48]. In other cases, however, experimental results were contradictory (Box 1).
Apparently, the problem was not in the lack of effort to characterize these genes, but in the
pleiotropic phenotypes of their mutations, which made it difficult to pinpoint the primary
function.
What could be the functions of all those “hypothetical” genes?

Given that universally conserved genes are typically involved in translation, transcription or
ribosome biogenesis [9], widespread genes are likely to function in these or related
processes as well. Indeed, several recently characterized “conserved hypothetical” genes are
involved in post-transcriptional modification of tRNA [43,49]. Besides, a significant number
of “orphan” enzyme activities have not been associated with any protein sequence [50],
suggesting that some “hypothetical” genes might have well-known functions.
Characterization of the “missing” genes in various metabolic pathways allowed assigning
functions to a number of formerly “conserved hypothetical” genes [51,52]}.
Less common “hypothetical” genes are far more abundant in the genomes of free-living
organisms than in the relatively streamlined genomes of parasites, symbionts and
saprophytes [53]. Based on the observation that the fraction of metabolic and particularly
regulatory genes increases with the genome size [17,54,55], sophisticated regulation of gene
expression and complex (secondary) metabolism, including various post-transcriptional and
post-translational modifications, appear to be plausible roles for a fair number of the

remaining uncharacterized genes.
Recent studies have highlighted an additional class of functions that might account for the
abundance of uncharacterized genes in free-living organisms, namely, detoxification
(usually hydrolysis) of potentially hazardous side-products of various metabolic reactions
[56]. These activities, commonly referred to as “house-cleaning”, are particularly important
for aerobic organisms that have to cope with spontaneous oxidation of nucleotides, amino
acids, lipids, and other cellular components. For example, the recently characterized
“conserved hypothetical” gene yebR (renamed msrC) has been shown to encode an enzyme
that hydrolyzes methionine-(R)-sulfoxide, a product of methionine oxidation [57]. Other
cellular reactions that might require house-cleaning include methylation, acetylation and
adenylation, among potentially many others. It is probably no coincidence that many poorly
characterized proteins appear to function as hydrolases [27,28].

Finally, it has to be kept in mind that a considerable fraction of genes in many genomes
might not have definable cellular functions, but rather originate from viruses and mobile
elements and only transiently pass through microbial genomes. Genomes are highly
dynamic entities, and each sequence is a temporal snapshot that is likely to include many
short-lived elements that are not maintained by selection. The very notion of annotation for
such “selfish” genes is different from that applied to “regular” genes with distinct cellular
functions [9].
Concluding remarks
In conclusion, it might be worthwhile to make several basic generalizations regarding
genomes and the understanding of gene functions:
• Functions of many widespread genes are known; all universal genes are involved in
translation [9]
• Widespread genes with unknown functions remain uncharacterized for a reason:
they often affect multiple processes and their mutations typically are pleiotropic
(Box 1)
• The functions of a substantial fraction of genes in each sequenced genome remain
unknown
• Not every experiment on an unknown gene yields useful clues regarding function.
• Structural characterization of a protein rarely gives direct clues to its function
[41,42,58]).
• Analysis of gene expression rarely gives direct clues to gene functions
• Delineation of a protein interaction network involving the gene of interest rarely
gives direct clues to its function [26,59,60]
• Functional assignments for previously uncharacterized, widely conserved genes are
just like any biological discoveries: they require a lot of hard work and a bit of luck
So far there is no single high-throughput approach that would finally reveal the functions of
all “hypothetical” genes encoded in the sequenced genomes. This goal may be reachable
only through sustained efforts of numerous experimental, computational and structural
biologists [61]. At the end of 2009, NIH awarded a grant to the COMputational BRidge to
EXperiments (COMBREX, http://www.combrex.org/, formerly SciBay) consortium project
that aims to coordinate collaborative efforts of various research groups towards
computational identification of the most interesting families of “conserved hypothetical”
proteins and their experimental characterization
(http://www.nigms.nih.gov/News/Results/gogrant_112309.htm). This project seems to have

considerable potential to accelerate functional characterization of the remaining
“hypothetical” genes, thereby bringing us closer to the “complete understanding” of
genomes – and the organisms themselves.
Box 1. The long and circuitous path from experiment to “understanding”

