Genetic approaches to human renal agenesis/hypoplasia and dysplasia

Congenital abnormalities of the kidney and urinary tract are frequently observed in children and represent a significant cause of morbidity and mortality. These conditions are phenotypically variable, often affecting several segments of the urinary tract simultaneously, making clinical classification and diagnosis difficult. Renal agenesis/hypoplasia and dysplasia account for a significant portion of these anomalies, and a genetic contribution to its cause is being increasingly recognized. Nevertheless, overlap between diseases and challenges in clinical diagnosis complicate studies attempting to discover new genes underlying this anomaly. Most of the insights in kidney development derive from studies in mouse models or from rare, syndromic forms of human developmental disorders of the kidney and urinary tract. The genes implicated have been shown to regulate the reciprocal induction between the ureteric bud and the metanephric mesenchyme. Strategies to find genes causing renal agenesis/hypoplasia and dysplasia vary depending on the characteristics of the study population available. The approaches range from candidate gene association or resequencing studies to traditional linkage studies, using outbred pedigrees or genetic isolates, to search for structural variation in the genome. Each of these strategies has advantages and pitfalls and some have led to significant discoveries in human disease. However, renal agenesis/hypoplasia and dysplasia still represents a challenge, both for the clinicians who attempt a precise diagnosis and for the geneticist who tries to unravel the genetic basis, and a better classification requires molecular definition to be retrospectively improved. The goal appears to be feasible with the large multicentric collaborative groups that share the same objectives and resources.
Introduction and definition
Congenital abnormalities of the kidney and urinary tract are frequently observed in the first year of life, when they collectively represent a significant cause of morbidity [1] and mortality. Data from birth defects registries [Metropolitan Atlanta Congenital Defects Program (MACDP); California Birth Defects Monitoring Program (CBDMP) [2] indicate an overall frequency from three to six per 1,000 births, and the abnormalities seriously impact life expectancy (http://www.marchofdimes.com). Human urinary tract abnormalities are phenotypically variable and may affect several segments simultaneously, often aggregating to form complex phenotypes. Hence, clinical classification and diagnosis may be difficult. As a consequence of the overlap between anatomical defects, many investigators have opted to group renal and urologic malformations under the single label of Congenital Anomalies of the Kidney and Urinary Tract (CAKUT) [3]. This broad classification is supported by the fact that a mutation in a single gene can have pleiotropic effects on the development of the urogenital tract. For example, mutations in the PAX2 gene cause the renal-coloboma syndrome, but the clinical features of the trait vary significantly between affected individuals, ranging from renal agenesis/hypoplasia to vesicoureteral reflux (VUR) and secondary obstruction [4]. Conversely, mutations in different genes can result in similar renal phenotypes, e.g., EYA1 and PAX2 mutations both can cause the development of hypoplastic kidneys [5]. Hence, improved classification of urinary tract malformations may require understanding of primary molecular defects. A broad but clinically useful diagnostic scheme consists of classifying malformations depending on whether the kidney, the collecting system, or both are affected. This scheme stems from the fact that the upper tract (glomeruli and tubules) is derived from the metanephric mesenchyme (MM), and the lower urinary tract (collecting duct, renal pelvis, ureter) is derived from the ureteric bud [1]. Even if this is in contrast with more recent data about the reciprocal interaction between the ureteric bud and the MM (see below), this classification can be clinically useful to partition patients with different types of urinary tract abnormalities. In this review, we focus on the malformations that primarily involve a reduction of renal parenchyma in the form of renal agenesis and/or hypoplasia/dysplasia, occurring both as isolated forms or in association with other malformations of the lower urinary tract (see below).
Primary renal agenesis
Bilateral renal agenesis is a rare and fatal event, usually associated with severe oligohydramnios, which produces a characteristic clinical pattern with facial compression and pulmonary hypoplasia (Potter syndrome). An estimate of the incidence of bilateral agenesis is 0.1/1000 births. Unilateral renal agenesis is more common, although the frequency is difficult to estimate, as it is usually clinically silent and is commonly detected as a chance observation by autopsy or by prenatal ultrasound [6].
Primary renal hypoplasia and dysplasia
Strictly speaking, renal hypoplasia is defined as a small kidney, which contains intact nephrons that are reduced in number, whereas a dysplastic kidney contains disorganized elements and maldifferentiated tissue. Noninvasive imaging studies such as ultrasounds and dimercaptosuccinic acid (DMSA) scan offer limited information to help distinguish a hypoplastic kidney from a dysplastic one. Unequivocal distinction between these two entities therefore depends on histological examination of renal tissue obtained from kidney biopsy or surgical nephrectomy, which are rarely performed. A further confounding factor is the reduction of kidney size due to chronic injury and scarring from VUR. Most of the time, a DMSA scan helps differentiate primary hypoplasia or dysplasia from small kidneys secondary to VUR. However, a DMSA scan has a low negative predictive value in distinguishing primary hypoplasia or dysplasia from a secondary reduction in kidney size from VUR when scars or areas of negative isotope uptake are present. In practice, the diagnosis of primary renal hypoplasia is favored when the following criteria are satisfied: (a) a reduction of renal size by 2 standard deviations (SDS) from the mean size for the age, (b) exclusion of renal scarring by DMSA scan, and (c) a presence of compensatory hypertrophy of the contralateral kidney. In all cases, the exclusion of renal cysts by ultrasonography is mandatory to avoid confusion with primary renal hypoplasia associated with fibrosis and cysts, nephronophthisis being the most pertinent example. The presence of VUR and/or ureteropelvic junction obstruction (UPJO) does not automatically exclude the diagnosis of hypoplasia, as both conditions are frequently associated with primary renal-size defects. It is clear that this problem is difficult to resolve if the ureteral defect presents ipsilateral to renal hypoplasia. For example, severe antenatal hydronephrosis due to UPJO can determine the involution of the renal parenchyma and lead to an erroneous diagnosis of primary renal agenesis after birth. In bilateral cases, syndromic traits as well as inherited disorders such as medullary cystic kidney disease/nephronophthisis have to be excluded. Unequivocal exclusion of renal dysplasia is usually not feasible except in rare cases for which histology is available. It is possible that in the near future, molecular genetic advances could modify our present understanding and allow for a more direct separation of the two pathological entities based on laboratory tests.
These challenges in clinical diagnosis of renal hypoplasia complicate studies attempting to discover new genes underlying this anomaly. For research purposes, we utilize a tentative classification scheme for categorizing our subjects for genetic studies: (1) isolated bilateral hypoplasia/dysplasia, (2) isolated unilateral hypoplasia/dysplasia, and (3) hypoplasia/dysplasia associated with lower tract abnormalities such as VUR or UPJO. Once the genetic basis of different subsets of urinary tract malformations is identified, the classification will likely be retrospectively changed and improved.
Kidney development and mouse models
The development of mammalian kidney derives from reciprocally inductive events between two tissue compartments of the embryonic metanephros: the ureteric bud (UB), an outgrowth of the nephric duct, and the MM. The ureteric bud invades the metanephric blastema at embryonic day 10.5–11 in the mouse and 35–37 in humans. The MM induces the ureteric bud to grow and branch while the ureteric bud induces the MM to transdifferentiate and form the nephrons’ epithelia (see recent reviews in kidney development in human and mice [7, 8]).
In recent years, many factors, specific for either the UB or the MM, have been demonstrated to induce and regulate the epithelial conversion in the mesenchymal cells and the UB branching, leading to the development of the final structure and function of the kidney. Most data constituting the basis of our current knowledge on the topic are based on gene targeting studies in mice (Table 1). A partial list of genes includes protooncogenes RET and Wingless-related 11 (WNT11) that are well recognized UB-specific molecules, whereas glial cell-line-derived neurotrophic factor (GDNF), Wilms tumor 1 (WT1), and Eyes absent 1 (EYA1) represent important examples of MM-specific factors. The paired-box gene 2 (PAX2) appears to be expressed in both structures during kidney development [7, 9]. It is noteworthy that almost half of the genes on the list are transcriptional factors or encode for proteins that are involved in the mesenchymal to epithelial conversion. GDNF signaling through the RET receptor is one of the best studied pathways, representing a critical step in the normal growth and branching of the UB during kidney development [10]. Perturbation of Gdnf/Ret signaling has been shown to be the downstream mechanism underlying impaired nephrogenesis in many other mutant models (e.g., in Gdf11 and Six1 null mice). Numerous factors other than the Gdnf/Ret pathway also participate in kidney and urologic development (e.g. Wnt signaling), as evidenced by the long list of mutant mice with malformations in the kidney and urologic tract (Table 1).
