UCHIME improves sensitivity and speed of chimera detection

It is known that PCR amplification of highly homologous genes from complex DNA mixtures can generate a significant proportion of chimeric sequences. The 16S rRNA gene is not only widely used in estimating the species diversity of endosymbionts in aphids but also used to explore the co-diversification of aphids and their endosymbionts. Thus, chimeric sequences may lead to the discovery of non-existent endosymbiont species and mislead Buchnera-based phylogenetic analysis that lead to false conclusions. In this study, a high probability (6.49%) of chimeric sequence occurrence was found in the amplified 16S rRNA gene sequences of endosymbionts from aphid species in the subfamily Lachninae. These chimeras are hybrid products of multiple parent sequences from the dominant species of endosymbionts in each corresponding host. It is difficult to identify the chimeric sequences of a new or unidentified species due to the high variability of their main parent, Buchnera aphidicola, and because the chimeric sequences can confuse the phylogenetic analysis of 16S rRNA gene sequences. These chimeras present a challenge to Buchnera-based phylogenetic research in aphids. Thus, our study strongly suggests that using appropriate methods to detect chimeric 16S rRNA sequences may avoid some false conclusions in endosymbiont-based aphid research.

Development of a PCR amplification assay as a screening test using bulk milk samples for identifying dairy herds infected with bovine viral diarrhea virus

In ecological studies, microbial diversity is nowadays mostly assessed via the detection of phylogenetic marker genes, such as 16S rRNA. However, PCR amplification of these marker genes produces a significant amount of artificial sequences, often referred to as chimeras. Different algorithms have been developed to remove these chimeras, but efforts to combine different methodologies are limited. Therefore, two machine learning classifiers (reference-based and de novo CATCh) were developed by integrating the output of existing chimera detection tools into a new, more powerful method. When comparing our classifiers with existing tools in either the reference-based or de novo mode, a higher performance of our ensemble method was observed on a wide range of sequencing data, including simulated, 454 pyrosequencing, and Illumina MiSeq data sets. Since our algorithm combines the advantages of different individual chimera detection tools, our approach produces more robust results when challenged with chimeric sequences having a low parent divergence, short length of the chimeric range, and various numbers of parents. Additionally, it could be shown that integrating CATCh in the preprocessing pipeline has a beneficial effect on the quality of the clustering in operational taxonomic units.

A score-based read selection strategy enables the assembly of novel full-length ribosomal RNA sequences for mosquitoes, which improves the physical and computational removal of interfering ribosomal RNA reads in RNA-seq and provides another molecular marker for taxonomic and phylogenetic inquiries.

Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Materials and methods Appendix 1 Appendix 2 Data availability References Decision letter Author response Article and author information Metrics Abstract Total RNA sequencing (RNA-seq) is an important tool in the study of mosquitoes and the RNA viruses they vector as it allows assessment of both host and viral RNA in specimens. However, there are two main constraints. First, as with many other species, abundant mosquito ribosomal RNA (rRNA) serves as the predominant template from which sequences are generated, meaning that the desired host and viral templates are sequenced far less. Second, mosquito specimens captured in the field must be correctly identified, in some cases to the sub-species level. Here, we generate mosquito rRNA datasets which will substantially mitigate both of these problems. We describe a strategy to assemble novel rRNA sequences from mosquito specimens and produce an unprecedented dataset of 234 full-length 28S and 18S rRNA sequences of 33 medically important species from countries with known histories of mosquito-borne virus circulation (Cambodia, the Central African Republic, Madagascar, and French Guiana). These sequences will allow both physical and computational removal of rRNA from specimens during RNA-seq protocols. We also assess the utility of rRNA sequences for molecular taxonomy and compare phylogenies constructed using rRNA sequences versus those created using the gold standard for molecular species identification of specimens—the mitochondrial cytochrome c oxidase I (COI) gene. We find that rRNA- and COI-derived phylogenetic trees are incongruent and that 28S and concatenated 28S+18S rRNA phylogenies reflect evolutionary relationships that are more aligned with contemporary mosquito systematics. This significant expansion to the current rRNA reference library for mosquitoes will improve mosquito RNA-seq metagenomics by permitting the optimization of species-specific rRNA depletion protocols for a broader range of species and streamlining species identification by rRNA sequence and phylogenetics. Editor's evaluation Mosquitoes are an important vector for viruses and other pathogens worldwide. However, significant genomic resources are scarce for the study of these species. In this work, the authors create a significant genomic resource that will enable the study of mosquitoes and the pathogens that they carry. https://doi.org/10.7554/eLife.82762.sa0 Decision letter Reviews on Sciety eLife's review process Introduction Mosquitoes top the list of vectors for arthropod-borne diseases, being implicated in the transmission of many human pathogens responsible for arboviral diseases, malaria, and lymphatic filariasis (WHO, 2017). Mosquito-borne viruses circulate in sylvatic (between wild animals) or urban (between humans) transmission cycles driven by different mosquito species with their own distinct host preferences. Although urban mosquito species are chiefly responsible for amplifying epidemics in dense human populations, sylvatic mosquitoes maintain the transmission of these viruses among forest-dwelling animal reservoir hosts and are involved in spillover events when humans enter their ecological niches (Valentine et al., 2019). Given that mosquito-borne virus emergence is preceded by such spillover events, continuous surveillance and virus discovery in sylvatic mosquitoes is integral to designing effective public health measures to pre-empt or respond to mosquito-borne viral epidemics. Metagenomics on field specimens is a powerful method in our toolkit to understand mosquito-borne disease ecology through the One Health lens (Webster et al., 2016). With next-generation sequencing becoming more accessible, such studies have provided unprecedented insights into the interfaces among mosquitoes, their environment, and their animal and human hosts. As mosquito-associated viruses are mostly RNA viruses, RNA sequencing (RNA-seq) is especially informative for surveillance and virus discovery. However, working with lesser studied mosquito species poses several problems. First, metagenomics studies based on RNA-seq are bedevilled by overabundant ribosomal RNAs (rRNAs). These non-coding RNA molecules comprise at least 80% of the total cellular RNA population (Gale and Crampton, 1989). Due to their length and their abundance, they are a sink for precious next-generation sequencing reads, decreasing the sensitivity of pathogen detection unless depleted during library preparation. Yet the most common rRNA depletion protocols require prior knowledge of rRNA sequences of the species of interest as they involve hybridizing antisense oligos to the rRNA molecules prior to removal by ribonucleases (Fauver et al., 2019; Phelps et al., 2021) or by bead capture (Kukutla et al., 2013). Presently, reference sequences for rRNAs are limited to only a handful of species from three genera: Aedes, Culex, and Anopheles (Ruzzante et al., 2019). The lack of reliable rRNA depletion methods could deter mosquito metagenomics studies from expanding their sampling diversity, resulting in a gap in our knowledge of mosquito vector ecology. The inclusion of lesser studied yet medically relevant sylvatic species is therefore imperative. Second, species identification based on morphology is notoriously complicated for members of certain species subgroups. This is especially the case among Culex subgroups. Sister species are often sympatric and show at least some competence for a number of viruses, such as Japanese encephalitis virus, St Louis encephalitic virus, and Usutu virus (Nchoutpouen et al., 2019). Although they share many morphological traits, each of these species have distinct ecologies and host preferences, thus the challenge of correctly identifying vector species can affect epidemiological risk estimation for these diseases (Farajollahi et al., 2011). DNA molecular markers are often employed to a limited degree of success to distinguish between sister species (Batovska et al., 2017; Zittra et al., 2016). To address the lack of full-length rRNA sequences in public databases, we sought to determine the 28S and 18S rRNA sequences of a diverse set of Old and New World sylvatic mosquito species from four countries representing three continents: Cambodia, the Central African Republic, Madagascar, and French Guiana. These countries, due to their proximity to the equator, contain high mosquito biodiversity (Foley et al., 2007) and have had long histories of mosquito-borne virus circulation (Desdouits et al., 2015; Halstead, 2019; Héraud et al., 2022; Jacobi and Serie, 1972; Ratsitorahina et al., 2008; Saluzzo et al., 2017; Zeller et al., 2016). Increased and continued surveillance of local mosquito species could lead to valuable insights on mosquito virus biogeography. Using a unique score-based read filtration strategy to remove interfering non-mosquito rRNA reads for accurate de novo assembly, we produced a dataset of 234 novel full-length 28S and 18S rRNA sequences from 33 mosquito species, 30 of which have never been recorded before. We also explored the functionality of 28S and 18S rRNA sequences as molecular markers by comparing their performance to that of the mitochondrial cytochrome c oxidase subunit I (COI) gene for molecular taxonomic and phylogenetic investigations. The COI gene is the most widely used DNA marker for molecular species identification and forms the basis of the Barcode of Life Data System (BOLD) (Hebert et al., 2003; Ratnasingham and Hebert, 2007). Presently, full-length rRNA sequences are much less represented compared to other molecular markers. However, given the availability of relevant reference sequences, 28S and concatenated 28S+18S rRNA sequences can be the better approach for molecular taxonomy and phylogenetic studies. We hope that our sequence dataset, with its species diversity and eco-geographical breadth, and the assembly strategy we describe would further facilitate the use of rRNA as markers. In addition, this dataset enables the design of species-specific oligos for cost-effective rRNA depletion for a broader range of mosquito species and streamlined molecular species identification during RNA-seq. Results Poor rRNA depletion using a non-specific depletion method During library preparations of mosquito samples for RNA-seq, routinely used methods for depleting rRNA are commercial kits optimised for human or mice samples (Belda et al., 2019; Bishop-Lilly et al., 2010; Chandler et al., 2015; Kumar et al., 2012; Weedall et al., 2015; Zakrzewski et al., 2018) or through 80–100 base pair antisense probe hybridisation followed by ribonuclease digestion (Fauver et al., 2019; Phelps et al., 2021). In cases where the complete reference rRNA sequence of the target species is not known, oligos would be designed based on the rRNA sequence of the closest related species (25, this study). These methods should deplete reads from the conserved regions of rRNA sequences. However, reads from the variable regions remain at abundances high enough to compromise RNA-seq output. In our hands, we have found that using probes designed for the Ae. aegypti rRNA sequence followed by RNase H digestion according to the protocol published by Morlan et al., 2012, produced poor depletion in Aedes albopictus, and in Culicine and Anopheline species (Figure 1), in which between 46% and 94% of reads post-depletion were ribosomal. Additionally, the lack of full-length reference rRNA sequences compromises the in silico clean-up of remaining rRNA reads from sequencing data, as reads belonging to variable regions would not be removed. To solve this and to enable RNA-seq metagenomics on a broader range of mosquito species, we performed RNA-seq to generate reference rRNA sequences for 33 mosquito species representing 10 genera from Cambodia, the Central African Republic, Madagascar, and French Guiana. Most of these species are associated with vector activity for various pathogens in their respective ecologies (Table 1). In parallel, we sequenced the mitochondrial COI gene to perform molecular species identification of our samples and to comparatively evaluate the use of rRNA as a molecular marker (Figure 2). Figure 1 Download asset Open asset Percentage of rRNA reads in mosquito total RNA sequencing (RNA-seq) data after depletion using probes antisense to Aedes aegypti sequences. Pools of five individual mosquitoes from genera Aedes (Ae), Culex (Cx), Mansonia (Ma), and Anopheles (An) were ribodepleted by probe hybridisation followed by RNase H digestion according to the protocol by Morlan et al., 2012. Y-axis depicts percentages of remaining rRNA reads calculated as the number of rRNA reads over total reads per sample pool. Depletion efficiency decreases with taxonomic distance from Ae. aegypti underlining the need for reference sequences for species of interest. Table 1 Mosquito species represented in this study and their vector status. Mosquito taxonomy‡Origin*Collection site (ecosystem type)Vector for†ReferenceAedes (Fredardsius) vittatusCFRural (village)ZIKV, CHIKV, YFVDiallo et al., 2020Aedes (Ochlerotatus) scapularisGFRural (village)YFVVasconcelos et al., 2001Aedes (Ochlerotatus) serratusGFRural (village)YFV, OROVCardoso et al., 2010; Romero-Alvarez and Escobar, 2018Aedes (Stegomyia) aegyptiCFUrbanDENV, ZIKV, CHIKV, YFVKraemer et al., 2019Aedes (Stegomyia) albopictusCF, KHRural (village, nature reserve)DENV, ZIKV, CHIKV, YFV, JEVAuerswald et al., 2021; Kraemer et al., 2019Aedes (Stegomyia) simpsoniCFRural (village)YFVMukwaya et al., 2000Anopheles (Anopheles) baezaiKHRural (nature reserve)Unreported–Anopheles (Anopheles) coustaniMG, CFRural (village)RVFV, malariaMwangangi et al., 2013; Nepomichene et al., 2018; Ratovonjato et al., 2011Anopheles (Cellia) funestusMG, CFRural (village)ONNV, malariaLutomiah et al., 2013; Tabue et al., 2017Anopheles (Cellia) gambiaeMG, CFRural (village)ONNV, malariaBrault et al., 2004Anopheles (Cellia) squamosusMGRural (village)RVFV, malariaRatovonjato et al., 2011; Stevenson et al., 2016Coquillettidia (Rhynchotaenia) venezuelensisGFRural (village)OROVTravassos da Rosa et al., 2017Culex (Culex) antennatusMGRural (village)RVFVNepomichene et al., 2018; Ratovonjato et al., 2011Culex (Culex) duttoniCFRural (village)Unreported–Culex (Culex) neaveiMGRural (village)USUVNikolay et al., 2011Culex (Culex) orientalisKHRural (nature reserve)JEVKim et al., 2015Culex (Culex) perexiguusMGRural (village)WNV, USUVVezenegho et al., 2022Culex (Culex) pseudovishnuiKHRural (nature reserve)JEVAuerswald et