Many “conserved hypothetical” genes remain without an assigned function simply
because they have never been studied experimentally. In other cases, experimental
studies brought contradictory results that could not be easily reconciled. To illustrate the
difficulty of, in the words of Sydney Brenner, "converting data into knowledge and
knowledge into understanding", let us consider the history of functional characterization
of three widespread genes.
YgjD/Gcp/Kae1/QRI1/OSGEPL family

The E. coli ygjD (gcp) gene has orthologs in almost every bacterial, archaeal and
eukaryotic genome. In many eukaryotes it is found in two paralogous copies, such as
QRI7 and Kae1 in yeast, At4g22720 and At2g45270 in Arabidopsis thaliana, and
OSGEPL and OSGEPL1 in human. In addition, there is a family of more distant bacterial
paralogs, represented by E. coli YeaZ and B. subtilis YdiC. We have previously
discussed the potential functions of this protein family (which contains an actin/HSP70
superfamily ATPase domain), and expressed doubts about its annotation as "O-
sialoglycoprotease", which was based on a single experimental observation, and further
suggested an association of this protein with translation (e.g. co-translational degradation
of misfolded proteins) [39]. In the past several years, proteins of this family have been
studied in several model organisms, and the crystal structures of several family members
have been solved [46,59]. An archaeal YgjD family member showed no protease activity,
but has been reported to bind DNA and possess an apurinic endonuclease activity [62]. In
yeast, Kae1 is a subunit of the KEOPS complex which regulates transcription, telomere
uncapping and telomere length, and is required for cell growth; this protein is targeted to
mitochondria and appears to be essential for genome maintenance [46]. Despite all these
observations, the actual function of the YgjD family proteins remains enigmatic [46,60].
A recent study suggested their involvement in biosynthesis of
threonylcarbamoyladenosine (t6A), a universal tRNA base modification occurring at
position 37 in a subset of tRNAs decoding the ANN codons [63]. If so, translational
defects resulting from impaired t6A biosynthesis could explain at least some properties of
the ygjD mutants.

YebC/YrbC/DUF28/UPF0082 family
The E. coli yebC is another widespread gene with orthologs in most bacterial and
eukaryotic genomes. In some organisms, there are two paralogs, such as yebC and yeeN
in E. coli, or yeeI and yrbC in B. subtilis. Products of these genes are listed as “domain of
unknown function” (DUF28, PF01709) in the Pfam database [29] and as uncharacterized
protein family UPF0082 in UniProt [40]. Crystal structures of three members of this
family have been solved [64], but those structures have provided no clear indication of
protein function. Analysis of the genome neighborhoods of the yebC-like genes has
revealed potential association with the Holliday junction resolvase RuvABC and
suggested that YebC might be involved in DNA recombination and repair in bacteria and
mitochondria [39]. Indeed, a recent study demonstrated DNA binding by a Pseudomonas
aeruginosa YebC family protein, and suggested a role in transcription regulation [65].
However, another study [48] mapped the cytochrome c oxidase deficiency in humans
(late-onset Leigh syndrome) to a mutation in the human YebC ortholog CCDC44
(renamed TACO1) and concluded that this protein was required for the proper translation
of the mitochondrial COX1 protein. Three possible mechanisms of YebC action to ensure
translation of the full-length COX1 polypeptide were considered: (i) securing an accurate
start of translation, (ii) stabilizing the elongating polypeptide, and (iii) interacting with
the peptide release factor [48]. While involvement in translation appears very likely for
such a widespread protein family, its apparent capacity to bind DNA remains to be
confirmed and/or explained.
YjgF/YabJ/YER057c/UK114 family
The E. coli yjgF gene has highly conserved homologs in bacteria, archaea and
eukaryotes, often with multiple paralogs in the same genome. Representatives of the
YjgF protein family are known as "purine regulatory protein YabJ" in B. subtilis and as
"tumour-associated antigen UK114" in human and other mammals. Members of this
family have been reported to possess ribonuclease activity, to function as a molecular
chaperone, calpain activator, transcriptional regulator, and translational inhibitor, and
also to affect photosynthesis, isoleucine biosynthesis and mitochondrial genome