Table 1Principal genes targeted in mice leading to renal agenesis, hypoplasia, dysplasiaGeneHuman homologKidney phenotypeReferenceFoxd1FOXD1Small, fused, undifferentiated kidneysHatini et al. [59]Eya1EYA1Absent kidneysJohnson et al. [60]Xu et al. [61]Emx2EMX2Absent kidneysMiyamoto et al. [62]Hoxa11/Hoxd11HOXA11/HOXD11Small or absent kidneysDavis et al. [63]Lhx1LHX1Absent kidneysShawlot and Behringer [64]Pax2PAX2Small or absent kidneysTorres et al. [65]Wt1WT1Absent kidneysKreidberg et al. [66]Agtr2AGTR2Multiple urinary tract malformationsNishimura et al. [67]Bmp4BMP4Altered ureteric bud (UB) branchingMiyazaki et al. [68]Bmp7BMP7Disrupted nephrogenesisDudley et al. [69]Wnt4WNT4Undifferentiated kidneysStark et al. [70]RetRETAbsent kidneys, severe dysgenesisSchuchardt et al. [71]GdnfGDNFAbsent kidneys, severe dysgenesisSanchez et al. [72]Moore et al. [73]Pichel et al. [74]Six1SIX1Absent kidneysXu et al. [75]Six2SIX2Small kidneysSelf et al. [76]Sall1SALL1Absent kidneysNishinakamura et al. [77]Fgfr1/Fgfr2FGFR1/FGFR2Absent kidneysPoladia et al. [78]Slit3SLIT3Small or absent kidneysLiu et al. [79]Pbx1PBX1Small or absent kidneysSchnabel et al. [80]Fgf8FGF8Small kidneysPerantoni et al. [81]Rara/Rarb2RARA/RARB2Small kidneysMendelsohn et al. [82]Lim1LIM1Absent kidneysKobayashi et al. [83]
The interdependence between developmental pathways explains why defects in different genes result in similar phenotypes and why morphologic classification of abnormalities alone cannot predict the location or nature of primary defects. Available data thus suggest a large list of candidate genes for human renal and urologic malformations, highlighting the potential for genetic heterogeneity of the trait.
Genetic contribution to human renal agenesis/hypoplasia and dysplasia
A genetic contribution to the development of renal hypoplasia/dysplasia has been recognized for many years. For the isolated, nonsyndromic renal agenesis/hypoplasia and dysplasia, only segregation studies have been performed, and no loci and/or genes have been mapped so far. Much more is known about rare syndromic forms, for which several genes have been already implicated.
Syndromic forms
Syndromic forms of renal hypoplasia/dysplasia include rare disorders affecting extrarenal organs such as the eye, the central nervous system, the skin, the limbs, and others. The list of syndromes that include the renal agenesis/hypoplasia/dysplasia phenotype consists of at least 73 clinical conditions (for more details, see Limwongse and Cassidy [11]). Several genes underlying these defects having been identified (Table 2). Renal-coloboma syndrome, orofaciodigital syndrome, branchiootorenal syndrome, renal cysts and diabetes syndrome, and Fraser syndrome are the most frequent syndromes associated with renal parenchymal defects. It seems clinically relevant that the renal abnormalities may represent the first manifestation of the disease, thus requiring a detailed evaluation of other organs. A list of extrarenal signs and symptoms that clinicians should look for to define these syndromes include retinal coloboma [4], deafness, external ear abnormalities including cysts and fistulas [12, 13], anus imperforates and limb and ear anomalies [14], diabetes and renal cystic dysplasia [15], and others. Finally, renal agenesis/hypoplasia is frequently part of chromosomal disorders (Table 3) that must be recognized for genetic counseling. Most common syndromes that should be considered in the initial differential diagnosis are listed in Tables 2 and 3, and we suggest referring to popular Web sites for further details (links provided at the end).
Table 2List of human malformation syndromes with kidney hypoplasia/dysplasiaGeneHuman syndromeKidney phenotypeOMIMJAG1, NOTCH2Alagille syndromeMCDK, kidney dysplasia, kidney mesangiolipidosis#118450#610205BBS1-BBS11Bardet-Biedl syndromeRenal dysplasia and calyceal malformations#209900EYA1, SIX1, SIX2Branchiootorenal syndromeRenal agenesis/dysplasia#113650SOX9Campomelic dysplasiaDiverse renal malformations#114290CHD7CHARGE syndromeDiverse urinary tract malformations#214800Del. 22q11Di George syndromeRenal agenesis, dysplasia, VUR#188400GATA3Hypothyroidism, sensorial deafness, renal anomalies (HDR)Renal agenesis, dysplasia, VUR#146255DNA repairFanconi anemiaRenal agenesis#227650FRAS1, FREM2Fraser syndromeRenal agenesis, dysplasia#219000KALL1, FGFR1Kallman’s syndromeRenal agenesis, dysplasia#308700, #147950PAX2Renal coloboma syndromeRenal hypoplasia, MCDK, VUR#120330TCF2Renal cysts and diabetes syndromeRenal dysplasia, cysts#137920GPC3Simpson-Golabi-Behmel syndromeRenal dysplasia, cysts#300209DHCR7Smith-Lemli-Opitz syndromeRenal dysplasia, cysts#270400SALL1Townes-Brocks syndromeRenal dysplasia, lower urinary tract malformations#107480LMX1BNail-patella syndromeGlomerulus malformation, renal agenesis#161200NIPBLCornelia de Lange syndromeRenal dysplasia#122470CREBBPRubinstein-Taybi syndromeRenal agenesis#180849WNT4Rokitansky syndromeRenal agenesis#277000PEX-familyZellweger syndromeRenal dysplasia, cysts#214100GLI3Pallister-Hall syndromeRenal agenesis, dysplasia#146510p57(KIP2)Beckwith-Wiedemann syndromeRenal dysplasia#130650SALL4Okihiro syndromeRenal ectopia with or without fusion, lower urinary tract malformations#607323TBX3Ulnar-Mammary syndromeRenal agenesis#181450MCDK multicystic dysplastic kidney, VUR vesicoureteral refluxTable 3Common chromosomal disorders associated with urinary tract anomaliesChromosomal disordersRenal agenesisHypoplasiaOther associated anomaliesPatau syndrome (trisomy 13)+Holoprosencephaly, midline anomalies, cleft lip/palateMiller-Dieker syndrome (17p13 deletion)+MR, lissencephaly, microgyria, agyria, typical facie, seizuresEdward syndrome (trisomy 18) 18q deletion+IUGR, CHD, clenched hands, rocker bottom feet SS, MR, microcephaly, narrow external ear canals, long handsDown syndrome (trisomy 21)+MR, hypotonia, CHD, typical face, clinodactylyCateye syndrome (tetrasomy 22p)+MR, CHD, colobomas, anal/digital anomaliesVelocardiofacial syndrome (22q11 deletion)++Conotruncal CHD, thymic aplasia, typical face, cleft palateTurner syndrome (45,X or 46,X,i(Xq))++SS, amenorrhea, webbed neck, cubitus valgus, hypogonadismMR mental retardation, IUGR intrauterine growth retardation, CHD congenital heart disease, SS short stature
Nonsyndromic forms
It is well known that nonsyndromic renal malformations may occur as hereditary traits and can present with familial aggregation. Evidence in favor of a genetic determination of the disease is raised by an increased recurrence risk among first-degree relatives and by several reports of familial occurrence of multiple malformations, including renal agenesis/hypoplasia and dysplasia. The relative recurrence risk of bilateral and unilateral agenesis has been estimated at 4–9% [6, 16, 17]. For familial cases, in most of the pedigrees, the suggested mode of inheritance was autosomal dominant with reduced penetrance, estimated to range between 50% and 90% [16]. For example, a large pedigree with an autosomal dominant mode form of nonsyndromic renal hypoplasia and dysplasia has recently been described [18]. However, a Somalian kindred in which the trait was segregating in an autosomal recessive fashion has been reported [19]. Nevertheless, until recently, no linkage studies in familial renal agenesis/hypoplasia and dysplasia have been reported. Incomplete penetrance, variable expression and the fact that anatomical defects in many family members can be clinically silent, complicate recruitment of large pedigrees that would be suitable for linkage analysis.
Strategies for gene discovery
Strategies to find genes causing renal agenesis/hypoplasia and dysplasia vary significantly depending on the characteristics of the study population available. Different data sets of patients have potential advantages and possible pitfalls.
Candidate gene studies
So far, candidate gene studies have been the only alternative to linkage analysis to find genes underlying both Mendelian and complex traits. Such studies have identified many genes causing rare genetic diseases [20] (The Human Gene Mutation Database, http://www.hgmd.cf.ac.uk/ac/index.php) and most of the genes that are known contribute to susceptibility to common diseases [21, 22]. Large cohorts of sporadic cases or small pedigrees can be utilized in case-control association studies to find common disease associated alleles. Such cohorts can also be screened by resequencing of candidate genes to detect rare variants with large effects that account for disease in a small proportion of the patients. Selection of one approach over the other depends on the expected degree of genetic and allelic heterogeneity of the trait under investigation. Genetic heterogeneity refers to the situation where mutations in different genes account for disease in different affected individuals. Allelic heterogeneity refers to the presence of many independent mutations in a given gene. For a trait with high locus and allelic heterogeneity, the search for common disease-contributing alleles is problematic, and resources would be better directed toward comprehensive resequencing of candidate genes to discovery the rare disease-causing variants. In practice, the heterogeneity parameters are difficult to predict a priori. The resequencing approach has been successfully applied to find several genes causing kidney developmental disorders. As an example, mutations in the uroplakin III gene, which produce VUR in mice [23], explain a small fraction of human renal hypodysplasia [24–28]. Similarly, results from the ESCAPE study recently provided the first comprehensive analysis of renal developmental genes in children affected by nonsyndromic renal hypodysplasia, showing a fairly high prevalence of PAX2 and TCF2 mutations [5, 29]. Another success of the candidate gene approach is the latest discovery of mutations in genes of the renin-angiotensin system (RAS) in severe forms of renal tubular dysgenesis [30]. The search for common variants predisposing to nonsyndromic renal hypodysplasia has not been frequently applied. However, these common predisposing alleles may not be recognized until a comprehensive search is undertaken. As an example, a common noncoding variant in a RET enhancer has recently been shown to be a strong risk allele for Hirschsprung disease, explaining the paucity of coding mutations found in families showing linkage to the RET locus [31].