al., 2021Culex (Culex) quinquefasciatusMG, CF, KHRural (village, nature reserve)ZIKV, JEV, WNV, DENV, SLEV, RVFV, Wuchereria bancroftiBhattacharya and Basu, 2016; Maquart et al., 2021; Ndiaye et al., 2016; Serra et al., 2016Culex (Culex) tritaeniorhynchusMG, KHRural (village, nature reserve)JEV, WNV, RVFVAuerswald et al., 2021; Hayes et al., 1980; Jupp et al., 2002Culex (Melanoconion) spissipesGFRural (village)VEEVWeaver et al., 2004Culex (Melanoconion) portesiGFRural (village)VEEV, TONVTalaga et al., 2021; Weaver et al., 2004Culex (Melanoconion) pedroiGFRural (village)EEEV, VEEV, MADVTalaga et al., 2021; Turell et al., 2008Culex (Oculeomyia) bitaeniorhynchusMG, KHRural (village, nature reserve)JEVAuerswald et al., 2021Culex (Oculeomyia) poicilipesMGRural (village)RVFVNdiaye et al., 2016Eretmapodites intermediusCFRural (village)Unreported–Limatus durhamiiGFRural (village)ZIKVBarrio-Nuevo et al., 2020Mansonia (Mansonia) titillansGFRural (village)VEEV, SLEVHoyos-López et al., 2015; Turell, 1999Mansonia (Mansonioides) indianaKHRural (nature reserve)JEVArunachalam et al., 2004Mansonia (Mansonioides) uniformisMG, CF, KHRural (village, nature reserve)RVFV, Wuchereria bancroftiLutomiah et al., 2013; Ughasi et al., 2012Mimomyia (Etorleptiomyia) mediolineataMGRural (village)Unreported–Psorophora (Janthinosoma) feroxGFRural (village)ROCVMitchell et al., 1986Uranotaenia (Uranotaenia) geometricaGFRural (village)Unreported– * Dengue virus, DENV; Zika virus, ZIKV; chikungunya virus, CHIKV; Yellow Fever virus, YFV; Oropouche virus, OROV; Japanese encephalitis virus, JEV; Rift Valley Fever virus, RVFV; O’Nyong Nyong virus, ONNV; Usutu virus, USUV; West Nile virus, WNV; St Louis encephalitis virus, SLEV; Venezuelan equine encephalitis virus, VEEV; Tonate virus, TONV; Eastern equine encephalitis virus, EEEV; Madariaga virus, MADV; Rocio virus, ROCV. † Origin countries are listed as their ISO alpha-2 codes: Central African Republic, CF; Cambodia, KH; Madagascar, MG; French Guiana, GF. ‡ Subgenus indicated in brackets. Figure 2 Download asset Open asset Novel mosquito rRNA sequences were obtained using a unique reads filtering method. (A) Schematic of sequencing and bioinformatics analyses performed in this study to obtain full-length 18S and 28S rRNA sequences as well as cytochrome c oxidase I (COI) DNA sequences. Nucleic acids were isolated from mosquito specimens for next-generation (for rRNA) or Sanger (for COI) sequencing. Two in-house libraries were created from the SILVA rRNA gene database: Insecta and Non-Insecta, which comprises 8,585 sequences and 558,185 sequences, respectively. Following BLASTn analyses against these two libraries, each RNA-sequencing (RNA-seq) read is assigned a ratio of BLASTn scores to describe their relative nucleotide similarity to insect rRNA sequences. Based on these ratios of scores, RNA-seq reads can then be filtered to remove non-mosquito reads prior to assembly with SPAdes to give full-length 18S and 28S rRNA sequences. Image created with https://biorender.com/. (B) Based on their ratio of scores, reads can be segregated into four categories, as shown on this ratio of scores versus number of reads plot for the representative specimen ‘CF S27’: (i) reads with hits only in the Insecta library (shaded in green), (ii) reads with a higher score against the Insecta library (shaded in blue), (iii) reads with a higher score against the Non-Insecta library (shaded in yellow), and (iv) reads with no hits in the Insecta library (shaded in red). We applied a conservative threshold at 0.8, indicated by the black horizontal line, where only reads above this threshold are used in the assembly with SPAdes. For this given specimen, 175,671 reads (96.3% of total reads) passed the ≥0.8 cut-off, 325 reads (0.18% of total reads) had ratios of scores <0.8, while 6,423 reads (3.52%) did not have hits against the Insecta library. rRNA reads filtering and sequence assembly Assembling Illumina reads to reconstruct rRNA sequences from total mosquito RNA is not a straightforward task. Apart from host rRNA, total RNA samples also contain rRNA from other organisms associated with the host (microbiota, external parasites, or ingested diet). As rRNA sequences share high homology in conserved regions, Illumina reads (150 bp) from non-host rRNA can interfere with the contig assembly of host 28S and 18S rRNA. Our score-based filtration strategy, described in detail in the Materials and methods section, allowed us to bioinformatically remove interfering rRNA reads and achieve successful de novo assembly of 28S and 18S rRNA sequences for all our specimens. Briefly, for each Illumina read, we computed a ratio of BLAST scores against an Insecta library over scores against a Non-Insecta library (Figure 2A). Based on their ratio of scores, reads could be segregated into four categories (Figure 2B): (i) reads mapping only to the Insecta library, (ii) reads mapping better to the Insecta relative to Non-Insecta library, (iii) reads mapping better to the Non-Insecta relative to the Insecta library, and (iv) reads mapping only to the Non-Insecta library. By applying a conservative threshold at 0.8 to account for the non-exhaustiveness of the SILVA database, we removed reads that likely do not originate from mosquito rRNA. Notably, 15 of our specimens were engorged with vertebrate blood, a rich source of non-mosquito rRNA (Appendix 1—table 1). The successful assembly of complete 28S and 18S rRNA sequences for these specimens demonstrates that this strategy performs as expected even with high amounts of non-host rRNA reads. This is particularly important in studies on field-captured mosquitoes as females are often sampled already having imbibed a blood meal or captured using the human landing catch technique. We encountered challenges for three specimens morphologically identified as Mansonia africana (Specimen ID S33–S35) (Appendix 1—table 1). COI amplification by PCR did not produce any product, hence COI sequencing could not be used to confirm species identity. In addition, the genome assembler SPAdes (Bankevich et al., 2012) was only able to assemble partial length rRNA contigs, despite the high number of reads with high scores against the Insecta library. Among other Mansonia specimens, these partial length contigs shared the highest similarity with contigs obtained from sample ‘Ma uniformis CF S51’. We then performed a guided assembly using the 28S and 18S sequences of this specimen as references, which successfully produced full-length contigs. In two of these specimens (Specimen ID S34 and S35), our assembly initially produced two sets of 28S and 18S rRNA sequences, one of which was similar to mosquito rRNA with low coverage and another with 10-fold higher coverage and 95% nucleotide sequence similarity to a water mite of genus Horreolanus known to parasitize mosquitoes. Our success in obtaining rRNA sequences for mosquito and water mite shows that our strategy can be applied to metabarcoding studies where the input material comprises multiple insect species, provided that appropriate reference sequences of the target species or of a close relative are available. Altogether, we were able to assemble 122 28S and 114 18S full-length rRNA sequences for 33 mosquito species representing 10 genera sampled from four countries across three continents. This dataset contains, to our knowledge, the first records for 30 mosquito species and for seven genera: Coquillettidia, Mansonia, Limatus, Mimomyia, Uranotaenia, Psorophora, and Eretmapodites. Individual GenBank accession numbers for these sequences and specimen information are listed in Appendix 1—table 1. Comparative phylogeny of novel rRNA sequences relative to existing records To verify the assembly accuracy of our rRNA sequences, we constructed a comprehensive phylogenetic tree from the full-length 28S rRNA sequences from our study and relevant rRNA sequences from GenBank (Figure We applied a for GenBank sequences with at least 95% coverage of our sequence to as many species or genera as Although we found records for the species in our the resulting tree that our 28S sequences according to their respective species and by to at with the of and 28S rRNA sequences a with related sequences from Anopheles Anopheles and Anopheles high homology for or other members of (Figure in 28S rRNA sequences a to sister species Culex (Figure in Figure with 2 all Download asset Open asset 28S sequences from this study with or from existing GenBank phylogenetic tree based on full-length 28S sequences bp) from this study and from GenBank was using the method and constructed to in et al., 2018) using an Horreolanus species found among our samples as an at each from from GenBank are with and their accession numbers are For sequences from this each specimen information on and specimen ID specimens produced to two 28S this is indicated by the numbers 1 or 2 at the of the specimen genera are indicated by Culex in Anopheles in Aedes in Mansonia in in in in in in in and in at is Figure data 1 sequence of 28S rRNA sequences from this study and from GenBank Download 28S rRNA phylogenetic (Figure with GenBank Figure this study to that of 18S rRNA sequences (Figure 2). Although all rRNA trees show the of into in and other the phylogenetic relationships between the 28S and 18S rRNA trees and are The 18S rRNA tree also several taxonomic (i) the lack of by species the Culex (ii) the lack of between 18S rRNA sequences of and (iii) the of CF a Culex and (iv) the lack of a (Figure 2). However, 28S and 18S rRNA sequences are by in and should not be when concatenated 28S+18S rRNA sequences were from the specimens (Figure the phylogenetic tree resulting from these sequences more the 28S tree (Figure with to the of the the with in tree in 28S rRNA in concatenated 28S+18S rRNA For were higher in the concatenated tree compared to the 28S the 28S+18S rRNA tree an from genera yellow), Aedes blue), and driven by the inclusion of 18S rRNA sequences. also the found in the 18S rRNA tree and to the close between Culex and Mansonia relative to the 28S tree (Figure the Culex and Mansonia genera are no in the concatenated 28S+18S rRNA tree (Figure Culex is with to of genus Mansonia (Figure and which we to be Mansonia a distinct in 28S or 18S rRNA thus representing a of Figure with 2 all Download asset Open asset 28S and 18S rRNA sequences phylogenetic relationships that are with with higher 28S sequences This phylogenetic tree based on concatenated 28S+18S rRNA sequences bp) from this study was using the method and constructed to using et al., 2018) using an Horreolanus species found among our samples as an at each from specimen information on indicated in and specimen ID specimens produced to two 28S+18S rRNA this is indicated by the numbers 1 or 2 at the of the specimen genera are indicated by Culex in Anopheles in Aedes in Mansonia in in in in in in and in at is Figure data 1 sequence of 122 28S rRNA sequences, two sequences from Horreolanus Download Figure data 2 sequence of 114 18S rRNA sequences, two sequences from Horreolanus Download The concatenated 28S+18S rRNA tree (Figure is known the of our specimens, (i) the of from (ii) the of genus Anopheles into two Anopheles and (iii) the of genus Aedes into and (iv) the of the the Culex genus and 2016). rRNA as a molecular marker for taxonomy and phylogeny We sequenced a of the COI gene to confirm morphological species identification of our specimens and to compare the functionality of rRNA and COI sequences as molecular markers for taxonomic and phylogenetic investigations. COI sequences were able to determine the species in most specimens for the COI sequences from our of specimen shared high nucleotide similarity with several other Anopheles species such as the most and closest In the case of Ae. three specimens had been morphologically identified as Ae. their COI sequences similarity to that of Ae. As GenBank no records of Ae. COI at the of this we aligned the Ae. COI sequences against two sister species of Ae. Ae. and Ae. We found they shared only and respectively. Given this significant we these specimens to be Ae. were especially among Culex specimens belonging to the or where the sequence with of the top two hits by a For between and of the and between and of the Among our three specimens of two to to a species that is different from related to We that these specimens could be based on morphological similarity were not able to verify this by molecular as no COI reference sequence is for this species. These specimens are hence as ‘Ma