maintenance (reviewed in [66,67]). Crystal structures of bacterial, archaeal and

eukaryotic members of this family have been solved, revealing an inter-subunit cleft that
is capable of binding a variety of small molecule ligands [58]. Despite all these efforts,
the cellular functions of the members of the YjgF family remain unclear.
Acknowledgments
This study was supported by the Intramural Research Program of the National Library of Medicine at the U.S.
National Institutes of Health.
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Figure 1.
Accumulation of protein sequences of unknown function in the genome databases. Open
symbols indicate the total number of protein sequences encoded in prokaryotic (blue) and
eukaryotic (red) genomes; filled symbols indicate the number of “hypothetical” or
“uncharacterized” proteins. The data are taken from the NCBI’s RefSeq database [68]; the
numbers for 2010 are extrapolated from the first 4 months.

Table 1
Updated “top 10” list of widespread “known unknown” genes
Gene name Protein family PDB Initial predictions (2004) Updated functional
entry annotation, reference
E. coli Yeast Human Pfam COG
ygjD QRI7 OSGEP PF00814 0533 2VWB Putative metal- and ATP- DNA binding protein with
dependent protease. Fused to a apurinic endonuclease activity
Ser/Thr protein kinase domain [62];
in some archaea. Gene threonylcarbamoyladenosine
neighborhoods suggest biosynthesis in tRNA [63]
association with translation
ychF YBR025c PTD004 PF06071 0012 1JAL Predicted GTPase; binds An ATPase in the GTPase
double-stranded RNA; family [69]
coexpressed with peptidyl-
tRNA hydrolase; predicted to
be a translation factor
yrdC SUA5 YRDC PF01300 PF03481 0009 1HRU Double-stranded RNA binding Ribosome maturation factor
protein, predicted translation RimN [70];
initiation factor; induced by threonylcarbamoyladenosine
ischemia in humans biosynthesis in tRNA [43]
ybeM NIT2 NIT1 PF00795 0388 1EMS A member of nitrilase Omega-amidodicarboxylate
superfamily, predicted amidohydrolase activity [71]
amidase. Some members
might function as glutaminase
subunits of NAD synthetase.

In worm and fly fused to Fhit
domain (diadenosine
triphosphatase), a potential
tumor suppressor
yihA YDR336w HSPC135 PF01926 0218 1PUI A GTP-binding protein Crucial for ribosome assembly
required for E. coli cell or stability in Staphylococcus
division; co-occurrence with aureus [72]
the gene for ClpP protease in
several genomes suggests
involvement in regulated
(perhaps co-translational)
protein degradation
yigB YMR130c C9orf158 PF00702 1011 1NRW A phosphatase of haloacid Flavin mononucleotide (FMN)
dehalogenase (HAD) phosphatase activity [28]
superfamily; adjacency to the
XerC DNA recombinase gene
suggests a role in DNA
recombination and/or repair
yfhR YNL320w BEM46 PF00561 1073 2WTM Predicted enzyme of the alpha/ Important for cell polarity [73]
beta hydrolase fold, most
likely an esterase; possible
role in yeast budding
yfcE VPS29 PEP11 PF00149 0622 1SU1 A phosphoesterase of the A phosphodiesterase with
calcineurin-like superfamily; variable activity against 2',3'-
vacuolar sorting protein in cAMP [74,75]
yeast. Gene neighborhood is
compatible with a role in RNA
metabolism
- NUG1 GNL3 PF01926 1161 1PUJ Predicted GTPase; genome No news [76]
context suggests possible
involvement in translation. In
yeast, required for nuclear
export of 60S pre-ribosomal
particles. In humans, nucleolar
protein, important for cell
proliferation
yhcM AFG1 LACE1 PF03969 1485 n/a Predicted ATPase, in Promotes degradation of
eukaryotes localized to the cytochrome c oxidase
mitochondria