Traditional linkage studies and genetic isolates
The genome-wide linkage analysis/positional cloning approach is a time-tested method used to identify disease-causing mutations, and it has been extremely successful in the past few decades for mapping genes that underlie monogenic Mendelian diseases [32, 33]. This approach hinges on availability of single, uniquely large pedigrees that segregate genes with large effect or a large number of small-sized pedigrees. Mutations in genes underlying Mendelian forms of disease usually account for a fraction of sporadic forms (e.g. PAX2 and TCF2).
For renal agenesis/hypoplasia and dysplasia, large pedigrees amenable for linkage analysis are very difficult to ascertain because these traits have incomplete penetrance (due to genetic and environmental modifiers). Moreover, many malformations, such as unilateral agenesis can be clinically silent and will not be detected without systematic screening of family members. As for candidate gene studies, locus heterogeneity is another potentially complicating factor that may dilute the power of linkage studies. Our previous data demonstrated that in the setting of reduced penetrance, variable expressivity, and very high genetic heterogeneity, approaches based on a limited number of uniquely large pedigrees or a very large number of medium-sized kindreds, are more likely to be successful to map a disease gene [34]. As a result of these difficulties, no linkage studies of renal agenesis/hypoplasia have been published so far. These kinds of patient cohorts are very arduous to collect and require multicenter collaborative efforts. We have been able to collect seven multigenerational extended pedigrees segregating congenital anomalies of the kidney and urinary tract, including renal agenesis/hypoplasia, as an autosomal dominant trait with reduced penetrance trait. These families allowed us to localize a gene for this trait to a ~7 Mb interval to chromosome 1p32–33 in a setting of genetic heterogeneity [35]. This work represents the first step toward the discovery of a new gene and, possibly, a new pathway, in kidney development.
Genetic isolates represent a population structure that can greatly facilitate gene identification efforts. The genetic isolates are populations that are originated from a limited group of founders with little subsequent immigration into the population. Without an inflow of genes, a long period of time would be required for spontaneous mutations to rebuild genetic diversity. Therefore, genetic isolates are likely to harbor few disease-contributing alleles that have been inherited identical by descent from common ancestors [36–38]. These ancestral mutations can be detected by searching for a shared haplotype signature in affected individuals, representing a powerful shortcut to narrow down a linkage interval to a handful of genes. This strategy, called linkage disequilibrium (LD) mapping, has allowed the identification of several genes for Mendelian disorders [39–41]. Hence, the advantages of studying a genetic isolate rely on: (a) a higher prevalence of certain diseases, allowing traits with reduced penetrance to express and show their hereditary component, (b) a more uniform genetic background, thus reducing the genetic heterogeneity, (c) usually good genealogical records, (d) a more uniform environment, and (e) the possibility of speeding up gene discovery through linkage disequilibrium mapping. We have recently characterized a genetic isolate in an Italian valley, in which different glomerular diseases occurred at a much higher prevalence compared with the general population, in apparently unrelated individuals. The genealogical reconstruction allowed us to reconnect most of the patients to a few founders up to the sixteenth century [42]. This study is an example of how an isolate can allow traits that display reduced penetrance and variable expressivity to express their genetic component and represent a first step to find genes causing or predisposing to such diseases. Further investigation of recognized population isolates for developmental disorders, especially renal agenesis/hypoplasia and dysplasia, might help to accelerate gene mapping.
Genome-wide association studies
The genome-wide association study is an approach aimed at exhaustively covering the genome to look for causative variants. Similar to genome-wide linkage studies, no assumptions are made about either the location of the causative variant or the biological role of the disease gene. Therefore, this approach represents an unbiased method to find disease-causing genes, with also a very high probability of discovering new genes, thus unraveling new pathophysiological pathways. Genome-wide association studies were not feasible until now because of the lack of information about the variability in the human genome and lack of low-cost, high-throughput genotyping technology. This situation has changed in the past 2 years: dbSNP (http://www.ncbi.nlm.nih.gov/projects/SNP/) now contains about 5 million SNPs, including most of the SNPs with a minor allele frequency higher than 1% estimated to exist in the human genome [43]. Moreover, the HapMap project [44] represents a fundamental advance to performing efficient and successful genome-wide studies through the determination of LD patterns and haplotype blocks across the genome. Another important step has been the tremendous improvement in genotyping technology, with the development of platforms for fast, high-throughput, low-cost SNPs genotyping. Such platforms allow the simultaneous genotyping of 100–500,000 SNPs in a single assay, allowing a dense coverage of the human genome [45, 46]. Some examples of success of this approach have been recently published. For example, genome-wide association studies on patients affected by age-related macular degeneration allowed the individuation of a common variant in the complement factor H as a major risk-associated allele [47, 48]. Similarly, polymorphisms in the transcription factor TCF7L2 have been found to confer risk to type 2 diabetes in different populations [49, 50]. Whether genome-wide association studies will lead to significant discoveries in renal agenesis/hypoplasia and dysplasia is still unclear, but certainly, this approach represents a very promising strategy to identify common variants conferring susceptibility to more frequent, complex traits.
Search for structural variations in the genome
A number of urogenital malformations are associated with chromosomal abnormalities. For example, a deletion on chromosome 10q26 has been implicated in urogenital development [51]. Similarly, two distinct loci for renal malformations, including VUR, have been mapped to chromosome 13q by deletion mapping using microsatellites in a limited number of affected individuals [52, 53]. Advances in technology, mainly, genome-scanning array technologies and comparative DNA-sequence analyses, have identified a high prevalence of DNA variations that involve segments that are smaller than those recognized by standard cytogenetics techniques [54]. These structural variations are a common feature of our genomic landscape, encompassing deletions, duplications, inversions, and translocations, which range from a few bases up to hundreds of kilobases. These rearrangements comprise benign polymorphisms, as well as deleterious mutations that can disrupt gene structure or affect gene regulation. Newer techniques now allow for the identification of structural variation at the genome-wide level, enabling examination of single patients to rapidly define a chromosomal region (locus) of interest. Several studies have already reported structural variations associated to human disease, leading in some cases to a molecular definition of a disorder before a recognized clinical syndrome [55–57].
These technologies have also been already successfully applied to developmental disorders. A genome-wide search for structural variations using comparative genomic hybridization (CGH) array allowed the discovery of the gene CHD7 as a cause of CHARGE syndrome, a rare, complex disorder in which congenital anomalies affect in a nonrandom fashion several tissues, including the urinary tract [58]. Careful clinical selection of patients and application of genome-wide methods for searching structural variation in renal agenesis/hypoplasia and dysplasia can help find new loci linked to the disease, confirm and narrow loci obtained by linkage analysis, and speed up the discovery of causative genes.
Conclusions
Renal agenesis/hypoplasia and dysplasia still represents a challenge for both the clinicians who attempt a precise diagnosis and for the geneticists who try to unravel the genetic basis. Genetic and clinical approaches are now converging toward a common goal, which is the discovery of genetic markers, to make the diagnosis of this trait easier. The final objective is to improve classification, to make a reliable prognosis, and to attempt prevention. Based on advances from the last few years, the goal appears to be more feasible with large multicentric collaborative groups that share the same objectives and resources.

renal agenesis/hypoplasia and dysplasia
linkage analysis
association studies
gene mapping
structural variants


An approach to classifying sequence tags sampled from Plasmodium falciparum var genes

Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) appears to play an key role as both a virulence factor and as a target of naturally acquired immunity [1,2]. This large family of molecules is encoded by the highly polymorphic superfamily of var genes of which there are 60 variants in every genome [3].
A rapidly growing collection of var sequences is now available from clinical isolates around the world [4–11]. Despite immense diversity both in terms of overall organization and primary sequence, the majority of var genes contain a DBL1α region [3]. The existence of short islands of homology within this region has enabled the design of primers that can be used to sample sequence from most var genes to create DBL1α sequence tags [5]. A standard approach to classification of these sequence tags would enable direct comparisons to be made between different studies. However, the extreme diversity of var genes and the fact that they undergo intra-genic recombination [4,12,13], makes this difficult.
