Evolution of yeast noncoding RNAs reveals an alternative mechanism for widespread intron loss.
ABSTRACT The evolutionary forces responsible for intron loss are unresolved. Whereas research has focused on protein-coding genes, here we analyze noncoding small nucleolar RNA (snoRNA) genes in which introns, rather than exons, are typically the functional elements. Within the yeast lineage exemplified by the human pathogen Candida albicans, we find--through deep RNA sequencing and genome-wide annotation of splice junctions--extreme compaction and loss of associated exons, but retention of snoRNAs within introns. In the Saccharomyces yeast lineage, however, we find it is the introns that have been lost through widespread degeneration of splicing signals. This intron loss, perhaps facilitated by innovations in snoRNA processing, is distinct from that observed in protein-coding genes with respect to both mechanism and evolutionary timing.
- SourceAvailable from: Philip Stevens[Show abstract] [Hide abstract]
ABSTRACT: BACKGROUND: Although Candida albicans and Candida dubliniensis are most closely related, both species behave significantly different with respect to morphogenesis and virulence. In order to gain further insight into the divergent routes for morphogenetic adaptation in both species, we investigated qualitative along with quantitative differences in the transcriptomes of both organisms by cDNA deep sequencing. RESULTS: Following genome-associated assembly of sequence reads we were able to generate experimentally verified databases containing 6016 and 5972 genes for C. albicans and C. dubliniensis, respectively. About 95% of the transcriptionally active regions (TARs) contain open reading frames while the remaining TARs most likely represent non-coding RNAs. Comparison of our annotations with publically available gene models for C. albicans and C. dubliniensis confirmed approximately 95% of already predicted genes, but also revealed so far unknown novel TARs in both species. Qualitative cross-species analysis of these databases revealed in addition to 5802 orthologs also 399 and 49 species-specific protein coding genes for C. albicans and C. dubliniensis, respectively. Furthermore, quantitative transcriptional profiling using RNA-Seq revealed significant differences in the expression of orthologs across both species. We defined a core subset of 84 hyphal-specific genes required for both species, as well as a set of 42 genes that seem to be specifically induced during hyphal morphogenesis in C. albicans. CONCLUSIONS: Species-specific adaptation in C. albicans and C. dubliniensis is governed by individual genetic repertoires but also by altered regulation of conserved orthologs on the transcriptional level.BMC Genomics 04/2013; 14(1):212. · 4.04 Impact Factor
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ABSTRACT: Candida albicans is an opportunistic human fungal pathogen that causes candidiasis. As healthcare has been improved worldwide, the number of immunocompromised patients has been increased to a greater extent and they are highly susceptible to various pathogenic microbes and C. albicans has been prominent among the fungal pathogens. The complete genome sequence of this pathogen is now available and has been extremely useful for the identification of repertoire of genes present in this pathogen. The major challenge is now to assign the functions to these genes of which 13% are specific to C. albicans. Due to its close relationship with yeast Saccharomyces cerevisiae, an edge over other fungal pathogens because most of the technologies can be directly transferred to C. albicans from S. cerevisiae and it is amenable to mutation, gene disruption, and transformation. The last two decades have witnessed enormous amount of research activities on this pathogen that leads to the understanding of host-parasite interaction, infections, and disease propagation. Clearly, C. albicans has emerged as a model organism for studying fungal pathogens along with other two fungi Aspergillus fumigatus and Cryptococcus neoformans. Understanding its complete life style of C. albicans will undoubtedly be useful for developing potential antifungal drugs and tackling Candida infections. This will also shed light on the functioning of other fungal pathogens.ISRN microbiology. 01/2012; 2012:538694.
