Plesiorycteropus) has remained one of Madagascar’s least well-understood extinct species since its discovery in the 19th century. Initially considered a close relative of the aardvark (Orycteropus) within the order Tubulidentata, more recent morphological analyses challenged this placement on the grounds that the identifiably derived traits supporting this allocation were adaptations to digging rather than shared ancestry. Because the skeletal evidence showed many morphological traits diagnostic of different eutherian mammal orders, they could not be used to resolve its closest relatives. As a result, the genus was tentatively assigned its own taxonomic order ‘Bibymalagasia’, yet how this order relates to other eutherian mammal orders remains unclear despite numerous morphological investigations. This research presents the first known molecular sequence data for Plesiorycteropus, obtained from the bone protein collagen (I), which places the ‘Malagasy aardvark’ as more closely related to tenrecs than aardvarks. More specifically, Plesiorycteropus was recovered within the order Tenrecoidea (golden moles and tenrecs) within Afrotheria, suggesting that the taxonomic order ‘Bibymalagasia’ is obsolete. This research highlights the potential for collagen sequencing in investigating the phylogeny of extinct species as a viable alternative to ancient DNA (aDNA) sequencing, particularly in cases where aDNA cannot be recovered.Madagascar is well known for its diverse fauna and flora, being home to many species not found anywhere else in the world. However, its biodiversity in the recent past included a range of extinct enigmatic fauna, such as elephant birds, giant lemurs and dwarfed hippopotami. The ‘Malagasy aardvark’ ( Plesiorycteropus) is one of the few recently extinct Madagascan mammals that remains poorly understood and its phylogenetic placement within eutherian mammals unclear The island of Madagascar had a wide range of enigmatic Quaternary fauna that are now extinct, including ‘giants’  and ‘dwarfs’ (dwarf and pygmy hippopotami). The variations in size are considered an evolutionary response to insularisation th century. Lamberton Plesiorycteropus displayed the greatest number of resemblances to aardvarks, but subsequently described a series of features strikingly different from tubulidentates, noting that these other features were more akin to those in pangolins, others to armadillos and anteaters and ultimately proposed several hypotheses. These included that it is 1) a ‘palaeontological chimera’ composed of characteristics to distantly related mammals; 2) it is representative of a single type of mammal showing convergently evolved characteristics of distantly related mammals with uncertain affinity; or 3) it is a member of a deep mammalian lineage that, isolated by insularisation, retained ancestral traits that have since been lost in derivative lineages facing more intense selection pressures. Several further studies accepted Plesiorycteropus as a tubulidentate, but also remained uncertain as to its phylogenetic placement Plesiorycteropus was indeed only from one type of mammal, and was confident in its systematic position within Tubulidentata considering comparisons with xenarthrans as phylogenetically distracting. Throughout most of the early 20th century, it was regarded as an ‘edentate’  or more specifically as a tubulidentate and therefore most closely related to the extant aardvark .The first known collected specimen of this unique digging creature was found at Belo in western Madagascar but remained poorly described th century, this association was challenged on the grounds that many of the identifiably derived traits of the skeleton of Plesiorycteropus, particularly those associated with scratch-digging, are shared not only by aardvarks but a number of other mammalian groups, including the armadillos (Dasypodidae), pangolins (Manidae), anteaters (Myrmecophagidae), and many others Plesiorycteropus that had no obvious connection with digging, the ones that could be adequately documented were also found not to be unique to aardvarks but instead sharing traits with various groups from ‘Condylarthra’, a paraphyletic grouping within the ungulates Plesiorycteropus and any other group within the ungulates could be confidently identified. Rather than making Plesiorycteropus the only recent mammal without a recognised order, a new order was created for it termed ‘Bibymalagasia’ Plesiorycteropus made it difficult to ascertain which of the apparently derived features document its true affinity However, towards the end of the 20st century were the first phylogenies to include Plesiorycteropus that sampled widely across mammals PlesiorycteropusOrycteropus albeit with little support. Although more recent molecular studies on extant species has changed our understanding of phylogenetic relationships among eutherian mammals, such as the pseudoungulates  Plesiorycteropus has remained elusive.Towards the beginning of the 21Plesiorycteropus’ phylogenetic placement can be re-investigated. Given the recent revisions in systematics using molecular sequence data Plesiorycteropus as more closely related to any of the former ‘Edentata’ group including the aardvarks, armadillos, anteaters or pangolins; Patterson’s tubulidentate hypothesis Plesiorycteropus and Orycteropus forming a monophyletic group with Orycteropus; whereas MacPhee’s ‘Bibymalagasia’ hypothesis Plesiorycteropus placed separate from all known orders, and not most closely related to Tubulidentata. To resolve the molecular