Despite the high diversity there does appear to be underlying simplicity to the var genes that supports the use of information present in DBL1α sequence tags in making comparisons between the expression levels in different isolates. Analysis of the fully sequenced genome of a single P. falciparum isolate 3D7 suggests that the genomic location of the 60 var genes promotes genetic structuring and the maintenance of genetically distinct sequence types [14–16]. In addition, structural features of the genes within the single genome of 3D7 closely mirrors the range of structural features among collections of DBL1α sequence tags from clinical parasite isolates [9]. We previously used a small number of key sequence features in an algorithm to classify the DBL1α sequence tags from a single geographical location in Kenya into six groups [9] (see Fig. 1A and below). This var tag grouping system, though it is based on portion of the DBL1α domain (see Supplementary information), corresponded well with whole var gene classification based on the whole genome sequence of the parasite line 3d7 [9]. This grouping system appears to be biologically meaningful. Expression of group 2 sequences was strongly associated with the parasite rosetting phenotype in Kilifi whereas expression of group 1 sequences was negatively associated with the repertoire of antibodies to infected erythrocyte surface antigens carried by the patient at the time of disease [9]. Thus DBL1α sequence tags appear to contain useful information about the genes to which they belong that is currently not directly accessible in field studies of clinical parasite isolates.
We have developed a rapid approach to performing the classification using text string analysis functions in Microsoft Excel and Perl (see Supplementary files). This classifies sequence tags directly without the need for prior alignment and can be performed on many sequences simultaneously. The approach is summarized in Fig. 1A. The classification is based around a count of the number of cysteine residues within the tag region and a set of sequence motifs at four positions of limited variability (PoLV 1–4) whose positions within the sequence are fixed in relation to four anchor points (a–d, marked with arrows in Fig. 1A). Thus PoLV1 and PoLV4 are fixed in relation to the 5′ and 3′ ends of the sequence, respectively (anchor points a and d). PoLV2 and PoLV3 are fixed in relation to a “WW” motif (anchor point b). The definition of the groups defined by these features is summarized in the box in Fig. 1. Henceforth we will refer to these groupings as cyteine/PoLV groups.
This text string analysis approach was tested on the original set of sequences from Kilifi, Kenya [9] and sequences from 9 other studies (see Fig. 1B–E). The sequences were pre-screened to ensure that they contained a 5′DIGDI and 3′PQFLR consensus sequences. Overall 99.6% of sequences could be classified using this approach. This included 100% of sequences from Malawi (J. Montgomery unpublished), Papua New Guinea [7,17], Mali [10], Solomon Islands [7], and The Philippines [7] together with 100% of sequences from one dataset from Brazil [6]. A dataset from Venezuela (52 non-identical sequences [8]) carried two sequences that could not be classified. A dataset from Brazil (137 non-identical sequences, [18]) carried one sequence that could not be classified. The original dataset from Kilifi (878 non-identical sequences [9]) carried two sequences that could not be classified. All five of these sequences lacked WW or VW motifs required as anchor points within the sequence.
Part of the rationale for this grouping system came from a search for PoLV motifs that were associated with sequences with distinct length distributions [9]. Two motifs were identified which were independently associated with short sequences. These are MFK* at PoLV1 and *REY at PoLV2 (an asterisk here denotes any amino acid). We hypothesised that if sequences of different length recombine with each other they will generate a wide range of sequences of different lengths whereas genetically isolated sequences, i.e., those that are not recombining with one another are able to maintain distinct distributions in their length. If these groupings are genuine the sequences classified into different groups should have similar lengths in different settings. As shown in Fig. 1B–E, broadly similar distributions of sequence length are observed within the six different groups between three different continents, suggesting that sequences generated in these different studies shared the same set of structural features. Specifically, MFK* (carried at PoLV1 in group 1) and *REY (carried at PoLV2 in groups 2 and 5) are associated with short sequences in each geographical region. No examples of sequences with both MFK* and *REY motifs were found, suggesting that these motifs are mutually exclusive. In addition, though *REY motifs were found in sequences with 2 or 4 cysteine residues (cys2 or cys4), with the exception of a single cys4 (group 4) sequence from the Philippines, MFK* motifs were found exclusively in cys2 (group 1) sequences.
Further support for the cysteine/PoLV groupings comes from recent publications. Trimnell et al. found a good correspondence between cysteine/PoLV groupings of cys2 sequences and groups defined phylogenetically within a globally sampled subset of var genes with a specific upstream control region, upsA [11]. Also evident from sequences reported in that study is the fact that DBL1 from two other globally sampled subsets of var genes can be easily distinguished from DBL1 domains from other vars using unique PoLV motifs. var2csa vars have a unique PoLV2 motif “EVIT”, whereas Type3 vars have a unique PoLV4 motif “PPVV” (data not shown).
Kraemer et al. have recently performed an analysis and re-classification of whole var genes from 3D7, HB3 and IT4 [19]. Fig. 2A and B summarizes the relationship between the cysteine/PoLV groupings and whole var gene classification. With the exception of group 6 sequences which were not found in HB3 var genes all sequence groups were represented. In all three genomes cysteine/PoLV group 1 sequences are exclusively found in group A var gene and long genes with >5 domains whereas cysteine/PoLV group 5 are found only in non-group A genes and those with 4–5 domains. Cys2 sequence tags (groups 1–3) were never found in group C var genes.
Kyriacou et al. used a phylogenetic approach to compare DBL1α sequence tags from Mali [10]. Visual inspection of the layout of these sequences reveals three main groups and a minor group. There was good correspondence between these groups and the cysteine/PoLV groupings (Fig. 2C [10]). This study showed that cys2 sequence tags were more frequent among parasite isolated from children with cerebral malaria than those from children with hyperparasitaemia. However, division of the sequences into cysteine/PoLV groups suggests that the frequency of group 2 sequences is similar in parasites from these two groups of children (Fig. 2D [10]).
At a higher level of resolution, the distinct sequence identifier (DSID) (see Fig. 1A) is a potentially useful method of further classifying sequence tags. This consists of a string of sequence features in the form “PoLV1-PoLV2-PoLV3-number of cysteines-PoLV4-sequence tag length”. The DSID captures more of the overall sequence diversity than the previously described “sequence signature” [9] whilst remaining robust to minor changes introduced by sequencing or PCR errors. Among the 1595 non-identical sequences identified in all the studies described here, there were 1111 DSIDs. Fig. 1F–G illustrates the potential usefulness of this approach to classification. In Fig. 1F, 44 “common” sequences that were shared between more than one study were selected. Fishers exact test was used to determine whether these common sequences were shared between two studies more or less than would be expected by chance (+ or − symbols, respectively). Fig. 1G is the same except that the analysis was done at the level of 157 “common” DSIDs that were shared between more than one study. In contrast to Fig. 1F, there was a highly significant similarity between var genes from South American isolates in support a recent study of Amazonian isolates [18]. In contrast to the low overlap between DSIDs from Kilifi and from South America (Fig. 1G) there is considerable overlap in the constituent PoLV motifs themselves (see Supplementary information). This illustrates the potential for recombination to generate diversity from a limited number of sequence blocks [4,12,13].
Since the cysteine/PoLV system of classification is based on commonly occurring sequence features it is hoped that it will useful for initial analysis and annotation, comparison of different geographical regions over time and identification of unusual sequences.

pfemp1, plasmodium falciparum erythrocyte membrane protein 1


Transcriptomics and adaptive genomics of the asymptomatic bacteriuria Escherichia coli strain 83972

Escherichia coli strains are the major cause of urinary tract infections in humans. Such strains can be divided into virulent, UPEC strains causing symptomatic infections, and asymptomatic, commensal-like strains causing asymptomatic bacteriuria, ABU. The best-characterized ABU strain is strain 83972. Global gene expression profiling of strain 83972 has been carried out under seven different sets of environmental conditions ranging from laboratory minimal medium to human bladders. The data reveal highly specific gene expression responses to different conditions. A number of potential fitness factors for the human urinary tract could be identified. Also, presence/absence data of the gene expression was used as an adaptive genomics tool to model the gene pool of 83972 using primarily UPEC strain CFT073 as a scaffold. In our analysis, 96% of the transcripts filtered present in strain 83972 can be found in CFT073, and genes on six of the seven pathogenicity islands were expressed in 83972. Despite the very different patient symptom profiles, the two strains seem to be very similar. Genes expressed in CFT073 but not in 83972 were identified and can be considered as virulence factor candidates. Strain 83972 is a deconstructed pathogen rather than a commensal strain that has acquired fitness properties.
Introduction
Urinary tract infection (UTI) is one of the most common infectious diseases in humans and a major cause of morbidity. It is estimated that 40–50% of adult healthy women have experienced at least one UTI episode (Foxman 2002). UTI can be caused either by pathogenic strains leading to symptomatic UTI or by asymptomatic bacteriuria (ABU) strains resulting in a symptom-free carriage resembling commensalism. Escherichia coli is responsible for more than 80% of all UTIs. Acute pyelonephritis is a severe acute systemic infection caused by uropathogenic E. coli (UPEC) clones with virulence genes clustered on “pathogenicity islands” (PAIs) (Eden et al. 1976; Funfstuck et al. 1986; Stenqvist et al. 1987; Orskov et al. 1988; Johnson 1991; Welch et al. 2002). Paradoxically, a large proportion of UTIs are caused by ABU E. coli. Individuals infected with ABU-class E. coli may carry high urine titres of a single E. coli strain for months or years without provoking a host response.