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ABSTRACT: The selectivity with which a biomolecule can bind its cognate ligand when confronted by the vast array of structurally similar, competing ligands that are present in the cell underlies the fidelity of some of the most fundamental processes in biology. Because they collectively comprise one of only a few methods that can sensitively detect the 'encounter' complexes and subsequent intermediate states that regulate the selectivity of ligand binding, single-molecule fluorescence, and particularly single-molecule fluorescence resonance energy transfer (smFRET), approaches have revolutionized studies of ligand-binding reactions. Here, we describe a widely used smFRET strategy that enables investigations of a large variety of ligand-binding reactions, and discuss two such reactions, aminoacyl-tRNA selection during translation elongation and splice site selection during spliceosome assembly, that highlight both the successes and challenges of smFRET studies of ligand-binding reactions. We conclude by reviewing a number of emerging experimental and computational approaches that are expanding the capabilities of smFRET approaches for studies of ligand-binding reactions and that promise to reveal the mechanisms that control the selectivity of ligand binding with unprecedented resolution.FEBS Letters 07/2014; 588(19). · 3.34 Impact Factor
, 838 (2010);
, et al.Quinn M. Mitrovich
Mechanism for Widespread Intron Loss
Evolution of Yeast Noncoding RNAs Reveals an Alternative
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difference in fluorescence signal between the
activated and kirromycin-stalled states (34).
This structure of 70S–tRNA–EF-Tu–GDPCP
provides an atomic-resolution model of a trans-
lational GTPase in its activated state. Codon rec-
in the 30S subunit, tRNA, and EF-Tu that posi-
does not require a large opening of the “hydro-
phobic gate,” but instead requires the positioning
of the catalytic histidine into the active site by the
SRL residue A2662. The high level of sequence
and structural conservation between all transla-
tional GTPases suggests that although each factor
recognizes a distinct ribosomal state, each must
bind in such a way that the SRL interacts with its
catalytic histidine. Therefore, GTPase activation
by the SRL is the universal mechanism for trig-
kingdoms of life.
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35. We thank R. Green for reagents and D. de Sanctis at
the European Synchrotron Radiation Facility for
facilitating data collection. This work was supported by
the Medical Research Council UK, the Wellcome Trust,
the Agouron Institute, and the Louis-Jeantet Foundation.
R.M.V. is supported by a Gates-Cambridge scholarship,
and T.M.S. by the Human Frontier Science Program and
Emmanuel College. V.R. is on the Scientific Advisory
Board and holds stock options in Rib-X pharmaceuticals.
Transfer of Thermus thermophilis ribosome strain T.th.
HB8-MRC-MSAW1 requires a Materials Transfer
Agreement with the MRC. Coordinates and structure
factors have been deposited at the Protein Data Bank
with accession codes 2xqd and 2xqe.
Supporting Online Material
Materials and Methods
Figs. S1 to S4
29 June 2010; accepted 3 September 2010
Evolution of Yeast Noncoding RNAs
Reveals an Alternative Mechanism
for Widespread Intron Loss
Quinn M. Mitrovich,1,2* Brian B. Tuch,1,3*† Francisco M. De La Vega,3
Christine Guthrie,2‡ Alexander D. Johnson1,2‡
The evolutionary forces responsible for intron loss are unresolved. Whereas research has focused
on protein-coding genes, here we analyze noncoding small nucleolar RNA (snoRNA) genes in
which introns, rather than exons, are typically the functional elements. Within the yeast lineage
exemplified by the human pathogen Candida albicans, we find—through deep RNA sequencing and
genome-wide annotation of splice junctions—extreme compaction and loss of associated exons, but
retention of snoRNAs within introns. In the Saccharomyces yeast lineage, however, we find it is the
introns that have been lost through widespread degeneration of splicing signals. This intron loss,
perhaps facilitated by innovations in snoRNA processing, is distinct from that observed in
protein-coding genes with respect to both mechanism and evolutionary timing.
n eukaryotes, protein-coding genes are fre-
precisely removed from RNA transcripts by
the nuclear spliceosome (1). Over evolutionary
time scales, the presence of introns is dynamic,
with intron gain and loss rates varying substan-
tially across eukaryotic lineages (2–4). The mech-
anisms of intron gain and loss speak to questions
about both the origins of introns and the markedly
different intron-exon patterns observed across eu-
karyotes (5)—for example, whether spliceosomal
introns arose within eukaryotes (“introns late”),
within an ancestor of both prokaryotes and eu-
karyotes (“introns early”), or even before the emer-
gence of protein-coding genes (“introns first”) (6).