phylogeny of Plesiorycteropus, sequence data was obtained for bone type 1 collagen from the fossil specimen and compared with all previously hypothesized closest relatives.With novel methods in extracting molecular sequence data from fossils, particularly the sequencing of collagen using soft-ionization mass spectrometry m/z) determined  producing ‘MS spectra’. These peptides can then be further fragmented into smaller peptide fragments and these fragment ions are further analysed by the mass analyzer and recorded as ‘MS/MS spectra’ . These MS/MS spectra are then either sequenced manually, or compared with the expected fragmentation patterns of known amino acid sequences within a given protein sequence database  using a specialist search engine , the ‘ion scores’ being indicative of the relative quality of the sequence match.In brief, mass spectrometry is an analytical technique in which molecules are converted to gaseous ions that are subsequently separated in a mass spectrometer and detected. ‘Soft-ionization’ techniques are where the evaporation and ionisation of the molecules into the gaseous phase are carried out without extensive fragmentation, and thus peptides can be analysed (sequenced) without breaking apart into constituent amino acids. The two most commonly used soft-ionization techniques are Matrix Assisted Laser Desorption Ionization (MALDI) and Electrospray Ionization (ESI) ‘Shotgun proteomics’ techniques are ideal for the analysis of ancient samples because typical protein isolation techniques, such as gel electrophoresis, are often rendered ineffective due to protein degradation. This is because collagen, making up approximately 90% of the total protein in bone, progressively degrades in a way that produces a vast range of collagen fragments that are unpredictable in size Plesiorycteropus as a pioneering example of the application of proteomics techniques to palaeobiology.Several proteins, such as albumin and osteocalcin, have been investigated in fossil material as potential alternatives for obtaining phylogenetic information Plesiorycteropus as well as modern samples of postulated close relatives. The sub-fossil Plesiorycteropus madagascariensis sample was drilled from femur specimen Oxford University Museum (OUM) 14395, which was collected by Paul Methuen from Antolambiby, Madagascar and presented to the Oxford University Museum of Natural History in 1911. A modern bone specimen aardvark (Orycteropus afer) was also obtained from OUM. Additional modern bone specimens obtained from University Museum of Zoology Cambridge (UMZC) included Sunda pangolin (Manis javanica) (UMZC E.1308), tamandua (Tamandua tetradactyla) (UMZC E.583), giant anteater (Myrmecophaga tridactyla) (UMZC E.562), hairy armadillo (Chaeotophractus villosus) (UMZC E.1062), golden mole (Ambylosomus hottentotus) (NFC-2), greater hedgehog tenrec (Setifer setosus) (UMZC 2011.2.2) and rock hyrax (Procavia capensis) (UMZC_H.4891.C). A skin cut from modern elephant shrew (Petrodromus tetradactylus) (A.533) was also obtained from Manchester Museum. LC-MS data for American mastodon (Mammut americanum) was taken from a previous publication Protein sequencing analyses were carried out on sub-fossil specimens of 2O)/1% buffer B (0.1% FA in acetonitrile (ACN)) to 25% B in 45 min at 200 nL min−1. Peptides were selected for fragmentation automatically by data dependent analysis. Proteomics data files were searched using Mascot against a local database that incorporates UniProt with sequences identified from MALDI-ToF/ToF-MS/MS using two missed cleavages, error tolerances of 0.5 Da (MS and MS/MS) using ‘Error tolerant’ searches with the two  variable modifications, oxidation of Proline (P) and Lysine (K) that search the Unimods database for further modifications that could occur as a result of amino acid substitution or amino acid modification during diagenesis. Following revision of the sequences from these results, the searches were carried out as normal searches but with pyro-glutamic acid, oxidation ) and deamidation of asparagine (N) and glutamine (Q) modifications. LC-MS collagen peptide sequence matches were selected with a peptide ion score >40 and are listed with their respective ion scores in the Supplementary Information (Soluble proteins were extracted from modern and fossil samples by agitation in 0.6 M hydrochloric acid (HCl) for 4 h and exchanged into 50 mM ammonium bicarbonate using ultrafiltration (30 kDa molecular weight cut-off (MWCO)). The proteins were then digested by trypsin (37°C for 18 h), acidified to 0.1% trifluoroacetic acid (TFA) and further purified and concentrated by C18 ziptip following ormation –S11.The collagen α1(I) and α2(I) sequences were concatenated and aligned with orthologues from 11 LC-MS datasets as well as 26 other species Plesiorycteropus PMF was compared with those of other mammalian groups in order to identify some of the dominant peptides present that differed between the taxa, particularly the highly variable ‘species biomarker’ peptides described previously Plesiorycteropus and tenrecs, golden moles and elephant shrews, and between aardvark and hyrax could be observed in these PMFs. However, to get robust statistical support, LC-MS sequencing and subsequent phylogenetic analysis was carried out. Selected peptides with sufficiently high peak intensities in the MALDI-ToF PMFs were analysed by MS/MS for sequence analysis  from the LC-MS analyses were manually aligned and analysed by Maximum Likelihood ML;  and Neigrecoidea .One major limitation of protein (peptide) sequencing by soft-ionization mass spectrometry relies on the matching of peptide fragments ions observed in tandem mass spectra with those predicted from a given database. Thus for fragment ion mass spectra deriving from extinct taxa that do not have closely-related representative species, levels of confidence need to be placed on inference from such data. In the case of collagen (I) analyses, the typical sequence coverage for unconstrained searches is approximately 70–80%, which reduces to ∼50% when appropriate (∼40) ion scores are used. Thus with increasing confidence thresholds in peptide matching using conventional approaches  there is an observable increase in the amount of missing sequence information.Plesiorycteropus within Afrotheria, and more specifically as being more closely related to tenrecs than any other previously postulated species (Bootstrap score = 81). Because it consistently forms a monophyletic grouping with tenrecs within a clade that includes the golden moles, these results not only support the order Tenrecoidea (golden moles and tenrecs) but include Plesiorycteropus as a new member of this order. The close relationship consistently observed between hyracoids and tubulidentates has long been considered on morphological grounds The sequence analyses  consistePlesiorycteropus is more closely related to tenrecoids than to tubulidentates, and that because its collagen sequence confidently places it within the order Tenrecoidea, the molecular phylogeny obtained does not support the creation of the taxonomic order ‘Bibymalagasia’ that was proposed in the mid 1990s Plesiorycteropus retained some morphological characteristics from its earlier common ancestor with aardvarks, characters such as its relatively small eyes and adaptations to climbing However, the results clearly show that Plesiorycteropus as well as many other extinct taxa. These results suggest that this mysterious Plesiorycteropus is likely a form of ‘giant’ tenrec, a diverse family that are now only represented by much smaller taxa. The results presented here demonstrate that fossil collagen (I) can be used to resolve such questions of taxonomy and, given its longevity over millions of years, has enormous potential to build a more complete Tree of Life than possible with DNA sequencing alone.The inconclusive results from multiple investigations over the past one hundred years Figure S1MALDI-ToF-MS spectra showing the PMFs of collagen digests extracted from elephant shrew (Petrodromus tetradactylus), golden mole (Ambylosomus hottentotus), Sunda pangolin (Manis javanica), anteater (Myrmecophaga tridactyla) and hairy armadillo (Chaeotophractus villosus).(TIF)Click here for additional data file.Figure S2Tandem MS spectra of nine homologous peptides with precursor ions at m/z 2161 (Sunda pangolin), 2147 (anteaters), 2129 (Plesiorycteropus), 2131 (hairy armadillo), 2089 (aardvark), 2073 (rock hyrax), 2145 (golden mole), 2161 (elephant shrew) and 2115 (elephantids) split into two windows of m/z range 200–1100 (A) and 1100–2000 (B).(TIF)Click here for additional data file.Figure S3Bayesian analysis of the 37 collagen sequences used in this study rooted to Gallus including the LC-MS-derived sequences.(TIF)Click here for additional data file.Table S1Mascot results for Manis bone acid-insoluble protein digest LC-MS data.(DOCX)Click here for additional data file.Table S2Mascot results for Orycteropus bone acid-insoluble protein digest LC-MS data.(DOCX)Click here for additional data file.Table S3Mascot results for Tamandua bone acid-insoluble protein digest LC-MS data.(DOCX)Click here for additional data file.Table S4Mascot results for Petrodromus skin acid-insoluble protein digest LC-MS data.(DOCX)Click here for additional data file.Table S5Mascot results for Ambylosomus bone acid-insoluble protein digest LC-MS data.(DOCX)Click here for additional data file.Table S6Mascot results for Chaetophractus bone acid-insoluble protein digest LC-MS data.(DOCX)Click here for additional data file.Table S7Mascot results for Plesiorycteropus bone acid-insoluble protein digest LC-MS data.(DOCX)Click here for additional data file.Table S8Mascot results for Setifer bone acid-insoluble protein digest LC-MS data.(DOCX)Click here for additional data file.Table S9Mascot results for Procavia bone acid-insoluble protein digest LC-MS data.(DOCX)Click here for additional data file.Table S10Mascot results for Myrmecaphaga bone acid-insoluble protein digest LC-MS data.(DOCX)Click here for additional data file.Table S11Mascot results for Mammut bone acid-insoluble protein digest LC-MS data.(DOCX)Click here for additional data file.Text S1Collagen (I) sequence accession numbers.(DOCX)Click here for additional data file.Text S2Concatenated collagen sequences.(DOCX)Click here for additional data file.