A score-based read selection strategy enables the assembly of novel full-length ribosomal RNA sequences for mosquitoes, which improves the physical and computational removal of interfering ribosomal RNA reads in RNA-seq and provides another molecular marker for taxonomic and phylogenetic inquiries.

Background, aim, and scope Forest plantations, widely grown for wood production, involve the selective promotion of single-tree species or replacement of natural species by exotic tree species. Slash pine (Pinus elliottii) has been chosen for reforestation of infertile sandy soils in southeast Queensland, Australia. These exotic pine plantations minimize soil and water losses and are important scientific study sites. The soil environment of these plantations, though devoid of sufficient nutrients, organic carbon and other factors, harbors innumerable bacteria that may play a crucial role in maintaining soil quality and ecosystem functions. These soil microorganisms also have the potential for use as sensitive biological indicators to reflect environmental changes. It is therefore essential to understand the interrelationships among bacterial communities and their environment by assessing their structural and functional diversity and their responses to disturbances. The main aim of our investigation was to determine the diversity of bacterial communities in forest litters and soil during the forest leaf litter decomposition using culturedependent and culture-independent techniques. Materials and methods A 25-cm (diameter)×40-cm core sample was collected and fractionated into three subsamples designated E1 (L leaf litter layer), E2 (F leaf litter layer), and E5 (0–10 cm soil layer). Both culture-dependent and culture-independent methods were applied in this study. In the culture-independent study, a strategy of wholecommunity DNA extraction, polymerase chain reaction (PCR) amplification followed by cloning and 16S rDNA sequence analysis was used; for culture-dependent study, the strategy included sample plating and bacteria isolating, DNA extraction, PCR amplification, and 16S rDNA sequence analysis. The diversity similarities between two bacterial communities and two methods are quantified using Jensen–Shannon divergence. Results From culture-dependent study, 336 colonies in total were isolated and grouped from the three subsamples, and the 16S rRNA sequence analysis from a representative isolate from each morphogroup (21 isolates) indicated that they belonged to the phyla Actinobacteria, Firmicutes, and Proteobacteria. Culture-independent assessment based on 16S rRNA gene library comprising 194 clones revealed that members of the phylum Actinobacteria were absent in the culture-independent studies. Clones in libraries from E1 consisted exclusively of members of the Firmicutes. The majority of clones from E2 were related to Firmicutes (79%) and Proteobacteria (21%). Clones derived from E5 were mostly affiliated with Acidobacterium (42%), followed by unclassified bacteria (27%), Verrucomicrobiales (12%), Proteobacteria (11%), and Planctomycetes (8%). Discussion This study showed that bacterial culturabilities in different fractions of leaf litters were similar, and both of them were higher than the bacterial culturability in the soil. Unculturable bacterial diversity in the soil, however, was much higher than the leaf litter bacterial diversity. The bacterial diversity on the top layer of leaf litters was slightly

Pathogenic intestinal spirochetes of swine include Serpulina hyodysenteriae, a strongly beta-hemolytic spirochete that causes swine dysentery, and S. pilosicoli, a weakly beta-hemolytic intestinal spirochete (WBHIS) that causes porcine colonic spirochetosis. Because of the existence of nonpathogenic WBHIS in the normal swine colon, it is important to develop laboratory procedures for accurate identification of S. pilosicoli. The purpose of the present study was to assess hippurate hydrolysis and polymerase chain reaction (PCR) amplification of specific 16S ribosomal RNA (rRNA) sequences for identification of porcine S. pilosicoli. Additionally, the enteropathogenicity of 8 field isolates of porcine S. pilosicoli was determined by challenge exposure of 1-day-old chicks and sequential histologic examination of the cecal mucosa. The field isolates of porcine S. pilosicoli hydrolyzed hippurate and yielded S. pilosicoli-specific products by PCR amplification of 16S rRNA sequences. Although all of the field isolates of porcine S. pilosicoli attached to the cecal epithelium of challenge-exposed chicks by day 21 postinoculation, some isolates had locally invasive phenotypes. We concluded that identification of porcine S. pilosicoli could be made on the basis of results of hippurate hydrolysis and 16S rRNA PCR amplification. Challenge inoculation of 1-day-old chicks followed by histologic examination of the cecal mucosa demonstrated the enteropathogenicity of porcine S. pilosicoli.