of comprehensive intron loss within both the pro-
karyotic and archaeal lineages, whose modern
representatives lack spliceosomal introns. Within
eukaryotes, the hemiascomycetous yeasts show
substantial intron loss, with modern species like
Saccharomyces cerevisiae and Candida albicans
devoid of introns in >90% of their genes (7). A
postulated mechanism for this loss is reverse
transcription of spliced RNA, followed by homol-
containing genomic sequence with the intronless
copy (8). Previous studies of intron loss have
focused on protein-coding genes, however, and
are therefore likely to be biased toward mecha-
Fig. 1. Sequencelibrarycom-
parisons reveal noncoding
RNAs. RNA sequence data are
shown for MRPS35, a repre-
sentative protein-coding gene
intron. Nonadenylated RNAs,
such as mature snoRNAs, are
on a log2axis. (Bottom track) One of 1706 lower-confidence snoRNA predictions generated using the
snoscan algorithm (22).
1Department of Microbiology and Immunology, University of
California, San Francisco, San Francisco, CA 94143–2200, USA.
2Department of Biochemistry and Biophysics, University of Cal-
ifornia, San Francisco, San Francisco, CA 94143–2200, USA.
3Genetic Systems Division, Research and Development, Life
Technologies, Foster City, CA 94404, USA.
*These authors contributed equally to this work.
†Present address: Genome Analysis Unit, Amgen, South San
Francisco, CA 94080, USA.
‡To whom correspondence should be addressed. E-mail:
email@example.com (C.G.); firstname.lastname@example.org
5 NOVEMBER 2010 VOL 330
on January 20, 2011
nisms that lead to precise intron removal. Here,
we examine instead the evolution of splicing pat-
terns in yeast noncoding genes.
specific sequence reads to the C. albicans genome
(9). Our data confirm 89% of 421 previously an-
notated spliceosomal introns (7, 10) (table S1),
while correcting or rejecting seven of these an-
notations (table S2). We also find 68 previously
unannotated splice junctions, identifying 15 new
introns in protein-coding genes (table S3), 30
examples of alternative splicing [table S4 and
supporting online (SOM) text], and 23 new in-
ysis of 11 of these spliced, noncoding RNAs
but that their introns contain C/D box snoRNAs—
noncoding RNAs that target modifications to
ribosomal RNA (rRNA) (11). In the nonhemias-
comycetous fungus Neurospora crassa, snoRNAs
are also generally processed from the introns of
non–protein-coding precursors (12). This is differ-
ent, however, from the more closely related hemi-
arise from unspliced primary transcripts and, there-
fore, require a splicing-independent processing
pathway (13). This difference between C. albicans
and S. cerevisiae suggests that the transition of
snoRNAs from intron sequences to unspliced,
dedicatedtranscriptsoccurredwithin the Saccha-
romyces lineage, well after the onset of rapid
intron loss from protein-coding genes in the hemi-
To trace the evolution of snoRNAs through-
out the hemiascomycetes, we first generated 40
high-confidence C/D box snoRNA predictions
11 identified in our intron analysis (above), as well
and predict that 33 of the 40 identified C. albicans
snoRNAs are intronic. This confirms the difference
between C. albicans and S. cerevisiae, as C/D box
of 47 (13)].
We next identified orthologous snoRNAs from
other sequenced yeasts (Fig. 2), beginning with
computational predictions (or, for S. cerevisiae,
target sites, and finally confirming and refining
our predictions by searching for limited primary
sequence identity among predicted snoRNAs (15).