Cell-based high-throughput RNAi screening has become a powerful research tool in addressing a variety of biological questions. In RNAi screening, one of the most commonly applied assay system is measuring the fitness of cells that is usually quantified using fluorescence, luminescence and absorption-based readouts. These methods, typically implemented and scaled to large-scale screening format, however often only yield limited information on the cell fitness phenotype due to evaluation of a single and indirect physiological indicator. To address this problem, we have established a cell fitness multiplexing assay which combines a biochemical approach and two fluorescence-based assaying methods. We applied this assay in a large-scale RNAi screening experiment with siRNA pools targeting the human kinome in different modified HEK293 cell lines. Subsequent analysis of ranked fitness phenotypes assessed by the different assaying methods revealed average phenotype intersections of 50.7±2.3%–58.7±14.4% when two indicators were combined and 40–48% when a third indicator was taken into account. From these observations we conclude that combination of multiple fitness measures may decrease false-positive rates and increases confidence for hit selection. Our robust experimental and analytical method improves the classical approach in terms of time, data comprehensiveness and cost. Large-scale RNA-interference (RNAi) screening has become a widely used approach in invertebrate model organisms and in cell culture. RNAi screening has the power to resolve the architecture and dynamic regulation of cellular signalling pathways and can help to identify genetic interactions involved in human diseases Intracellular ATP [ATP]i serves as an energy carrier that drives virtually all cell functions. Persistent ATP depletion causes a cell to die and in turn cell death is indicated by low ATP levels. Due to its simple accessibility e.g. by an ATP-dependent luciferase-luciferin reaction [ATP]i has been a long-serving indicator of cellular viability. Cellular metabolism creates a continuous demand of energy that requires permanent energy supply. Variation in metabolic activity results in fluctuation of [ATP]i. For example, [ATP]i varies markedly during cell differentiation In healthy cells the cytoplasmic membrane effectively separates the intracellular fluid from the outside environment. It represents an impermeable barrier for charged fluorescent dyes but is permeable for uncharged and hydrophobic compounds such as Calcein acetoxymethyl (AM). Upon permeation of the cytoplasmic membrane, non-fluorescent Calcein-AM is hydrolyzed by intracellular esterases and the product Calcein, a hydrophilic, strongly fluorescent compound remains inside the cell. Thus, Calcein stained cells have esterase activity and in consequence an intact membrane. Due to its low cytotoxicity and simple handling Calcein-AM is a well-suited and commonly employed fluorescent probe for staining viable cells Nucleus or DNA stain using fluorescent molecules, such as Hoechst 33342, Hoechst 33258, DAPI or other dyes have been long-serving and commonly applied indicators of cellular viability. Hoechst 33342 exhibits distinct fluorescence emission upon binding into the minor groove of DNA and stains condensed as well as normal chromatin of living or dead cells. The fluorescence intensity of DNA stain is directly proportional to nuclear DNA content and can therefore be used to monitor DNA replication during cell cycle and cell division. When applied to assess cellular viability the total Hoechst fluorescence signal or the number of cell exhibiting DNA stain is quantified Any of the mentioned approaches yield information on specific cellular conditions and physiological characteristics which only partially and indirectly reflect cellular viability. In fact, none of the described methods clearly and exclusively reflects cellular viability comprehensively. The same is true for other methods assessing cellular viability such as measurement of redox potential by MTT or MTS, the utilization of charged dyes such as trypan blue or propidium iodide or comparable methods. This might explain why striking candidates selected from large-scale screening approaches require re-testing in time-consuming, independently and sequentially conducted secondary experiments to eliminate false-positive hits. To overcome these limitations, we aimed to assess cellular viability by a ‘multiplexing’ approach which combines different methods in a single experiment.Conventional high-throughput cell-based viability assays, typically run individually and in serial mode, are time and cost intensive and lead to variations. Multiplexing, i.e. the combination of different assaying methods in the same experiment, adds a level of efficiency by reducing sample supply, reducing cell culture and assay consumable requirements. Redundant steps for sample preparation, plate replication, and assay execution are eliminated and experimental variation is reduced.web cellHTS2In this article, we describe a methodology based on a multiplexing approach for synthetic lethality screening to address the issues encountered in the classical assaying methods described above. We aimed to create a viability multiplexing assay which combines multiple indicators in the same experiment, thus decreasing variability and increasing efficiency, throughput and confidence for hit selection. We further aimed to conduct a case study and to measure the quality of the assay using the software For generation of stable, tetracycline-inducible HEK-293 cell lines (Invitrogen), Huntingtin poly Q tract of 23  and poly Q tract of 79  proteins were subcloned into a gateway compatible pcDNA5/FRT/TO vector, according to the manual of the Flp-In system (Invitrogen) and as reported in 2 in a humidified incubator according to standard procedures and used for RNAi screening when approximately 80% confluent.HEK293 cells were maintained in DMEM (Invitrogen) with 10% fetal bovine serum (Invitrogen) and supplemented with penicillin (100 U/ml)/streptomycin (100 mg/ml) (Invitrogen). Media were additionally supplemented with Hygromycin B (100 µg/mL), Blasticidin S (15 µg/mL), and tetracycline (4 µg/mL).Cell lines were cultured at 37°C, 5% CO2. All dispensing