Escherichia coli 83972 is a prototype ABU strain and undoubtedly the best-characterised ABU-class E. coli to date. Strain 83972 was originally isolated in the 1970s from a young girl who had carried it for at least 3 years without symptoms (Lindberg et al. 1975; Andersson et al. 1991). The strain is well adapted for growth in the human urinary tract (UT) where it establishes long-term bacteriuria (Hull et al. 2000). It has been used for prophylactic purposes in numerous studies; as such it has been used as an alternative treatment in patients with recurrent UTI who are refractory to conventional therapy (Hull et al. 2000). An ongoing study on patients infected with strain 83972 has so far reported over 50 patient years with no serious side effects (Sundén et al. 2006).
ABU patients may carry a single strain for months or years, creating a condition that resembles commensalism, but with a strain that may have evolved from a pathogenic ancestor. Several lines of evidence support the notion that the ancestor of strain 83972 was a pyelonephritic UPEC strain; it belongs to the B2 clonal group, a group associated with pyelonephritis and other extra-intestinal invasive clinical syndromes such as bacteremia, prostatitis and meningitis; the strain also contains gene clusters in various stages of erosion encoding the three UPEC-class fimbriae, i.e. the fim, pap and sfa/foc clusters (Klemm et al. 2006; Roos et al. 2006a).
Several studies have investigated the virulence characteristics of uropathogenic E. coli (UPEC) isolates and ABU isolates, in order to get better understanding of how some UTI strains can cause severe disease, while others can be used prophylactically to prevent the same (Blanco et al. 1996; Dobrindt et al. 2003; Vranes et al. 2003; Johnson et al. 2005; Marrs et al. 2005). ABU strains have been shown to lack many of the virulence-associated phenotypes; many of them are nonhaemolytic, nonadherent and lack haemagglutination ability (Vranes et al. 2003). Strain 83972 lacks many of the virulence-associated phenotypes but has been shown to carry many of the virulence-associated genes, such as kps, iutA, fyuA and malX (Dobrindt et al. 2003). However, apart from the fimbrial clusters, the strain has not been sequenced and it is not known which genes it shares with other E. coli isolates, which of the genes in the E. coli “core genome” it carries and which genes it shares with other UTI isolates.
Thus far, although a number of UPEC isolates (i.e. CFT073, 536, UTI89 and F11) have been completely sequenced, no genomic sequencing of any ABU strain has been reported. Comparative genomics profiling using microarray chips designed to cover entire genomes is one strategy to obtain information about variability between different strains of the same species and indication of horizontal gene transfer (Willenbrock et al. 2006). DNA microarray-assisted functional genomics provides the global expression profile of a strain, revealing which genes are expressed under certain conditions. Global gene expression profiling of ABU strain 83972 employing the GeneChip E. coli Genome 2.0 Array (Affymetrix), containing four E. coli genomes including that of the UPEC isolate CFT073, will not only provide information regarding the up- and down-regulation of genes comparing different conditions, but will also reveal which genes are actually present (expressed) in the genome of 83972. Bacterial pathogens differ from commensals by expression of specific virulence factors such as those that mediate histological damage. Commensals, in contrast, have generally been regarded as bacteria lacking such virulence factors or other specific mechanisms for interaction with host tissues. Here we compare the global expression profiles of E. coli ABU strain 83972 grown under a number of different in vitro conditions and in three patients in order to get a representative picture of which genes are present/expressed in the genome of this asymptomatic UTI strain.
Materials and methods
Bacterial strain
Escherichia coli 83972 is a prototype ABU strain and lacks defined O and K surface antigens (Lindberg et al. 1975). It belongs to the ECOR group B2 together with many other UTI strains such as the well-characterized and virulent E. coli isolates CFT073, 536 and J96.
Growth conditions and stabilisation of RNA for microarray experiments of E. coli 83972 grown on urine agar plates
Human urine was collected from four healthy men and women volunteers who had no history of UTI or antibiotic use in the prior 2 months. The urine was pooled, filter sterilised, stored at 4°C, and used within the following day. E. coli 83972 was grown aerated in triplicates in 10 ml of human urine for 6 h. Thereafter, 100 μl of each culture was spread on urine plates (1:1 ratio of human urine and 0.9% NaCl) containing 1.5% agar. The plates were incubated at 37°C for 16 h. Subsequently, 600 μl of a 1:2 mixture of PBS and RNAprotect™ Bacteria Reagent (QIAGEN AG) was poured on the plates, mixed with the lawn of cells and incubated for 5 min at room temperature to stabilise RNA. The stabilised mixture was then centrifuged and pellets were stored at −80°C. The samples from 83972, grown exponentially in MOPS and urine, in urine biofilms and in patients (>108 CFU/ml) were all treated identically with RNAprotect Bacteria Reagent and have been described previously (Roos and Klemm 2006; Hancock and Klemm 2007).
RNA isolation and microarray hybridisation
Total RNA was isolated using the RNeasy® Mini Kit (QIAGEN AG) and on-column DNase digestion was performed using RNase-Free DNase Set (QIAGEN AG). The quality of the total RNA was examined by agarose gel electrophoresis and by measuring the absorbance at 260 and 280 nm to ensure intact high-quality RNA. Purified RNA was precipitated with ethanol and stored at −80°C until further use. Conversion of RNA (10 μg per sample) to cDNA, labelling and microarray hybridisation were performed according to the GeneChip Expression Analysis Technical Manual 701023 Rev. 4 (Affymetrix, Inc., Santa Clara, CA). GeneChip E. coli Genome 2.0 Arrays (Affymetrix) were used for hybridisation of the labelled cDNA. The microarrays were scanned using the GeneChip Scanner 3000.
Data analysis
The raw intensities from the microarray experiments were background corrected and quantile-normalised. All microarray data in the study were obtained from mRNA being converted to cDNA, i.e. no genomic DNA was used for hybridisation. Probe intensities were summarised to yield expression values for each probe set or gene. These calculations were performed using the implementation of GCRMA (Wu et al. 2004) in Bioconductor (Gentleman et al. 2004) (http://www.bioconductor.org, http://www.r-project.org). In order to derive a cut-off expression value for making presence/absence calls, we made use of intensities due to control probe sets with IDs beginning with AFFY. There were 96 such probes. The cut-off value was set so that only the top 1/16th of these control probes would be flagged as present. As a result 4,109 genes in the array were marked as present; the remaining genes are referred to as “absent” throughout this report, i.e. these genes could be truly absent, non-homologous or not expressed during any of the seven different growth conditions. Orthologs of all the genes in the array across E. coli K12 MG1655, E. coli O157:H7 Sakai, E. coli O157:H7 EDL933 and E. coli CFT073 were identified using bidirectional best hit BLAST.
Microarray data accession number
The supporting microarray data have been deposited in ArrayExpress (http://www.ebi.ac.uk/arrayexpress) with accession numbers E-MEXP-584 (MOPS, urine and patient arrays), E-MEXP-926 (biofilm arrays) and E-MEXP-1453 (urine-agar plate arrays).
Results
Genes expressed in ABU E. coli 83972
The bacterial transcriptome is a dynamic entity that reflects the organism’s immediate, ongoing response to its environment. DNA microarray-assisted functional genomics provides the global expression profile of the genome. The genomic expression profiles of the urinary tract infectious E. coli isolate 83972 were analysed under several different growth conditions and in different media using the GeneChip E. coli Genome 2.0 Array (Affymetrix). This array contains approximately 10,000 probe sets for all 20,366 genes present in E. coli strains MG1655 (K-12), CFT073 (UPEC), EDL933 (EHEC) and O157:H7-Sakai (EHEC). Due to the high degree of similarity between the E. coli strains, whenever possible, a single probe set is tiled to represent the equivalent ortholog in all the four strains.
In total, 21 microarrays were included in the study; arrays in triplicates were hybridised with RNA of the ABU strain 83972 cultured (1) aerobically to exponential phase in MOPS minimal medium, (2) aerobically to exponential phase in pooled human urine, (3) on urine agar plates, (4) statically in urine biofilm on Petri dishes and finally, (5–7) in three patients (Pat1, Pat2 and Pat3) in vivo. Figure 1 shows the expression levels of all CFT073 genes in strain 83972 during growth in the different environments; many genes were similarly expressed during all seven conditions. However, some genes were expressed only during one or a few of the conditions. For example, the genes encoding yersiniabactin in the high pathogenicity island (HPI), i.e. PAI-asnT, were highly expressed in Pat2 (and in biofilm), but much lower during the other conditions. The c2557–c2563 genes (around 2.4 M in Fig. 1), involved in nucleotide sugar and mannose metabolism and encoding hypothetical proteins, were highly expressed in Pat3 but not under any other condition. Another example is the c1968–c1971 genes (around 1.8 M), i.e. ydfI encoding a d-mannonate oxidoreductase, ydfJ encoding a metabolite transport protein and rspAB involved in the starvation response, which also were highly expressed only in Pat3.