This final refinement identified snoRNAs whose
rRNA target sites had changed between species,
methylationsites (fig.S1).We predict that105 of
the 255 analyzed snoRNAs are located within in-
Those in the Saccharomyces complex have few
intronic snoRNAs (three to five), whereas others
have substantially more (23 to 33). The most par-
Fig. 2. snoRNA-associated introns were lost in the Saccharomyces lineage. (A) Intron prediction scores
sequences (T 200 nt) in seven representative hemiascomycetes. Each snoRNA (horizontal row) is labeled
according to the nomenclature of S. cerevisiae (where applicable) or N. crassa (CD39) or by the predicted
C. albicans rRNA modification site. Intron scores greater than 5.0 (false-positive rate <0.4%) are shaded
green. Inferred intron loss events for each snoRNA, based on parsimony, are indicated on the left and
correspond to labeled branches (not drawn to scale) in the phylogeny shown above and in (B). snoRNAs
that were not identified or that lack sufficient flanking sequence to score are gray and labeled NA or ND,
respectively. Locations of N. crassa orthologs (green for intronic, white for exonic) are derived from (12).
Intron scores for reverse-complements of flanking sequences (right) are provided as a negative control.
(B) Phylogenetic pattern of snoRNA-associated intron loss. The number of loss events assigned to each
branch is indicated in yellow. Species: N. crassa, Y. lipolytica, Debaryomyces hansenii, C. albicans,
Kluyveromyces lactis, Kluyveromyces waltii, Zygosaccharomyces rouxii, and S. cerevisiae.
VOL 3305 NOVEMBER 2010
on January 20, 2011
of snoRNA-associated introns, most of which took
place in the common ancestor of the Saccharomy-
ces complex (Fig. 2B).
The intron loss mechanism proposed for
protein-coding genes—retrotransposition of spliced
mRNAs—cannot explain the pattern we observe,
as it would eliminate the snoRNAs along with
the introns. Rather, the introns appear to have
been lost through degeneration of their splicing
signals, effectively converting them into unspliced
in the snR72-78 polycistronic cluster are mostly
contained within individual introns (Fig. 3A). In
S. cerevisiae, the genes are arranged identically,
but are cotranscribed as a single unspliced pre-
cursor, then processed into individual snoRNAs
by the RNase III enzyme Rnt1 (16). The conser-
vation of genomic synteny among these species
strongly suggests intron loss through splice site
degeneration (“de-intronization”) with Yarrowia
lipolytica and the Candida clade representing
an intermediate state of partial intron loss and
snR57 cluster similarly supports this idea (be-
Intron loss through splice-site degeneration
would not be expected to occur within protein-
coding genes, as this would disrupt the encoded
protein. Consistent with this prediction, of the
five snoRNAs located in introns within protein-
coding regions in C. albicans, four remain asso-
complex species [snR18, snR24, snR54, and
CD39; (Fig. 2A)]. The fifth, snR39b, was likely
displaced from its associated coding sequence
through a genomic duplication event (fig. S2B).
The snoRNA CD39 is located within the ribo-
somal protein gene MRPS35 intron in all but
one of the species we analyzed (Fig. 2A). The
its intron, presumably through retrotransposition
rather than deintronization. This appears to have
eliminated CD39 from the genome as well: The
predicted rRNA methylation site for this snoRNA
is unmodified in S. cerevisiae (13).
In C. albicans, one unusual case of splicing
may reflect the particular processing require-
ments imposed by intron-hosted snoRNAs. The
from three introns of a single precursor (Fig. 3B).
We find that two of the introns (introns 2 and 3)
lie entirely within the sequence of an enveloping
intron (intron 1). The splicing signals for intron
1 are not present in the primary transcript, but
are created upon splicing of introns 2 and 3.
Thus, splicing of intron 1 can occur only after
introns 2 and 3 have been removed. (See SOM
text for similar scenarios in protein-coding
genes of other eukaryotes.) Analysis of both the
snR57/55/61 and the snR72-78 clusters in other
species indicates a significant reduction in the
sizes of internal exons within the hemiasco-
mycetes, driven perhaps by the same pressures
ultimately to nested splicing within the Candida
C/D box snoRNAs obey a strict “one-snoRNA-
per-intron” rule, a requirement imposed by their
exonucleolytic maturation pathways (18). Nested
splicing of snoRNA host transcripts fulfills this
requirement by allowing sequential removal of
individual introns despite the absence of interven-
Selective pressures are proposed to have driven
intron loss from hemiascomycete protein-coding
genes (19), and these same pressures may have
driven the loss we observe here. The dependence
of snoRNAs on splicing for their proper matura-
tion, however, would have imposed a constraint
against loss of their associated introns (18). This
constraint may have been overcome by the inno-
way in the Saccharomyces lineage, where exonic
C/D box snoRNAs first undergo endonucleolytic
must be more ancient, because other hemiasco-
mycetes (Fig. 2) and more distantly related fungi
(12) do have some exon-hosted snoRNAs. It is
unknown, however, whether processing of such
snoRNAs involves Rnt1 (SOM text).