steps were performed with a Multidrop Combi dispensing system (Thermo).A sub-library of the genome-wide siRNA library siGENOME  targeting the human kinome was used for RNAi screening. The library contained 779 siRNA pools, consisting of four synthetic siRNA duplexes each (dissolved in RNAse free water). Prior to RNAi screening the siRNA pools as well as additional positive and negative siRNA controls with known phenotypes were distributed into white 384-well multititer plates (Greiner) and stored at −20°C until the experiment. The siRNA library was arrayed in multititer plates using a Biomek FX200 liquid handling system (Beckman Coulter). Each well contained 5 µl of a 200 nM pool of four synthetic siRNA duplexes. Library siRNAs were spotted in columns 5–24; the remaining columns were used for controls. Positions E04 and F04 contained a non-targeting siRNA pool as negative control. Positions K04 and L04 contained a siRNA pool targeting WEE1 as positive siRNA transfection control. The gene WEE1 plays a key role in cell cycle and causes cell death when silenced by RNAi Calcein-AM is a membrane permeable fluorescein derivative able to diffuse into cells. Upon cell entry Calcein–AM is hydrolyzed and thereby modified into the green fluorescent Calcein. This fluorescent signal can be monitored using appropriate filter sets (485 nm excitation/530 nm emission wavelength). Importantly, after modification the molecule remains locally restricted to the cytoplasm and is no longer able to diffuse out of the cell. The fact that Calcein-AM is able to enter only intact cells makes it use to a valuable tool to discriminate viable from dying cells. Thus, the fluorescent signal generated from the assay is directly proportional to the total amount of living cells in a given sample The blue fluorescent Hoechst 33342 (hereafter to be referred to as Hoechst) dye is a cell permeable nucleic acid stain that has multiple applications, including sensitive determination of cell number The CellTiter-Glo Luminescent Cell Viability Assay is a method to determine the number of viable cells in culture based on quantification of the ATP present, which signals the presence of metabolically active cells. The assay procedure involves adding a single reagent directly to cells which results in cell lysis and generation of a luminescent signal proportional to the amount of ATP present. The amount of ATP is considered to be directly proportional to the number of cells present in culture.2. Upon fluorescence labelling the staining solution was entirely removed from the wells using a 24-channel wand (VP Scientific) and was replaced by 10 µl DMEM without phenol-red (Invitrogen). Immediately after solution exchange fluorescence intensity of Hoechst stain was measured using a Mithras LB940 plate reader (Berthold Technologies) with 355 nm excitation and 460 nm emission filter set . Subsequently Calcein fluorescence was recorded using the same plate reader with 485 nm excitation and 535 nm emission filter set . The ‘CellTiter-Glo Luminescent Cell Viability Assay Kit’ (Promega) was used according to the manufacturer's instructions and was diluted in DMEM without phenol-red in a ratio of 1∶1. 10 µl of this mixture was added to the cells and incubated for 10 min at room temperature. Subsequently, luminescence intensity was measured using a Mithras LB940 plate reader . All dispensing steps were performed with a Multidrop Combi dispensing system (Thermo). Screens were performed in duplicate.For assessing cell fitness using fluorescence reporters the cells were incubated in 50 µl cell culture media (DMEM) containing 10 µM Hoechst 33342 (Invitrogen) and 10 µM Calcein-AM (Sigma) for one hour at 37°C, 5% COweb cellHTS2. web cellHTS2 is a computational tool for statistical analysis of high-throughput screening data sets. The software provides tools for data normalization, quality control, such as correlation analysis, measures of dynamic range of the screening assay as well as scoring (Z-scores) and intuitive visualization of scored screening results. For generation of hit lists quantification data was first normalized to the plate median. Subsequently plate replicates were averaged and cell fitness phenotypes were ranked with lowest mean values indicating strongest phenotypes. Ranked phenotypes from multiplexed cell lines were then subjected to comparative analysis where genes were considered as hits when confirmed by all employed fitness indicators. The full web cellHTS2 data sets containing raw values and annotation files are provided in Plate reader data was statistically analyzed using the in-house built software We have established an assay for cell fitness multiplexing by combining a biochemical, luminescence-based approach  and two fluorescence-based assay types . The biochemical method assesses cellular fitness by quantifying ATP levels, Calcein labelling reflects cell fitness through membrane integrity and indirect measurement of ATP-dependent enzymatic esterase activity. Hoechst DNA stain correlates cell fitness with cellular DNA content. The experimental workflow is shown in 2 = 0.99±0.44; CTG, R2 = 0.99±1.3) whereas fluorescence intensities recorded from double stained cells in the Calcein channel show a non-linear relationship and less correlation (R2 = 0.95±5.4).To evaluate the correlation of cell number to fluorescence/luminescence intensity we seeded HEK293 cells at densities between 0–10000 cells per well into white 384-well plates and cultured the cells overnight. The next day cells were loaded with Calcein-AM, with