Gene name Protein family PDB Initial predictions (2004) Updated functional
entry annotation, reference
mitochondrially encoded
subunits [45]
Modified from Table 2 from Ref [39] with permission from Oxford University Press. Additional information on the listed gene products is
available from the respective online resources: for Pfam [29], in the http://pfam.sanger.ac.uk/family?PF00814 format; for COGs [33], in the
http://www.ncbi.nlm.nih.gov/COG/grace/wiew.cgi?COG0533 format; for Protein DataBank (PDB), in the
http://www.rcsb.org/pdb/cgi/explore.cgi?pdbId=2VWB format.
Abbreviations: n/a, not available; COG, Clusters of Orthologous Groups of proteins database.

Table 2
Updated “top 10” list of widespread “unknown unknown” genes
Gene name Protein family PDB Initial predictions (2004) Updated annotation
entry
yebC YGR021w PRO0477 PF01709 0217 1KON Often encoded in the same operon DNA-binding transcriptional
with Holliday junction resolvasome regulator [65]; translational
(RuvABC) subunits. However, also activator of COX I; mutation
found in eukaryotes (mitochondrial causes cytochrome c oxidase
protein) whose resolvases are deficiency and late-onset Leigh
unrelated to RuvABC. Potential role syndrome [48]
in DNA repair and/or recombination
ybgI NIF3 NIF3L1 PF01784 0327 1NMO In yeast, interacts with transcriptional Mitochondrial localization
coactivator NGG1p. Could be a
transcriptional regulator
ybeB - C7orf30 PF02410 0799 2ID1 Homologs of plant protein Iojap, Co-migrates with the 50S
required for normal function of subunit [77]; NAD-dependent
chloroplast ribosomes. In most nucleic acid AMP ligase,
bacteria, adjacent to the gene for releases NMN from NAD (V.
nicotinic acid mononucleotide de Crecy-Lagard, pers.
adenylyltransferase, suggesting a role commun).
in NAD metabolism and/or bacterial
cell division
yjeF YNL200c AIBP PF03853 0062 1JZT In many prokaryotes, fused to a sugar Mitochondrial localization,
kinase domain. In plants, fused to a role in spermiogenesis and

pyridoxamine 5-phosphate oxidase- oogenesis [47]
like domain. In humans, is secreted
by kidney cells; binds apolipoprotein
A-I. Domain fusions suggest a role in
RNA processing
yigZ YDL177c IMPACT PF01205 1739 1VI7 Imprinted gene in mouse, but not in Binds to the translational
human, a candidate gene for bipolar activator GCN1, inhibits
affective disorder. In fungi, fused protein kinase GCN2 [78]
with UDP-glucose 4-epimerase,
suggesting that it could be an enzyme
of sugar metabolism
rtcB - HSPC117 PF01139 1690 1UC2 In several bacteria, encoded in the Binds vinculin, potential role
same operon with RNA 3'-terminal in cell adhesion [79]
phosphate cyclase RtcA, suggesting
that RtcB could be an RNA
modification enzyme
ydjX YKR088c TMEM64 PF00597 0398 n/a Predicted membrane protein, may be No news
involved in utilization of 4-
hydroxybutyrate; moderate growth
defect in yeast mutants
ygfY SDH5 PGL2 PF03937 2938 1X6I In yeast, required for sporulation and Required for insertion of flavin
for growth on respiratory substrates; into the succinate
possible transcriptional regulator dehydrogenase complex,