hemiascomycete ancestor (21). By focusing in-
stead on noncoding RNAs, we describe unex-
pected patterns of both exon and intron loss. A
drastic reduction in the sizes of internal exons has
ultimately led to their complete loss in Candida
maintains the independent removal of now over-
ever, has experienced a second wave of intron
processing and mediated by a mechanism—splice
site degeneration—distinct from that which has
acted on protein-coding genes.
Fig. 3. Polycistronic snoRNA precursors exhibit unusual splicing patterns. (A) Splicing of the snR72-78
cluster in various fungal species (phylogenetic relations on left). snoRNAs are shown in green, introns as
lines, and exons as yellow boxes, with internal exons labeled by size. Cotranscription of entire clusters has
been demonstrated only for S. cerevisiae (16) and N. crassa (12). (B) Nested splicing of the snR57/55/61
snoRNA cluster in C. albicans. The 5′ splice site, branch site, and 3′ splice site sequences are shown for
of introns 2 and 3, to create de novo intron 1 splice sites. (Inset) Reverse transcription polymerase chain
reaction products of the snoRNA host transcript from wild-type cells and those deficient for nonsense-
mediated mRNA decay (upf1-D) or nuclear exonucleolytic decay (rrp6-D). We infer the order of splicing
events from the observable products: Intron 3 is nearly always removed, intron 2 is removed in a subset of
transcripts (lower two bands), and intron 1 only when 2 and 3 have also been removed (lower band).
5 NOVEMBER 2010VOL 330
on January 20, 2011
References and Notes
1. M. C. Wahl, C. L. Will, R. Lührmann, Cell 136, 701 (2009).
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11, 378 (1999).
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13, 305 (2007).
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material on Science Online.
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23. We thank C. Barbacioru, O. Homann, S. Kuersten,
S. Ledoux, I. Listerman, T. Lowe, C. Maeder, K. Pande,
A. Price, S. Roy, J. Steitz, and members of the Guthrie
and Johnson laboratories for helpful discussions and
support; and M. Barker and C. Monighetti for providing
sequence reads. This work was supported by NIH grants
GM021119 (C.G.) and GM37049 (A.D.J.) and by Life
Technologies Corp. C.G. is an American Cancer Society
Research Professor of Molecular Genetics. Sequence data
are available at the Gene Expression Omnibus (accession
Supporting Online Material
Materials and Methods
Figs. S1 to S4
Tables S1 to S6
1 July 2010; accepted 21 September 2010
Fate Mapping Analysis Reveals
That Adult Microglia Derive from
Florent Ginhoux,1,2* Melanie Greter,1Marylene Leboeuf,1Sayan Nandi,3Peter See,2
Solen Gokhan,4Mark F. Mehler,4,5Simon J. Conway,6Lai Guan Ng,2E. Richard Stanley,3
Igor M. Samokhvalov,7Miriam Merad1*
Microglia are the resident macrophages of the central nervous system and are associated
with the pathogenesis of many neurodegenerative and brain inflammatory diseases; however,
the origin of adult microglia remains controversial. We show that postnatal hematopoietic
progenitors do not significantly contribute to microglia homeostasis in the adult brain. In
contrast to many macrophage populations, we show that microglia develop in mice that lack colony
stimulating factor-1 (CSF-1) but are absent in CSF-1 receptor–deficient mice. In vivo lineage
tracing studies established that adult microglia derive from primitive myeloid progenitors that arise
before embryonic day 8. These results identify microglia as an ontogenically distinct population in
the mononuclear phagocyte system and have implications for the use of embryonically derived
microglial progenitors for the treatment of various brain disorders.