Fig. 1The expression levels of CFT073 genes in strain 83972 during seven different growth conditions. The outer blue circle shows the calculated absence (0.0) and presence (1.0) of CFT073 genes in ABU strain 83972. The seven PAIs of CFT073 are indicated in red
In total, there were 108 genes that were significantly changed in all six urine environments compared with MOPS. Twenty of these genes were up-regulated in all six urine conditions whereof half were related to different iron systems, i.e. iroN, fepA, fecI, iucBC, fhuA and exbD, as well as b3337 and b1995 involved in iron storage and encoding a putative haemin receptor, respectively. The other urine up-regulated genes were marA, a multiple antibiotic resistance gene, sodA, encoding superoxide dismutase, ahpC, encoding hydroperoxide reductase, b1452, c1220, c4210, lysA, rrsG, rrsH and yrbL. Most iron acquisition systems were expressed in all the six urine environments; the enterobactin, salmochelin, aerobactin, haem and sitABCD systems were all expressed in all the six urine conditions (although weaker in the urine plates). Interestingly, the fec system, which is a citrate-dependent iron uptake system found in K-12 but missing in CFT073 and other UPEC strains, was highly expressed in Pat3. Up-regulation of all these iron-uptake systems revealed that the strain has an impressive array of iron acquisition systems and all of these are active in the human bladder.
Nineteen of the top 31 highest expressed genes overall were genes involved in ribosomal synthesis. The high expression of ribosomal genes in E. coli 83972 suggests a rapid growth rate; the highest expression values were obtained in Pat1 followed by MOPS, urine and Pat2, indicating a growth rate just as fast in the patients in vivo as in exponential growth phase in a shake flask. This supports our hypothesis that the strain’s optimized growth properties in human urine explain its ability to successfully colonize the human urinary tract in the absence of functional fimbriae (Roos et al. 2006b).
Figure 1 reveals that strain 83972 almost exclusively expresses the iron uptake and transport systems in the seven CFT073 PAIs, almost none of the other genes in these islands are expressed. There are only two exceptions; c0300, located in PAI-aspV encoding a hypothetical protein, and c3686–3690, located in PAI-pheV encoding YrbH and KpsEDC. The yrbH gene belongs to the 131 genes that were recently identified as UPEC specific and it was the second highest expressed UPEC-specific gene in mice (Lloyd et al. 2007); in our samples the highest expression was found in the three patients and in MOPS. Outside the PAIs there are a few genes/gene clusters that are highly expressed in all urine samples or only in the patients. The enterobactin system was up-regulated during all urine conditions and the chu cluster (involved in haem uptake and transport) was highest up-regulated in the patients followed by in vitro urine growth. The ycdO and ycdB genes were highly expressed in the three patients; these have recently been identified to encode haemoproteins, probably involved in iron transport, induced at acidic conditions (Sturm et al. 2006).
Looking at the significantly changed genes for all six urine conditions compared with MOPS (in total 1,897 genes) revealed that Pat2 and Pat3 shared the largest number of similarly changed genes; 75% of all changed genes in Pat2 are regulated in the same way (i.e. up or down-regulated) in Pat3 (Fig. 2). Interestingly, Pat1 shared the largest number similarly regulated genes with the biofilm growth mode; also for Pat2 and Pat3, the biofilm growth mode showed a larger number of similarly regulated genes than Pat1 or any other condition. This could indicate that the expression profile of strain 83972 during in vivo growth is closer related to biofilm growth than to growth in shake flasks or plates.
Fig. 2Number of significantly up- and down-regulated genes in strain 83972 during the different growth conditions (i.e. exponential growth in urine, on urine-agar plates, in urine biofilm, in vivo in three patients) compared with exponential growth in MOPS minimal lab medium. The diagonal boxes (dark blue colour) show the number of significantly changed genes during cultivation in that specific condition compared with MOPS (e.g. 664 genes were up- or down-regulated in urine compared with MOPS and 938 genes were changed in plates compared with MOPS) and the other boxes show the number of significantly changed genes shared between two conditions (e.g. 311 of the 664 and 938 significantly changed genes in urine and plates compared with MOPS were shared between these two conditions, i.e. up- or down-regulated in both urine and plates compared with MOPS). Stronger blue colour indicates larger number of significantly changed genes shared between two conditions
Closeness to CFT073
Given the different growth conditions analysed, it is not unrealistic to assume that most genes present in strain 83972 would be expressed, to some extent, under at least one of these seven different conditions/environments, i.e. growth in liquid and on solid media; during exponential phase, in biofilm and during colony-forming conditions; in different growth media (human urine and minimal lab medium); as well as in vivo in three different individuals.
Data analysis of the 21 microarrays revealed that of the 8,716 E. coli transcripts on the microarray (not including probes representing intergenic regions and controls), 4,109 transcripts (47%) showed expression levels above detection limit during at least one of the growth conditions investigated (referred to as “present”, see blue, outer circle in Fig. 1). Figure 3 shows the distribution among the four E. coli genomes represented on the microarray of these 4,109 transcripts expressed in E. coli 83972. Not surprisingly, the UTI strain 83972 shows highest similarity with the UPEC isolate CFT073 of the four genomes on the array; the large majority of the 4,109 transcripts found present (96.3%) can be found in CFT073, corresponding to 71% of the CFT073 genome. E. coli 83972 expressed 150 genes that do not exist in CFT073; 85 of these can be found in MG1655 and the remaining 65 genes can be found exclusively in one or both of the two EHEC strains present on the array (Fig. 3). Thirty of the 65 genes homologous to EHEC genes are encoding proteins of cryptic prophages, whereas the large majority of the remaining 35 genes encode unknown or hypothetical proteins. The 85 genes that can be found in MG1655 but not in CFT073 includes the fec cluster encoding an iron citrate transport system (fecABCDEIR). In total, 3,959 CFT073 genes were expressed in strain 83972; this could be compared with 4,162 CFT073 genes present in the UPEC (cystitis) isolate F11 (Lloyd et al. 2007).
Fig. 3Venn diagrams showing the distribution of the 4,109 genes filtered present in strain 83972. The percentages indicated below each strain show how large part of the genome of the corresponding strain was filtered present in strain 83972
E. coli core genome
There is a large diversity in size of the chromosome of E. coli; in all 32 E. coli (and Shigella) genomes that have been fully sequenced, or at least with an expected coverage of greater than 99%, the size of the chromosome ranges from 4.5 to 5.6 Mbp. The genomes show a considerable amount of diversity, and the estimated size of the current pan-genome was estimated to contain 9,433 different genes (Willenbrock et al. 2008). Several studies have identified sets of “core genes” found in most E. coli genomes. However, the number of these core genes tends to decrease as the full genomic sequences of new E. coli strains become available. The size of the E. coli core genome has recently been predicted to contain 1,563 genes for an infinite number of E. coli strains, and the number of new genes predicted from each new E. coli genome that is sequenced is ∼79 (Willenbrock et al. 2008). In our analysis, 2,472 (60%) of the genes found present in strain 83972 were common in all the four E. coli genomes on the array (Fig. 3), which is well above the estimated E. coli core genome and also above the 2,241 common genes conserved among the 32 sequenced E. coli strains (Willenbrock et al. 2008). Furthermore, considering the fact that the microarray contains only four E. coli genomes, the total number of genes detected present (4,109 genes) in 83972 seems reasonable comparing the size of other sequenced UTI E. coli genomes. The genome size of strain 83972 has been reported to be 4.9±0.2 Mbp (Zdziarski et al. 2007), indicating that the strain contains roughly an additional 800 genes, not identified in the present analysis.
Of the 2,734 transcripts on the chip that are present in all the four strains represented on the microarray, 393 transcripts were below detection limit on all 21 microarrays and filtered as “absent” in strain 83972. These included 81 genes encoding hypothetical proteins. Several of the absent genes were found in clusters, many of which are involved in surface structure elements and chemotaxis. These included genes involved in flagellar biosynthesis (flgABCDEFGHIJKL, flhABE, fliACDEFGHIJKLNOPQRSTZ and motAB), curli production (csgABCEFG), colanic acid synthesis (wcaABCDEFGHI and wza) and chemotaxis (cheBRWYZ and tap). Other whole cluster of genes that were not expressed in the ABU strain but found in all the four E. coli present on the chip were hyaBCDEF (hydrogenase I), hycACD (hydrogenase 3), tauABCD (responsible for taurine uptake in E. coli) and b1500–1505 (containing the fimbrial-like genes ydeQRST), as well as the fimEAIC genes which previously have been shown to be absent in strain 83972 (Klemm et al. 2006).