mutation leads to
paraganglioma [44]
- YOR289w AMMECR1 PF01871 2078 n/a In humans, absent in the Alport Predicted RNA-modifying
syndrome, mental retardation, enzyme [80]
midface hypoplasia, and
elliptocytosis
ydiU YPL222w SELO PF02696 0397 n/a Selenoprotein O, in yeast localized to No news
mitochondria; not found in archaea
Modified from Table 3 from Ref [39] with permission from Oxford University Press. Additional information on the listed gene products is
available from the respective online resources: for Pfam [29], in the http://pfam.sanger.ac.uk/family?PF00814 format; for COGs [33], in the
http://www.ncbi.nlm.nih.gov/COG/grace/wiew.cgi?COG0533 format; for Protein DataBank (PDB), in the
http://www.rcsb.org/pdb/cgi/explore.cgi?pdbId=2VWB format.

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Trends Biotechnol. 2010 August ; 28(8): 398–406. doi:10.1016/j.tibtech.2010.05.006.

From complete genome sequence to

Michael Y. Galperin and Eugene V. Koonin

Don’t we already have enough genomes?

Corresponding author: Galperin, M. (galperin@ncbi.nlm.nih.gov).

intracellular cycada symbiont Candidatus Hodgkinia cicadicola, with its 143,795-bp

sequence of the deep-sea proteobacterium Idiomarina loihiensis revealed mostly proteolytic,

Trends Biotechnol. Author manuscript; available in PMC 2011 March 29.

cyclase, and at least 15 proteins involved in c-di-GMP-mediated regulation

What part of the genome do you not understand?

Trends Biotechnol. Author manuscript; available in PMC 2011 March 29.

Another important issue here is the definition of “function”, particularly as it applies to

substrates and a variety of electron acceptors, making a precise functional assignment

organisms such as E. coli, Bacillus subtilis, yeast, Dictyostelium, Drosophila,

Trends Biotechnol. Author manuscript; available in PMC 2011 March 29.

by high similarity to experimentally characterized homologs and/or operon structure. As

“Conserved hypothetical” and “putative uncharacterized” genes: when and

Trends Biotechnol. Author manuscript; available in PMC 2011 March 29.

What could be the functions of all those “hypothetical” genes?

post-translational modifications, appear to be plausible roles for a fair number of the

Trends Biotechnol. Author manuscript; available in PMC 2011 March 29.

(http://www.nigms.nih.gov/News/Results/gogrant_112309.htm). This project seems to have

Box 1. The long and circuitous path from experiment to “understanding”

Trends Biotechnol. Author manuscript; available in PMC 2011 March 29.

the ygjD mutants.

Trends Biotechnol. Author manuscript; available in PMC 2011 March 29.

maintenance (reviewed in [66,67]). Crystal structures of bacterial, archaeal and

Trends Biotechnol. Author manuscript; available in PMC 2011 March 29.

resequencing arrays. Nucleic Acids Res. 2010; 38:e111. [PubMed: 20185568]

regulating activity. PLoS One. 2009; 4:e6096. [PubMed: 19564920]

experimental study. Nucleic Acids Res. 2004; 32:5452–5463. [PubMed: 15479782]

Trends Biotechnol. Author manuscript; available in PMC 2011 March 29.

484. [PubMed: 12957540]

which exhibits atypical DNA-binding properties and apurinic-endonuclease activity in vitro.

Trends Biotechnol. Author manuscript; available in PMC 2011 March 29.

binding proteins. J. Biol. Chem. 2007; 282:19928–19937. [PubMed: 17430889]

Trends Biotechnol. Author manuscript; available in PMC 2011 March 29.

Trends Biotechnol. Author manuscript; available in PMC 2011 March 29.

subunits of NAD synthetase.

Trends Biotechnol. Author manuscript; available in PMC 2011 March 29.

Trends Biotechnol. Author manuscript; available in PMC 2011 March 29.

kinase domain. In plants, fused to a role in spermiogenesis and

possible transcriptional regulator dehydrogenase complex,

Trends Biotechnol. Author manuscript; available in PMC 2011 March 29.

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