In patients with acute leukaemia, studies of minimal (i.e. submicroscopic) residual disease (MRD) should improve measurements of treatment response and enable estimates of the residual leukaemic cell burden during clinical remission, thereby improving the selection of therapeutic strategies and, possibly, long-term clinical outcome. The most useful methods for MRD monitoring currently available are polymerase chain reaction (PCR) amplification of fusion transcripts and rearranged antigen-receptor genes, and flow cytometric detection of aberrant immunophenotypes. Several studies in patients with acute lymphoblastic leukaemia and acute myeloid leukaemia have demonstrated that MRD is a powerful and independent prognostic indicator. The strong association between MRD and risk of relapse was observed in children and adult patients irrespective of the methodology used to detect residual disease. This article discusses the relative advantages and disadvantages of each MRD assay, and reviews the reported correlations between MRD, clinical and biological features of the disease, and outcome. A multitude of factors influence treatment response in patients with acute leukaemia (Lowenberg et al, 1999; Pui et al, 2001). Cell lineage, stage of maturation, karyotype and molecular abnormalities regulate expression of genes that control drug metabolism and apoptosis, and determine the leukaemic cells' capacity to grow and their sensitivity to chemotherapy. Other important factors include size of the tumour burden, dosage of drugs and their interaction, pharmacokinetic and pharmacogenetic variables, and compliance with treatment schedule. These parameters are associated with a varying risk of relapse but their predictive power is far from absolute and their use to make treatment decisions in individual patients is inherently limited. In vivo measurements of leukaemia cytoreduction should reflect the combined effect of clinical and cellular variables; rather than predicting outcome, these measurements provide direct information on the effectiveness of treatment in each patient. However, estimates by conventional morphological techniques have limited sensitivity and accuracy: in most cases, leukaemic cells can be detected in bone marrow with certainty only when they constitute 5% or more of the total cell population. Such a high detection threshold results in the inability to measure fluctuations in the leukaemia tumour mass with accuracy, and resurgent disease can be diagnosed only at its most advanced stages. Methods for detecting MRD (i.e. submicroscopic) are at least 100-times more sensitive than conventional morphological techniques and allow a more stringent definition of 'remission' in patients with acute leukaemia, which is rapidly becoming the standard at many cancer centres (Fig 1). Factors that influence response to treatment in patients with acute leukaemia. An essential premise of MRD studies is that MRD levels provide reliable estimates of the residual leukaemia mass. This assumes that leukaemic cells are homogeneously distributed throughout the bone marrow so that measurements of MRD performed on a small aliquot of bone marrow (perhaps, equivalent to one thousandth of the total active marrow) are representative of the total leukaemia burden. However, observations in patients (Mathe et al, 1966) and in animal models of leukaemia (Martens et al, 1987) indicate that there could be considerable heterogeneity in the distribution of leukaemic cells after treatment and that individual samples may not be informative. Another caveat is that MRD signals may not correspond to viable leukaemic cells with a capacity for cell renewal. Cell viability can only be determined with methods that use intact cells (e.g. flow cytometry) but it is impossible to determine with MRD methods that examine nucleic acid material (e.g. polymerase chain reaction). Moreover, even when viable, leukaemic cells may lack sustained self-renewal capacity, a feature that is not possible to assess on a routine basis. As discussed in this article, most of these reservations have been dispelled by the results of several correlative studies of MRD and treatment outcome. It is now clear that detectable MRD implies the presence of leukaemic stem cells capable of leading to disease recurrence. The first MRD studies in patients with leukaemia were made soon after antibodies for leucocyte differentiation antigens became available. Strong expression of the common acute lymphoblastic leukaemia (ALL) antigen (CD10) and terminal deoxynucleotidyl transferase (TdT) in ALL cells, and apparent absence of these markers on normal peripheral blood cells, suggested the use of these molecules as markers of leukaemia. However, it was soon clear that a proportion of cells in the bone marrow (i.e. B-cell progenitors) expressed both CD10 and TdT (Janossy et al, 1980). The distinction between leukaemic lymphoblasts and normal lymphoid progenitors remains crucial for detecting MRD in patients with B-lineage ALL. In early studies, it was also noted that T-lineage ALL cells (and normal thymocytes) expressed TdT and T-cell markers whereas lymphoid cells in bone marrow and peripheral blood did not (Bradstock et al, 1981). This finding enabled the first productive MRD studies in patients with acute leukaemia, and remains the basis of MRD studies by immunological techniques in T-lineage ALL. Over the following two decades, many methods to study MRD have been tested (Campana & Pui, 1995; Szczepanski et al, 2001). In ALL, the most reliable methods include flow cytometric profiling of aberrant immunophenotypes, polymerase chain reaction (PCR) amplification of fusion transcripts and chromosomal breakpoints, and PCR amplification of antigen-receptor genes. In acute myeloid leukaemia (AML), only the first two of these methods can be applied, as most patients lack antigen-receptor gene rearrangements. Owing to limited sensitivity (approximately 1–5%), conventional karyotyping and fluorescence in situ hybridization (FISH) are occasionally useful for clarifying the nature of morphologically suspicious blast cells, but cannot reliably detect submicroscopic leukaemia (Mancini et al, 2000). However, improvements in image analysis technology, allowing simultaneous visualization of morphological, immunophenotypic and FISH features, may enhance the usefulness of this approach (Bielorai et al, 2002). The success of methods based on differential properties of normal and leukaemic cells in culture has been limited to a few laboratories (Estrov et al, 1986; Uckun et al, 1993). Many comprehensive reviews have addressed in detail the methodological aspects of MRD detection (Campana & Pui, 1995; van Dongen et al, 1999; Foroni et al, 1999; Lo Coco et al, 1999a; Szczepanski et al, 2001; Campana & Coustan-Smith, 2002; Liu & Grimwade, 2002). Table I and the following sections summarize the applicability and the main specific advantages and disadvantages of each technique. Breakpoint fusion regions of chromosomal aberrations, and rearranged immunoglobulin (IG) and T-cell receptor (TCR) genes are leukaemia-specific sequences that have been used with consistent success in molecular studies of MRD. The value of another target, WT-1, is supported by some studies (Cilloni et al, 2002; Ogawa et al, 2003). However, WT-1 is also expressed by normal haematopoietic progenitors (Hosen et al, 2002) and has not been found to be reliable by some investigators (Elmaagacli et al, 2000). Internal tandem duplications (ITD) of the FLT3 gene occur in approximately 20–30% of AML patients and, in principle, could be used as targets for PCR-based MRD studies (Stirewalt et al, 2001). However, FLT3/ITD detected at diagnosis appear to be unstable, often becoming undetectable at relapse (Kottaridis et al, 2002; Shih et al, 2002). With the exception of the TAL1 gene abnormalities, the genomic breakpoints of the most common known leukaemia fusion genes are spread over large distances within each gene locus. PCR analysis starting from DNA would require determination of the exact breakpoint in each patient, which is not practical (van Dongen et al, 1999). As the resulting mRNA is similar in many patients, RNA is the typical starting material for PCR-mediated amplification of breakpoint fusion sequences. After reverse transcription (RT) into cDNA, PCR amplification is applied using primers at opposite sites of the breakpoint fusion region (van Dongen et al, 1999). An advantage of using the breakpoint-fusion regions for MRD studies is their stability during the disease course. However, only 50% or less of ALL and AML cases in children and adults have specific chromosomal aberrations with well-defined breakpoint fusion regions (Look, 1997; Liu & Grimwade, 2002). Therefore, the applicability of this approach is restricted to certain subgroups of leukaemias. The uniqueness of each immunoglobulin (Ig) and TCR molecule depends on the rearrangement and joining of the multiple variable (V), diversity (D) and joining (J) gene segments of the IG and TCR gene loci, on the deletion of nucleotides from the germline sequences of the rearranging gene segments, and on the random insertion of nucleotides at the junctional sites (Foroni et al, 1999; Pongers-Willemse et al, 1999). Thus, the sequences of the junctional regions of rearranged IG and TCR genes are signatures of each lymphoid cell clone, normal or malignant. For MRD studies, the various IG and TCR gene rearrangements must be identified in each patient at diagnosis. The sequence information enables the design of junctional region-specific oligonucleotides, which can be used as primers in the PCR procedure to specifically amplify the rearrangements of the malignant clone (Pongers-Willemse et al, 1999) or as probes to distinguish PCR products derived from leukaemic cells among those that are derived from normal lymphoid cells (Yokota et al, 1991). Virtually all B-lineage ALL patients have rearranged IGH genes (van Dongen & Wolvers-Tettero, 1991). In addition, rearrangements of the IGK deleting element (Kde) occur at a relatively high frequency (approximately 60%) (Beishuizen et al, 1997). Most T-ALL patients have rearranged TCRB, TCRG and/or TCRD genes (van Dongen & Wolvers-Tettero, 1991), and cross-lineage TCR rearrangements are found in many patients with B-lineage ALL (Szczepanski et al, 1999). In the majority (approximately 90%) of B-lineage ALL patients, MRD can be revealed by junctional regions of IGH, IGK-Kde, TCRG and/or TCRD gene rearrangements (Pongers-Willemse et al, 1999), and in most (> 95%) T-ALL patients by TCRB, TCRG and/or TCRD (Kneba et al, 1995; Pongers-Willemse et al, 1999). The need to develop individual probes or primers is one of the main limiting factors in the widespread application of MRD studies by PCR amplification of IG and TCR genes. Some investigators have attempted to bypass this need by identifying the leukaemic DNA on the basis of size and signal intensity after separation by high-resolution gel electrophoresis (Deane & Norton, 1990; Sykes et al, 1997). Polyclonal background levels vary but usually limit the sensitivity of this approach to the detection of one leukaemic cell among 103 normal cells. IG and TCR gene rearrangements in B- and T-lineage ALL are prone to subclone formation and multiple IGH gene rearrangements are already found at diagnosis in 30% to 40% of B-lineage ALL patients (Beishuizen et al, 1994). This creates uncertainty about the prioritization of the clones that should be monitored in some patients. In addition, the emergence of subclones that are not apparent at diagnosis may occur, carrying the risk of false-negative results during MRD monitoring (van Dongen & Wolvers-Tettero, 1991; Beishuizen et al, 1994; Pongers-Willemse et al, 1999). In a recent analysis of 94 patients with B-lineage ALL, studied at diagnosis and relapse, 71% of the potential Ig and TCR targets for MRD analysis identified at diagnosis were preserved at relapse (Szczepanski et al, 2002). The most stable were IGK-Kde rearrangements and the least stable were incomplete TCRD rearrangements. Monoclonal rearrangements were significantly more stable than oligoclonal rearrangement. For these reasons, it has been recommended that at least two PCR targets should be used. In B-lineage ALL, these are available in approximately 70% of children and 50% of adults; in T-lineage ALL, two PCR targets (including TAL1 deletions) can be identified in approximately 90% of children and 85% of adults (Pongers-Willemse et al, 1999). Notably, clonotypic rearrangements of IG and TCR genes are found in only 50% of infants with t(4;11) ALL (Peham et al, 2002). Depending on the uniqueness of the sequence targeted and the quality of the material, PCR can detect one leukaemic cell in 103−106 normal cells. High sensitivity may paradoxically become a problem as it generates a propensity to false-positive results due to contamination, particularly when the same set of primers is applied to different patients. However, investigators are well aware of this potential problem and most take measures to minimize it. A complication of MRD studies by PCR is related to the limited quantitative power of the technique. Nevertheless, under typical conditions and using appropriate techniques, the quantification of leukaemic cells by PCR amplification of single-copy genes (e.g. IGH and TCR genes) can be adequate (Sykes et al, 1992; Cave et al, 1994; Ouspenskaia et al, 1995; Pongers-Willemse et al, 1998; Neale et al, 1999). When RNA is the target molecule, additional potential pitfalls may render the correlation more imprecise. RNA is prone to degradation, and the efficiency of its initial conversion to cDNA by reverse transcriptase may vary. For example, it was reported that, using standard techniques, less than 1000 PML-RARA molecules could be obtained from 1 µg of diagnostic bone marrow RNA derived from approximately 106 acute promyelocytic leukaemia (APL) cells (Seale et al, 1996). Poor yield of PML-RARA cDNA would then lead to low sensitivity of the RT-PCR. Moreover, the number of transcripts per cell is unlikely to be homogeneous in all the leukaemic cell population and is also unlikely to remain stable in cells exposed to chemotherapy. Finally, levels of transcript expression in patients with the same disease may differ considerably (Krauter et al, 1999; Buonamici et al, 2002), which may affect the consistency of MRD measurements in a patient population. MRD is traditionally quantified by comparing the PCR product obtained in the test sample with that of the patient's leukaemic cell DNA or RNA serially diluted into DNA or RNA from normal cells. Efforts to enhance the precision of the assay by competitive PCR or limiting dilution analysis have been effective (Sykes et al, 1992; Cave et al, 1994; Ouspenskaia et al, 1995) but the increased complexity of these approaches may hinder their routine application. Real-time quantitative PCR (RQ-PCR) appears to have solved some of the complications associated with PCR quantification. A fluorescent reporter is used in the PCR, and accumulation of fluorescence during the reaction ('real-time') is measured: the increase in fluorescence is proportional to the amount of target amplicon synthesized. Results are compared with those of serial dilutions of diagnostic material. This methodology can be applied to both breakpoint fusion region (Pallisgaard et al, 1999; Chen et al, 2001; de Haas et al, 2002) and antigen-receptor genes (Pongers-Willemse et al, 1998). In the latter case, to reduce the costs associated with designing fluorescent probes that match patient-specific sequences, probes matching germ line segments, such as V (Donovan et al, 2000; Verhagen et al, 2000), J (Bruggemann et al, 2000) and Kde regions (van der Velden et al, 2002a), and applicable to multiple patients can be used. Alternatively, some investigators have bypassed the requirement for fluoresceinated probes by using the DNA intercalating dye SYBR green I as a fluorescent reporter (Nakao et al, 2000; Li et al, 2002). In patients with T-lineage ALL, MRD can be monitored by searching for cells expressing TdT and CD3 or other cell markers in bone marrow or peripheral blood (Campana & Coustan-Smith, 2002) (Fig 2). In B-lineage ALL and AML, one needs to identify aberrant phenotypes that are not expressed by normal bone marrow or peripheral blood cells (Fig 2). Therefore, MRD studies in these leukaemias are complicated by variations in the cellular composition and immunophenotype of normal bone marrow that occur with age and exposure to various agents. For example, proportions of early lymphoid progenitors (or 'haematogones') are low in the bone marrow of healthy adults and especially low in patients receiving corticosteroids or chemotherapy (Paolucci et al, 1979). In contrast, these cells are found in high proportions in young children and in patients after transplantation or chemotherapy (Lucio et al, 1999; van Lochem et al, 2000; Van Wering et al, 2000; McKenna et al, 2001). These conditions may uncover normal cells expressing phenotypes that are undetectable in studies of healthy individuals. Nevertheless, immunophenotypes that clearly distinguish B-lineage ALL cells from normal lymphoid progenitors and haematogones have been identified (Campana & Coustan-Smith, 2002). Immunophenotypic differences between leukaemia cells and normal bone marrow cells. Flow cytometric dot plots shows expression of markers typically used for detecting MRD in T-lineage ALL, B-lineage ALL and AML (top row), and the expression of the same markers in bone marrow cells from healthy individuals (middle row) and from patients recovering after chemotherapy (bottom row). Dashed circles enclose areas of the dot plot corresponding to leukaemic cells in each case. Immunophenotypic analysis of T-lineage ALL cells was done on CD3+ cells, analysis of B-lineage ALL cells on CD19+ cells and analysis of AML cells on CD33+ and/or CD34+ cells (markers that were expressed in virtually all leukaemic cells at diagnosis). The analysis of the corresponding normal control subjects was done on the same cell subsets. Detection of MRD by flow cytometry in AML presents some specific difficulties. Owing to their immunophenotypic heterogeneity, AML cells usually spread across many areas of the dot plot instead of forming the tight cluster typical of ALL cells (Fig 2). Therefore, with any given marker combination, only a fraction of cells may be phenotypically abnormal. In addition, AML cells often have light scattering properties similar to those of normal cells with high autofluorescence. These features introduce complexity in the analysis, and may reduce the sensitivity of the assay. Nevertheless, sensitive MRD detection in AML is feasible. In a recent study using four-colour flow cytometry, 26 of 54 (48%) children with AML had leukaemia cells expressing immunophenotypes that allowed measurement of MRD with a sensitivity of one leukaemic cell among 104 or more normal cells; another 20 patients (37%) had immunophenotypes that enabled the detection of one leukaemic cell among 103 cells (unpublished observations). In adult AML, the proportion of patients that can be studied with a high degree of sensitivity may be larger. In one study, 46 of 53 patients had phenotypes that were found at frequencies of less than one in 104 cells in normal bone marrow, while seven had phenotypes found in normal bone marrow but at frequencies of less than one in 103 (San Miguel et al, 1997). In another study, 65 of 93 patients had a phenotype for detection of one leukaemic cell in 104 normal cells et al, 2000). The of that enable the analysis of gene expression has to identify markers of leukaemia. The results of one of studies et al, that a of the gene of normal and leukaemic cells identify applicable markers for MRD studies in both ALL and AML, and should allow the design of for reliable and monitoring of MRD. of the main of false-positive MRD results by flow cytometry is the use of markers to distinguish leukaemic cells from normal cells. The of needs to be by studies of bone marrow and peripheral blood cells not only from healthy individuals but also from patients at various of treatment (Campana & Coustan-Smith, 2002). The use of immunophenotypic that only in samples at certain during treatment a high risk of the influence of individual in and treatment compliance on normal is A of false-negative MRD results is the of immunophenotypic et al, 1998; et al, 2001). In the of this on MRD results can be by using multiple of markers in each patient. main influence MRD detection by flow the degree of morphological and between target cells and the cells, and the number of cells that can be As discussed immunophenotypes that not with the normal of leukaemic cells must be The number of cells that can be for each set of markers in clinical samples is usually less than 1 a cluster of at least is to flow cytometric the sensitivity of the assay under these would be (Campana & Coustan-Smith, 2002). Therefore, a sensitivity of (or one leukaemic cell in 104 normal should be during routine MRD even allowing for varying of available cells and immunophenotypes that are not expressed in of leukaemic cells. are various aspects of the procedure that need and have been discussed in detail (Campana & Coustan-Smith, 2002). it to that which may have minimal during routine of leukaemia, may be a of when MRD. signals can from conditions of the sample and of antibodies or to cells (Campana & Coustan-Smith, 2002). flow cytometry and PCR amplification of IGH genes, studied serial dilutions of normal and leukaemic cells and found the two methods to be et al, 1999). then bone marrow samples from children with ALL in clinical et al, 1999). In both techniques detected MRD levels 1 in The of leukaemic cells by the two methods well the had MRD levels 1 in Results were in only two PCR detected two in 104 and in 104 leukaemic cells, whereas the flow cytometric assay was both patients were MRD by both and remain in clinical after of compared the results obtained by using a of antibodies the MRD marker with those of PCR amplification of IGH genes in samples obtained from patients at various during et al, (unpublished In 46 MRD was by PCR analysis and by flow cytometric contrast, leukaemic cells were detected by both methods in and the MRD estimates by the two methods were The two methods results in only of the one was by PCR to have leukaemic cells, but MRD was detected by flow cytometric analysis of the two other samples were by analysis to have leukaemic cells but by PCR to have less than and (unpublished observations). A study comparing the results of flow cytometry and PCR amplification of TCRG and TCRD genes to detect MRD in bone marrow samples from patients with ALL found results in were more during the early of and were to low and presence of PCR et al, 2001). A study comparing flow cytometry to detection of transcripts in bone marrow samples from patients with ALL in observed results in samples et al, 2000). In two samples the assay was while cells were detectable by flow the samples had leukaemic cells by flow cytometry but signal by RT-PCR. also compared the results of flow cytometry with the results of amplification of fusion transcripts and observations). The methods results in of bone marrow samples of children with B-lineage ALL in clinical had MRD and were MRD the two one was by flow cytometry but by the other was by flow cytometry but by PCR patient had MRD that was detectable by both methods in and A similar was performed with samples obtained from 20 children with AML during treatment (unpublished observations). The molecular abnormalities studied were and In a of residual disease of these samples also had residual cells detectable by flow cytometry, whereas one with to levels of residual disease by did leukaemic were not detected by The samples had undetectable leukaemic transcripts or signals corresponding to levels of residual disease than but seven of these with undetectable leukaemic transcripts and with residual disease by had residual disease by flow The variable of MRD detection by flow cytometry in in and by to and the limited quantitative capacity of the conventional used can the observed A correlation between flow cytometric results and PCR detection of WT-1 was in a recent et al, 2002). Several studies in ALL have the of MRD at different during treatment (Fig there are due to differences in chemotherapy these studies have demonstrated the clinical of MRD. In one study, MRD was in patients by a competitive PCR assay junctional sequences of IGH and TCR et al, 1998). The absence or presence and of residual leukaemia during the first of were significantly with the risk of early relapse at each of the with leukaemic cells after the of or those with at had a particularly high risk of Another study monitored MRD in children with ALL to of the (van Dongen et al, 1998). This study also used PCR analysis of IGH and TCR genes as well as TAL1 patients had relapse at than those were MRD at the various at MRD levels at the of treatment and treatment were associated with a relapse when compared with patients with a low degree of MRD, and with a to relapse when compared with patients. MRD information from the first two was particularly allowing the of different risk a of patients with a relapse of a of patients with a relapse of and with a relapse of of MRD during treatment in children with ALL. the of patients, to the studies areas correspond to patients with MRD in each were obtained from et and results for the studies, from Cave et for the for and of study, and from van Dongen et for the used flow cytometry to study MRD in children with diagnosed ALL in a chemotherapy et al, found that detectable MRD (i.e. leukaemic at each of of and and of was significantly associated with a relapse (Fig with high levels of MRD at the of the or at