UPEC-associated genes present in strain 83972
The four UPEC isolates that have been sequenced, CFT073, UTI89, 536 and F11, contain 5,379, 5,154, 4,766 and 4,467 genes, respectively, on the chromosome. CFT073 and 536 are both O6 strains and yet show a large diversity; the genome of 536 is almost 300 kb smaller than that of CFT073 (Brzuszkiewicz et al. 2006). The genomic differences are mainly restricted to large pathogenicity islands, the additional DNA in CFT073 are genes of five cryptic prophages, which are absent in 536 (Brzuszkiewicz et al. 2006). The 427 genes that are present only in the strain 536, and the 432 genes present only in the two UPEC (compared with other sequenced E. coli) are scattered all over the genome (Brzuszkiewicz et al. 2006). Over 70% of the CFT073 transcripts were present in strain 83972 compared with 89% of the CFT073 transcripts found in strain 536. Figure 4 shows the homology of 16 sequenced E. coli and Shigella isolates including the three sequenced UPEC strains (UTI89, 536 and F11) pasted on the CFT073 genome; the outer, red circle in the figure shows the results from the presence/absence analysis on strain 83972. Many virulence-associated genes are located on the large pathogenicity islands (PAIs) found in different UPEC strains. The large pathogenicity island at pheV in CFT073 (also called PAI ICFT073) encodes haemolysin (hlyCABD), aerobactin biosynthesis proteins (iutA and iucABCD), antigen 43 (c3655) and the secreted autotransporter toxin (sat); these were all filtered present in our analysis, suggesting that strain 83972 harbours a similar island on its chromosome. Interestingly, the aerobactin system is missing in the other three UPEC isolates. Furthermore, this PAI contains genes encoding the uropathogenic-associated P fimbriae (papIBAHCDJKEFG). The pap gene cluster of 83972 has been sequenced (Klemm et al. 2006); the pap genes are all present and show 72–100% sequence homology with the corresponding genes in CFT073. The results of the microarray analysis corresponded very well to the observed sequence homology of the different genes in the cluster (i.e. if a specific gene on the microarray is represented with probes that contain a non-homologous region compared with the corresponding gene in the hybridised sample, that gene will not hybridise and will be filtered absent); the six genes with highest sequence homology were filtered present (i.e. papHCDJKF with 98, 100, 100, 98, 99 and 95% homology, respectively) and the four with least sequence homology were filtered absent (i.e. papIAEG with 94, 83, 77 and 72% homology).
Fig. 4BLAST atlas comparing the absent (0.0) and present (1.0) CFT073 genes in strain 83972 with other sequenced E. coli and Shigella strains, including the three sequenced UPEC isolates 536, UTI89 and F11. The UPEC CFT073 genome is used as reference. The outer blue circle represents the calculated absence/presence in 83972 followed by the three UPEC isolates; the six inner circles represent Shigella strains. The seven PAIs of CFT073 are indicated in red. The blow-up shows the presence/absence of the fim cluster (c5391–5400) in strain 83972
The employed microarray contains probes for all ten known and putative fimbriae-encoding gene clusters in CFT073. Together with the pap cluster, two other fimbrial clusters that have been associated with UPEC virulence are known to be present in strain 83972 and have been sequenced, i.e. the fim and sfa/foc clusters. As for the pap cluster, the filtering of absent genes corresponded very well to the actual presence and sequence homology of the genes; strain 83972 contains a large deletion in the fim cluster but shows high sequence homology with the present genes, and all the genes in the deleted part of the cluster, i.e. fimEAIC, were filtered absent (see blow-up in Fig. 4). Also, the sfa/foc cluster in 83972 shows high homology with that in CFT073 (98–100%), and eight of nine genes were filtered present; the putative regulatory gene, sfaC, was filtered absent. Regarding the other fimbrial clusters present on the microarray, none of the genes encoding F9 fimbriae, which appear to be common in UPEC and plays a role in biofilm formation (Ulett et al. 2007), and another putative fimbriae (yehABCD) were expressed and might be absent in strain 83972 (Table 1).
Table 1Analysis of fimbriae-encoding genes in strain 83972Descriptionc numberGenesNo of genesNo (%) of absent genesAbsentPutative chaperone-usher fimbrial operonc0166–0172yadN-ecpD-htrE-yadMLKC73 (43)ecpD, yadMKF1Cac1237–1245sfaCB-focAICDFGH91 (11)sfaCF9c1931–1936c1936-34-ydeSRQ66 (100)AllPutative chaperone-usher fimbrial operonc2635–2638yehABCD44 (100)AllPutative chaperone-usher fimbrial operonc2878–2884yfcOPQRSUV75 (71)yfcQRSUVP fimbriaeac3583–3593papIBAHCDJKEFG114 (36)papIAEGbPutative chaperone-usher fimbrial operonc3791–3794ygiLGH-c379441 (25)ygiLAuf fimbriaec4207–4214aufABCDEFG87 (88)aufBCDEFGP fimbriae (2)ac5179–5189papIBAHCDJKEFG2 (papAD)1 (50)papA_2Type 1 fimbriaec5391–5399fimBEAICDFGH94 (44)fimEAICaFimbrial operons present on PAIs. The two pap clusters share the same probes on the array with exception for papA and papD, which are represented by two separate probe sets eachbNone of these pap genes are absent in strain 83972, but papIAEG were filtered absent in the microarray analysis due to non-homologous sequence regions compared with the CFT073 pap probes present on the array
Presence of other pathogenicity islands in 83972
Strain 83972 seems to carry most of the pathogenicity islands of CFT073 (or PAIs similar to the ones in CFT073) according to our present/absent analysis (Table 2). The only PAI of CFT073 in which most genes (i.e. 93%) were filtered absent in strain 83972 is PAI-pheU (PAI IICFT073), the island that contains a second pap cluster. The three genes filtered present in this PAI are present in several of the other sequenced E. coli and Shigella strains indicating that these three genes not are unique/characteristic for this island wherefore this PAI is most probably absent in strain 83972.
Table 2Analysis of presence of pathogenicity islands in strain 83972Island nameCommon namec numberNo of genesaAbsent (%)Virulence-associated genesbPAI-CFT073-aspVPAI III CFT073c0253–c03689640 (42)cdiA (c0345), picU (c0350)PAI-CFT073-serXc1165–c12939233 (36)mchBCDEF (c1227, c1229–1232), sfa/foc (c1237–c1247), iroNEDCB (c1250–c1254), ag43 (c1273)PAI-CFT073-icdAc1518–c1601425 (12)sitDCBA (c1597–1600)PAI-CFT073-asnTHPI CFT073c2418–c2437193 (16)fyuA (1246)PAI-CFT073-metVc3385–c34102617 (65)hcp (c3391), clpB (c3392)PAI-CFT073-pheVPAI I CFT073c3556–c369811951 (43)hlyA (c3570), pap (c3582–c3593), iha (c3610), sat (c3619), iutA, iucDCBA (c3623–3628), ag43 (c3655), kpsTM (c3697–c3698)PAI-CFT073-pheUPAI II CFT073c5143–c52164643 (93)pap2 (c5179–c5189)aNo of genes in the PAI that were present on the array with unique probes (i.e. genes that are not orthologues to any other E. coli transcripts present on the array)bBoldface indicates genes filtered present in strain 83972
Insertion of the high pathogenicity island (HPI) of Yersinia pestis has been suggested to be one of the earliest events in the evolution of extraintestinal E. coli strains (Welch et al. 2002). The genes of HPI encoding yersiniabactin (Ybt) were all expressed in strain 83972. The HPI genes have been found up-regulated during urine biofilm growth of 83972 indicating that Ybt-mediated iron-uptake might play an important role in biofilm growth (Hancock and Klemm 2007) and a deletion mutant in the Ybt uptake receptor (FyuA) exhibits reduced biofilm formation (Hancock et al. 2008). The HPI genes have also been found up-regulated in vivo in two of the three patients (particularly in Pat2, see Fig. 1) infected with this strain (Roos and Klemm 2006).
The pks island, a recently characterised and widely spread genomic island found in, for example, meningitis strains and the uropathogenic strain CFT073, encodes a machinery for the synthesis of peptide–polyketides hybrid compounds (Nougayrede et al. 2006). The presence of the island is associated with the accumulation of double-strand DNA breaks in host cells and has genotoxic activity (Nougayrede et al. 2006). This island was expressed in strain 83972 and up-regulated in urine and in vivo (Table 4; Fig. 1). The pks island is widely distributed within E. coli phylogenetic group B2, and has been found in both pathogenic and commensal isolates; in commensal strains the cell-cycle-blocking activity might slow the turnover of the intestinal epithelium, and therefore prolong colonisation.
Presence of positively selected UPEC genes
A recent paper comparing the UPEC isolates CFT073 and UTI89 with six other finished E. coli genome sequences presented 29 genes that are under positive selection only in UPEC strains (Chen et al. 2006). These 29 genes are involved in various aspects of cell surface structure, DNA metabolism, nutrient acquisition and UTI. Of these 29 genes, 25 were filtered present in our ABU strain 83972; many of these genes are represented by more than one transcript on the array due to sequence differences among the four strains present on the array, in all cases the gene filtered present in 83972 corresponded to the CFT073 transcript. Four genes were filtered absent, agaI, yjiL, recC and yegO; they encode a putative galactosamine-6-phosphate isomerase, a hypothetical protein, exodeoxyribonuclease V gamma subunit and a hypothetical transport protein, respectively. The genes in the two COG categories that were significantly enriched in the two UPEC strains, i.e. “cell wall/membrane biogenesis” (amiA, cutE, fepE, ompC, ompF and yfaL) and “secondary metabolites biosynthesis, transport and metabolism” (entD, entF and yojI) (Chen et al. 2006), were all present in strain 83972.