Hoechst 33342 and with a mixture of Calcein AM and Hoechst 33342 for 1 h at 37°C. For determination of cellular background fluorescence a population of cells remained unstained. Upon loading of fluorescence indicators the staining solution was completely removed from the wells and was replaced by cell culture media without phenol-red to reduce light scattering and background fluorescence. In this configuration Calcein and Hoechst signals were measured sequentially using a plate reader. For assessing cell fitness via intracellular ATP level CTG was added to the cells and luminescence intensity was recorded using the same plate reader see , at 535 To determine the optimal timing conditions for fluorescence and luminescence recordings we conducted time course experiments with the individual cell fitness assays. For that HEK293 cells were seeded at a density of 10000 cells per well, incubated overnight and subsequently prepared for cell fitness multiplexing as shown in web cellHTS2. web cellHTS2. The color-coded representation facilitates fast and intuitive interpretation of screening results even from large data sets where hundreds of multititer plates are used in the same experiment. Warm colours (red) indicate large Z-scores and decreased viability whereas cold colours (blue) and low Z-scores represent unaffected or increased viability relative to the median of all samples per 384-well plate. The image plots indicate low sample variation and excellent performance of functional positive and negative siRNA controls. To assess replicate reproducibility we analysed replicate correlation for both cell lines measured with three fitness indicators. 2±SD) for all fitness indicators and cell lines are summarized in In a preparatory step prior to RNAi screening pre-distributed and frozen siRNA pools  of hits identified in two or three channels was calculated as measure of hit confirmation  is a progressive neurodegenerative genetic disorder, which affects muscle coordination and leads to cognitive decline and dementia. The disease is caused by an autosomal dominant mutation in either of an individual's two copies of a gene called Huntingtin (HTT). The human Huntingtin gene consists of 67 exons which encode a protein of 3144 amino acids and is ubiquitously expressed throughout all human tissues. Although its cellular functions are not yet fully understood, Huntingtin has been implicated in transcription, apoptosis, lipid metabolism, Wnt signalling, trafficking, mitochondria and calcium homeostasis, cell cycle and other cellular processes While recombinant expression of mutant HTT protein revealed no difference in viability compared to cells expressing the wild type protein (data not shown) we suspected that a second mutation, mimicked by gene silencing, might cause synthetic lethality, thus uncovering mutant HTT specific genetic interactions. We therefore analysed the loss-of-function phenotype of genes encoding human kinases in both recombinant cell lines. To identify mutant HTT protein-specific synthetic lethality we first selected 25 top-ranking genes from the three assaying methods and calculated the intersection of co-occurring viability phenotypes. From 25 selected top-ranking fitness phenotypes a total of 10 (40%) in the HTT-Q23 cell line and 12 (48%) in the HTT-Q79 cell line were parallel identified by all fitness indicators. In a next step we analyzed the intersection of fitness phenotypes in both recombinant cell lines see  and chosNone of the identified genes were further confirmed by re-testing with multiple independent siRNAs in independent assays. Thus, the identified genes are being considered as ‘primary hits’ hits - in terms of the classical approach where only a single indicator is assessed - until validation by functional analysis.To assess the comparability of the employed fitness indicators we calculated the average overlap of 25 top-ranking phenotypes identified by two and by three assaying methods. 50.7±2.3% (HTT-Q23) and 58.7±14.4%  of the selected viability phenotypes were confirmed by a second fitness indicator. When a third readout channel was included in the analysis the percentage of co-occurring fitness phenotypes decreased to 40% (HTT-Q23) and 48% (HTT-Q79). All of the employed detection channels are comparable for reproducing hits as described above and as shown in For the sake of feasibility, throughput and cost reasons, cell-based viability or fitness screening in high-throughput mode is often conducted by single channel readout approaches with the result of limited reliability regarding the confidence of identified genes and false-positive discovery rate. The initial benefit is often compromised by subsequent time-consuming and cost-intensive re-screens for hit-confirmation and validation. To address this issue, we have developed a multiplexing approach for assessment of cellular viability that is cost-effective, reliable and that is advantageous for high-throughput fitness screening for several reasons. First, this assay is low-, medium- to high-throughput compatible, thus allowing for efficient and thorough screening. Second, the approach makes use of easy-to-use reagents, circumventing the need for more complicated assays such as those involving transfection plasmids encoding for luminescence-based fitness indicators. Third, this assay reveals robust phenotypes confirmed by comparative analysis of multiple readout channels.We applied the assay in a large-scale RNAi screening experiment with siRNA pools targeting the human kinome in HEK293 cells