Comparative analyses of 16S rRNA clone libraries represent a standard tool in microbial ecology. Chimeric sequences are generally removed prior to such comparisons. A literature survey re- vealed a general pattern: (1) most commonly a single chimera identification approach (CIA) has been used; (2) putative chimeras have routinely been discarded without manual examination; (3) chimera fil- tered datasets have been submitted to repositories. To explore the effects of various CIAs on the study of microbial β-diversity relationships using complete primary data, 4 bacterial and 4 archaeal clone li- braries were generated from a submarine spring and analyzed together with 3 bacterial and 3 archaeal published primary datasets. The primary datasets were compared with their 8 different CIA filtered datasets using Chimera_check, CCODE, Pintail, Chimera Slayer and Bellerophon, the last with 4 dif- ferent settings. When CIA filtered datasets were pooled according to the CIA used, no significant dif- ferences between them could be observed, although there was not complete congruency between the different CIAs. When CIA filtered datasets of the same clone library were compared, generally no sig- nificant differences could be observed. In contrast, when CIA filtered datasets of different clone libraries were compared, the statistical significance of the relationships shifted from significant to in- significant or vice-versa in many cases depending on the CIA used. This precludes a correct identifica- tion of β-diversity. To solve this problem, we treated all CIA filtered datasets and primary data of a single clone library as CIA replicates in non-parametric MANOVA. This enabled unambiguous delin- eation of environmental samples by taking into account all CIA introduced data modifications.