Functional analysis of MG1655 transcripts of ABU E. coli 83972
To gain more information concerning what type of genes were absent, the MG1655 genes were grouped into functional categories defined by the clusters of orthologous groups (COGs) of proteins (Tatusov et al. 1997). Previous studies have, in attempts to identify essential genes and the E. coli core genome, found that groups with genes involved in metabolism and various cellular processes (excluding cell motility) contain a substantially higher percentage of conserved and essential genes, while COGs with genes of unknown function and external origin as well as genes involved in signalling and motility contain fewer essential genes (Anjum et al. 2003; Gerdes et al. 2003). Classification of the absent genes of strain 83972 revealed that the groups “cell motility”, “defence mechanisms” and “not in COGs” had a significant overrepresentation of absent genes (Table 3). A significantly lower proportion of absent genes were found in the groups: “cell cycle control”, “posttranslational modification” and “translation”. This is in agreement with a previously published study of pathogenic E. coli; Anjum et al. (2003) studied 26 strains of E. coli and found that the two groups with largest proportion of absent genes were “not in COGs” and “cell motility”, while the six groups with the lowest proportion of absent genes were “translation”, “cell division”, “posttranslational modification”, “coenzyme metabolism”, “nucleotide transport and metabolism” and “energy production and conversion”, which all, with exception for the last group, contained significantly fewer absent genes in strain 83972 (Table 3). This suggests that strain 83972 utilises a similar set of core genes as other E. coli strains.
Table 3Distribution of absent genes in functional categoriesFunctional categoryAbsentTotalZ testNo.%P valueAmino acid transport and metabolism9833.12960.636Carbohydrate transport and metabolism12642.32980.025Cell cycle control, cell division and chromosome partitioning516.1310.000Cell motility7784.6910.000Cell wall/membrane/envelope biogenesis8140.52000.087Coenzyme transport and metabolism2523.11080.001Defense mechanisms1748.6350.000Energy production and conversion8836.72400.563Function unknown6124.72470.003General function prediction only8330.92690.255Inorganic ion transport and metabolism5534.41600.921Intracellular trafficking, secretion and vesicular transport822.2360.000Lipid transport and metabolism2129.6710.129Nucleotide transport and metabolism1924.4780.002Posttranslational modification, protein turnover, chaperones2420.21190.000Replication, recombination and repair7042.41650.023Secondary metabolites biosynthesis, transport and catabolism2240.7540.075Signal transduction mechanisms3933.61160.748Transcription7833.22350.654Translation, ribosomal structure and biogenesis2113.51560.000Not in COGs57754.210650.0001,59539.24,070
CFT073 genes absent in strain 83972
There were 1,636 CFT073 genes that could not be detected according to our expression profiling in ABU strain 83972; 961 of these genes are exclusively found in CFT073, i.e. not present in the other three strains represented on the array. The majority, 645 genes, corresponded to hypothetical, putative or unknown proteins. Considering the very different patient symptom profiles of strains CFT073 and 83972 (one being a true pathogen, while the latter is a commensal-like strain), genes that are present in UPEC isolate CFT073 but not expressed in ABU strain 83972 can be considered as virulence factor candidates. However, most genes associated with UPEC pathogenesis were expressed in strain 83972 and up-regulated during growth in urine, e.g. all iron-related genes encoding uptake and transport of aerobactin, salmochelin, yersiniabactin and haem/haemoglobin (Table 4). Two exceptions were the ireA gene encoding an iron-regulated outer-membrane protein that was filtered absent as well as the tsx gene encoding a nucleoside-binding outer-membrane protein. Although the tsx gene has not previously been associated with UPEC virulence, it has just recently been identified together with more well-known UPEC genes as involved in movement from the intestinal tract to the bladder and vagina (i.e. occurred significantly more often in multiple-site isolates than in rectal site-only isolates) (Xie et al. 2006); furthermore, Tsx was also recently identified together with 22 other outer-membrane proteins from CFT073 cells grown under conditions mimicking the urinary tract (Hagan and Mobley 2007).
Table 4Characteristics of ABU isolate 83972 compared with UPEC isolates CFT073, UTI89 and 536CharacteristicaCFT073UTI8953683972Expression in 83972bSerotypeO6O18O6O?cCapsuleK2K1K15K?cChu++++U, BF, PatEnt++++U, BF, PatFep++++Pl, U, BF, PatFeo++++BF, PatFhu++++Pl, U, BF, PatIro++++U, PatIuc+−−+Pl, U, BF, PatIutA+−−+U, BF, PatSit++++U, BF, PatFyuA++++U, BF, PatIha+−−+U, PatIreA+−−−Pks island++++U, PatRfaH++++BFd-serine++++PatPap+++−Fim+++−Foc/sfa+++−Vat++++BF, PatSat+−−+UTsx+++−Biofilm formation1.01.314.4aBoldface indicates genes that were filtered absent in strain 83972bUp-regulation in urine (U), biofilm (BF), plates (Pl) and patients (Pat) compared with MOPS minimal mediumcUndefined. Extensive electron microscopy analysis of the strain has never reported any capsule
Type IV fimbriae are assembled by the type II general secretory pathway. They occur in a wide range of species and frequently are associated with diseases. The ppdD and hofBC genes (b0106–0108), which encode type IV prepilin and are present in CFT073, EDL933 and MG1655, were filtered absent in strain 83972.
CFT073 genes present in strain 83972 but not found in other UPEC strains
The majority of the genes that are absent in the other three UPEC isolates (i.e. 536, UTI89 and F11) were filtered absent in strain 83972 as well (gaps in Fig. 4). However, there are a few exceptions where a gene that is not found in any of the other UPEC strains is filtered present in strain 83972. The aerobactin system belongs to one of the exceptions, indicating that strain 83972 is particularly well equipped with iron uptake systems. The other exceptions are all but one located on PAIs and they all encode hypothetical proteins: c1194–c1204 (on PAI-serX), c1522–c1528 (on PAI-icdA), c3394–c3396 (on PAI-metV), c3681–c3682 (on PAI-pheV where the aerobactin genes also are found) and c5372–c5382. c3394–c3396 and c5372–c5382 are not present in any of the 16 sequenced E. coli and Shigella strains represented in Fig. 4, indicating that some genes unique to CFT073 can be found in strain 83972 as well.
Discussion
Bacterial genomes are under constant change. New genes are acquired by horizontal transfer and old ones are lost by mutations. It is generally believed that commensal E. coli can become pathogenic through the acquisition of novel genes encoding virulence factors and niche-adaptation factors (Kaper et al. 2004). In contrast to organisms that have acquired genes for pathogenesis, E. coli 83972 is an example of an organism that has adapted to a commensal-like existence through gene deletions and point mutations. Using primarily the CFT073 as a scaffold, we used presence/absence data from seven sets of different gene expression profiles (in total 21 microarrays) to model the gene pool of strain 83972. Given the limitations of the approach, i.e. genes not present on the employed chip have been ignored, a substantial body of information was gathered concerning the genomic content of the strain. As it turned out the strain was highly similar to CFT073; 96% (3,959) of the genes found to be expressed on the employed microarray by 83972 are also found in CFT073, and genes on six of the seven pathogenicity islands of CFT073 were expressed by 83972; furthermore, CFT073 genes not found in any other UPEC isolate were expressed by 83972. An estimated ∼900 CFT073 genes are not expressed by 83972. Arguably, in the light of the difference in patient symptoms invoked by encounters with the two strains, this list represents virulence gene candidates.
Although strain 83972 seems to be a deconstructed uropathogen and does not provoke symptoms in the human host it grows fast in urine and is an excellent colonizer of the human bladder (Roos and Klemm 2006; Roos et al. 2006b; Klemm et al. 2007). It can do so because it has kept a large assortment of fitness factors required for this particular ecological niche. Among the genes expressed under realistic environmental conditions such as in the human bladder are candidates for fitness factor genes, e.g. the many iron acquisitions systems expressed by the strain and many genes involved in sugar acid and amino acid metabolism. Interestingly, many of the known and putative virulence factors of the urinary tract are expressed by strain 83972 and might therefore be considered as fitness factors rather than virulence factors; these include 25 of 29 positively selected UPEC genes as well as the newly characterised pks island inducing breaks in double-stranded DNA in host cells. Also, virulence-associated genes such as cdiA, mchBCDEF, flu, hcp, rfaH, sat, picU and vat were all expressed by strain 83972. Very few of the known or putative virulence factors were absent in (or not expressed by) strain 83972. The pap, fim and foc/sfa clusters encoding UPEC-class fimbriae are dysfunctional in strain 83972 and the clpB, ireA and tsx genes were not expressed in the ABU strain. These stand out as potential virulence candidates together with a number of uncharacterised genes encoding hypothetical proteins.
Thus from the analyses performed here we can make predictions about several gene categories such as potential virulence genes, fitness genes and “household-class” genes. It is also noteworthy that the information reported herein complements a potential genome sequence of strain 83972. Whole genome sequencing can identify the presence of genes but is unable to reveal if they are transcribed. Genes can be silenced not only due to lesions in the actual gene and its promoter but also due to mutations of genes encoding regulatory factors. The methodology employed in the present work reveals the active genome of strain 83972.
ABU strain 83972 is closely related to fully virulent uropathogenic strains. All evidences suggest that the strain is a deconstructed pathogen. This study dispels the commonly held idea that ABU strains are commensals that have picked up niche-adaptation genes by horizontal gene transfer. Rather, strain 83972 was originally a true pathogenic strain that has lost whole or part of operons that contribute to virulence.

asymptomatic bacteriuria
urinary tract infections
global gene expression
virulence factors
microarray