lial progenitors (1, 2). The most consensual hy-
pothesis to date is that embryonic and perinatal
lthough microglial ontogeny is an exten-
sive area of research, much controversy
remains regarding the nature of microg-
hematopoietic waves of microglial recruitment
and differentiation occur in the central nervous
system (CNS) (1, 2). However, the exact con-
tribution of embryonic and postnatal hemato-
poietic progenitors to the adult microglial pool in
the steady state remains unclear. We examined
topoiesis to the adult microglial population that
populates the CNS during normal development.
Our results provide direct evidence that adult
microglia derive from primitive myeloid progen-
myeloid progenitors to an adult hematopoietic
To address the contribution of perinatal cir-
culating hematopoietic precursors to microglial
homeostasis, we reconstituted sublethally irra-
diated C57BL/6 CD45.2+newborns with hema-
topoietic cells isolated from CD45.1+congenic
mice (3). Although more than 30% circulating
leukocytes and tissue macrophages were of do-
nor origin 3 months after transplant (fig. S1A),
this time point (fig. S1, A and B). These results
perinatal circulating hematopoietic precursors,
including monocytes, do not substantially con-
tribute to adult microglial homeostasis. With use
of adult congenic bone marrow chimera models,
evidence in favor of (6–8) and against (9, 10) the
contribution of circulating hematopoietic cells to
microglial homeostasis has been proposed. We
found consistently that 10 to 20% of microglia in
the brain parenchyma are of donor origin at 10,
15, and 21 months after transplant (fig. S1C).
Parabiotic mice, which share the same blood
circulation, provide a means to follow the turn-
over of adult circulating hematopoietic precur-
sors without the need for exposure to radiation
injuries. Although the mixing of the myeloid
lineage is less efficient than the mixing of the
at 1 month and 12 months after parabiosis (fig.
S1D) (12). In contrast, less than 5% of microglia
in agreement with a previous report on 5-month-
old parabionts (9). Consistent with previous re-
ports (9, 10, 13), these results suggest that the
recruitment of bone marrow–derived cells to the
induced brain injuries that followed the trans-
plantation regimen. These results also suggest
that postnatal microglia are maintained indepen-
dently of circulating monocytes throughout life
and are maintained by local radio-resistant pre-
cursors that colonize the brain before birth.
Next, we examined the origin of microglia
during development. In mouse embryos, the first
wave ofhematopoietic progenitors appears in the
extra-embryonic yolk sac and leads to the pro-
duction of primitive hematopoiesis, which takes
place between E7.0 and E9.0 (14, 15). An in-
dependent wave of hematopoiesis termed “defin-
proper in the aorta, gonads, and mesonephros
(AGM) region (14, 15). Around E10.5, hema-
topoietic progenitors start to colonize the fetal
liver, which serves as a major hematopoietic or-
gan after E11.5, whereas later during develop-
1Department of Gene and Cell Medicine and the Immunology
Institute, Mount Sinai School of Medicine, 1425 Madison
Avenue, New York, NY 10029, USA.2Singapore Immunology
3-4, BIOPOLIS, 138648, Singapore.3Department of Develop-
mental and Molecular Biology, Albert Einstein College of
Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA.
Kennedy Center for Research on Intellectual and Develop-
College of Medicine, 1410 Pelham Parkway South, Bronx, NY
10461, USA.5Departments of Neuroscience, Psychiatry, and
Behavioral Sciences,AlbertEinsteinCollege of Medicine,1410
Pelham Parkway South, Bronx, NY 10461, USA.6Herman B
Wells Center for Pediatric Research, Indiana University School
USA.7Laboratory for Stem Cell Biology, Center for Develop-
mental Biology (CDB), RIKEN Kobe, Kobe 6500047, Japan.
*To whom correspondence should be addressed. E-mail:
VOL 330 5 NOVEMBER 2010
on January 20, 2011