To develop a sensitive method for the specific detection of HIV-1 RNA. Following reverse transcription, the presence of HIV-1 RNA can be detected by polymerase chain reaction (PCR) amplification. Since specific detection of HIV-1 RNA may be complicated by contamination with minute quantities of HIV-1 DNA, samples are treated with deoxyribonuclease (DNase) prior to analysis. This additional step increases the possibility of RNA degradation and sample contamination. A primer, HG141, was designed to hybridize to the poly(A) tract present in HIV-1 genomic and all HIV-1 messenger (m) RNA with its 5' end and to the region upstream of the poly(A) tract with its 3' end. The increased stability of the HG141 primer/HIV-1 RNA or complementary (c) DNA complex, enabled PCR amplification to be performed with HG141 and the return primer HG62 at an annealing temperature above the melting temperature (Tm) of the primer-HIV-1 DNA complex. After reverse transcription of samples obtained from HIV-1-infected H9 cells, HG62/141-primed PCR amplification specifically detected HIV-1 RNA sequences without the need for DNAase pre-treatment. This technique was more sensitive for the detection of HIV-1 RNA than SK38/39-primed PCR amplification of DNase-treated samples. Since the presence of HIV-1 RNA is indicative of HIV-1 replication for the presence of HIV-1 virions, the RNA-specific primer described should facilitate the assessment of HIV-1 replication and the plasma HIV-1 viral load in HIV-1-infected individuals. This should prove useful in the evaluation of the effects of therapeutic interventions on HIV-1 infection.