expressing wild type and mutant Huntingtin protein and we identified a number of synthetic lethal genetic interactions relevant to Huntington's disease. None of the identified genes were further confirmed by re-testing with multiple independent siRNAs in independent assays. One gene (MAPK8) was reported to be directly associated with Huntington's disease Our comparative analysis revealed a false-positive discovery rate of 39.7–41.3% for the single readout-based assay and a rate of 52–60% for a double readout-based assaying approach that is conducted using three different viability indicators. These results show the limitation of the classical approach and strongly imply that single channel measurements comprise a substantial level of uncertainty.The observed degree of phenotype intersection in comparative analysis is reproducible and comparable between the employed fitness marker and the reported decrease in co-occurring phenotypes along with increasing number of parallelized measures cannot be ascribed to poor hit reproducibility of a single viability indicator. In fact, the large proportion of identified false-positive hits meets the expectation for the employed fitness markers quite well. The applied probes are strongly biased when applied to quantify cell fitness as they assess different biological properties. The indicators yield information on specific cellular conditions and physiological characteristics which only partially and indirectly reflect cellular viability. CellTiter-Glo assesses intracellular ATP content, Calcein fluorescence reflects ATP-dependent esterase cleavage activity and Hoechst stain indicates nuclear DNA content. None of the used indicators clearly and exclusively reflect cellular viability comprehensively. The high degree in false-positive discovery rate is therefore an expected result of the individual markers that presumably represent real biological differences. In another experimental context, this probe-specific and additional information which was not further analyzed in the present study could be exploited in more detail to add a level of biological content besides assessment of cellular viability.Our results suggest that the extension of the classical approach by implementation of a second indicator is sufficient to significantly increase quality and robustness of screening data. The application of a third viability marker further improves confidence for hit selection and helps to remove false-positives but the advantage is less striking and the cost-benefit ratio needs careful consideration when applied routinely. In the context of our proof-of-concept study, conducted to evaluate the comparability of different viability indicators, employment of a third fitness marker was beneficial and helped to identify synthetic lethal genetic interactions relevant to in HD. Presumably, such an extension is advantageous for a multitude of other screening projects but implementation of additional probes for multiplexing could also be compromising or disadvantageous. For example when screening costs and preparation and reading time of existing assays increase disproportionality. The CellTiter-Glo reagent is expensive compared to Hoechst DNA stain or Calcein-AM ester and can dramatically increase assay reagent expenditures and large-scale screening approaches using libraries that contain numerous samples would of course be affected to a higher degree than low to medium-throughput experiments. Another aspect that needs careful consideration when employing multiple indicators is potential perturbation of primary fluorescent or luminescent probes by chemical or optical interference. Thus, careful evaluation and optimization upon implementation of additional indicators is strongly suggested to avoid unexpected impairment of established assaying techniques. The decision of whether a third indicator is advantageous for an existing screening approach strongly depends on the individual assay conditions, the biological question to be addressed and of course the available instrumental infrastructure and, hence, can only be made in case-specific manner and upon careful consideration of the mentioned criteria.In summary, our experimental and analytical method improves the classical cell viability assaying approaches in terms of time, data comprehensiveness and cost. Based on our observations it is very tempting to state that fitness multiplexing and subsequent comparative analysis of multi-parametric data enables accelerated hit confirmation compared to the classical approaches and eliminates the need for additional re-testing using independent assays. Due to the fact that the employed fitness indicators assess different biological properties, they also provide supplementary biological content that was discarded in the present study by exclusion of false-positive hits but that could be exploited in another biological context. It is important to mention that the applicability of the described method depends on the individual experimental setup, on the number of samples to be tested, the available instrumental infrastructure and the biological question to be assessed.While this work focuses on RNAi screening, our method could also be adapted for other strategies, such as small molecule or combined RNAi and small molecule screening. Altogether, this work contributes to furthering the applicability of cell-based high-throughput fitness screening.File S1The folder ‘Archive S1.7z’ contains all raw data and annotation files used for this manuscript.(7z)Click here for additional data file.