In the past decade, significant advances have been made in the ability to image the heart with radionuclide tracers under stress and resting conditions in patients with suspected or known coronary artery disease (CAD) for the detection of ischemia, determination of prognosis, assessment of myocardial viability, preoperative risk assessment for patients undergoing noncardiac surgery, and evaluation of the efficacy of revascularization in patients undergoing coronary artery bypass surgery or an interventional procedure.1 For many years, planar imaging and SPECT with 201Tl constituted the only scintigraphic techniques available for detecting CAD and assessing prognosis in patients undergoing stress perfusion imaging. The major limitation of 201Tl scintigraphy is the high false-positive rate observed in many laboratories, which is attributed predominantly to image attenuation artifacts and variants of normal that are interpreted as defects consequent to a significant coronary artery stenosis. Although quantification of 201Tl images improves specificity, the false-positive rate remains problematic, particularly in women and in obese patients. Breast attenuation artifacts in women are sometimes difficult to distinguish from perfusion abnormalities secondary to inducible ischemia or myocardial scar. In recent years, new 99mTc-labeled perfusion agents have been introduced into clinical practice to enhance the specificity of SPECT and to provide additional information regarding regional and global left ventricular systolic function via ECG gating of images. It was immediately apparent that the quality of images obtained with these new 99mTc-labeled radionuclides was superior to that of images obtained with 201Tl because of the more favorable physical characteristics of 99mTc imaging with a gamma camera. With 99mTc, doses of ≈10 to 20 times higher than those that are feasible with 201Tl can be administered, yielding images with higher count density. 99mTc demonstrates less scatter and attenuation than 201Tl, which is associated …

To facilitate our study of the molecular basis for the estrogen receptor (ER) subtype selectivity of novel ligands, we used DNA shuffling to construct chimeric ERs having ligand binding domains derived from both ER alpha and ER beta. The efficiency of chimera generation was low with traditional DNA shuffling protocols. Furthermore, ER ligand binding domain sequences lack convenient restriction sites for introducing chimeric ligand binding domain sequences into expression vectors. To overcome these problems, we developed a modified strategy whereby chimeric sequences were exclusively amplified from among the reassembled products from DNA shuffling using a special pair of PCR primers whose 3' ends specifically match the alpha and beta sequences, respectively, and whose 5' ends match sequences outside the ER beta ligand binding domain. When chimeric ligand binding domain DNA sequences, amplified with these primers, were co-transformed into a yeast strain with a linearized expression vector for ER beta, an active expression vector was produced by homologous recombination. Twenty-two different crossover sites were found; most occurred when there was a stretch of eight or more identical base pairs in both sequences, and many were concentrated in the regions important for studying ligand binding and transactivation. This method should prove to be useful for generating chimeric gene products from parent templates that share relatively low sequence identity.

DAO Diseases of Aquatic Organisms Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials DAO 24:185-189 (1996) - doi:10.3354/dao024185 16S rRNA targeted RT-PCR for the detection of Vibrio penaeicida, the pathogen of cultured kuruma prawn Penaeus japonicus Genmoto K, Nishizawa T, Nakai T, Muroga K Vibrio penaeicida is the causative bacterium of vibriosis in cultured kuruma prawn Penaeus japonicus in Japan. To develop a specific and sensitive method for the detection of the pathogen, a species-specific sequence in the 16S rRNA of V. penaeicida was determined and a polymerase chain reaction (PCR)-based method was devised on the basis of the sequence. Prior to sequencing, a part of the variable regions of the 16S rRNA was amplified by using primers designed from 2 conserved regions according to previously reported data on Vibrionaceae. The region of the 16S rRNA (nucleotide numbers 440 to 490 in Escherichia coli 16S rRNA) obtained by this procedure was found to be species-specific for V. penaeicida. It was confirmed that PCR and RT (reverse transcription)-PCR amplifications with a sense primer designed from the V. penaeicida-specific sequence were both able to differentiate V. penaeicida from other prawn-pathogenic vibrios. 16S rRNA-targeted RT-PCR was demonstrated to have 100 times higher sensitivity than 16S rDNA-targeted PCR and 10 fg of total nucleic acids extracted from cultured bacterial cells was sufficient to yield the visible fragment in gel electrophoresis. These results indicate that RT-PCR amplification with this primer is useful for specific and sensitive detection of V. penaeicida. Vibrio penaeicida . rRNA . PCR . Kuruma prawn . Vibriosis Full text in pdf format PreviousNextExport citation RSS - Facebook - Tweet - linkedIn Cited by Published in DAO Vol. 24, No. 3. Publication date: March 07, 1996 Print ISSN:0177-5103; Online ISSN:1616-1580 Copyright © 1996 Inter-Research.

Structure-based RNA multiple alignment is particularly challenging because covarying mutations make sequence information alone insufficient. Existing tools for RNA multiple alignment first generate pairwise RNA structure alignments and then build the multiple alignment using only sequence information. Here we present PMFastR, an algorithm which iteratively uses a sequence-structure alignment procedure to build a structure-based RNA multiple alignment from one sequence with known structure and a database of sequences from the same family. PMFastR also has low memory consumption allowing for the alignment of large sequences such as 16S and 23S rRNA. The algorithm also provides a method to utilize a multicore environment. We present results on benchmark data sets from BRAliBase, which shows PMFastR performs comparably to other state-of-the-art programs. Finally, we regenerate 607 Rfam seed alignments and show that our automated process creates multiple alignments similar to the manually curated Rfam seed alignments. Thus, the techniques presented in this paper allow for the generation of multiple alignments using sequence-structure guidance, while limiting memory consumption. As a result, multiple alignments of long RNA sequences, such as 16S and 23S rRNAs, can easily be generated locally on a personal computer. The software and supplementary data are available at http://genome.ucf.edu/PMFastR.