CLU, CR1, PICALM and BIN1. In this study, we conducted a genome-wide association study in an independent set of 1034 cases and 1186 controls using the Illumina genotyping platforms. By coupling our data with available GWAS datasets from the ADNI and GenADA, we replicated the original associations in both PICALM (rs3851179) and CR1 (rs3818361). The PICALM variant seems to be non-significant after we adjusted for APOE e4 status. We further tested our top markers in 751 independent cases and 751 matched controls. Besides the markers close to the APOE locus, a marker (rs12989701) upstream of BIN1 locus was replicated and the combined analysis reached genome-wide significance level (p = 5E-08). We combined our data with the published Harold et al. study and meta-analysis with all available 6521 cases and 10360 controls at the BIN1 locus revealed two significant variants  in limited linkage disequilibrium (r2 = 0.05) with each other. The independent contribution of both SNPs was supported by haplotype conditional analysis. We also conducted multivariate analysis in canonical pathways and identified a consistent signal in the downstream pathways targeted by Gleevec . We further tested variants in CLU, PICALM, BIN1 and CR1 for association with disease progression in 597 AD patients where longitudinal cognitive measures are sufficient. Both the PICALM and CLU variants showed nominal significant association with cognitive decline as measured by change in Clinical Dementia Rating-sum of boxes (CDR-SB) score from the baseline but did not pass multiple-test correction. Future experiments will help us better understand potential roles of these genetic loci in AD pathology.Recent GWAS studies focused on uncovering novel genetic loci related to AD have revealed associations with variants near  Less than 5% of AD patients can be categorized as early-onset disease (diagnosis before age 65). The cause for this subset of disease has been linked to gene mutations in APOE E4 variant does not account for all cases of AD. It is present in less than 50% in European AD cases and occurs even less frequently in African, Asian and Hispanic AD populations. Identification of additional genetic variants apart from APOE has been challenging due in part to the smaller effect sizes of these variants. Genome-wide association studies provide an unbiased approach to test the “common variants common disease” hypothesis. Previous GWAS studies GAB2PCDH11XCLU locus to play a role in disease susceptibility. Each study discovered an additional locus near PICALM or CR1 reached genome-wide significance level. In this study, we conducted a GWAS scan in 1034 cases and 1186 controls mostly collected from Pfizer clinical trials. We first examined genetic markers associated with disease susceptibility for late-onset AD by combining available GWAS data from Pfizer, Alzheimer's Disease NeuroImaging Initiative (ADNI) Genotype-Phenotype Alzheimer's disease Associations (GenADA) The 2 less than 0.3 or minor allele frequency less than 1%) were removed before any further analysis.To identify common genetic markers involved in AD susceptibility and progression, we first conducted a genome-wide association study in 1034 cases and 1186 controls . To this initial data set, we added available genome-wide individual data from ADNI and GenADA to increase the statistical power . All genotyping data were subjected to a strict quality control process including call rates, Hardy-Weinberg equilibrium (HWE) test, sample heterogeneity, gender check  and population stratification . Since limited number of markers are shared between Affymetrix 550 K (GenADA) and Illumina HumanHap 550/610 platforms (Pfizer and ADNI), we imputed the GenADA data set to the non-singleton HapMap SNPs based on the HapMap III reference haplotypes in unrelated Caucasian individuals. Poorly imputed SNPs  in each cleaned case/control sample set using PLINK APOE locus, meta-analysis revealed a number of distinct loci with suggestive association signals with p values less than 1×10−6  and PICALM  loci. The direction of effect for both variants is consistent across each individual sample set  is not significantly different in cases and controls. However, odds ratios for this variant appear to be consistent with the previous studies and close to be significant in the Pfizer sample set .We examined association of single nucleotide polymorphisms with AD disease status (χn 1×10−6 . Furthermple set . In addi−6) in an independent Genizon set of 751 cases and 751 controls from the Quebec Founder Population (QFP). Besides SNPs adjacent to the APOE locus, we only replicated the SNP (rs12989701) at the BIN1 locus . The SNP reached genome-wide significance level in the combined set (BIN1) in QFP and combined all available samples/data  to fine-map this locus. BIN1 resides across multiple linkage disequilibrium blocks in which linkage disequilibrium (LD) within the block is generally higher than the one between the blocks  while the LD between rs744373 and rs12989701 is quite low (r2 = 0.05) suggesting independent contributions to disease susceptibility. Both SNPs passed genome-wide significance level in the combined meta-analysis  to investigate whether the effect of rs12989701 is indeed independent of the previously identified rs744373. Distributions of rs12989701 alleles are still significantly different between AD cases and controls even after controlling for the rs744373 alleles (P = 0.002). Similar results were observed for rs744373 (P = 0.0059) when controlling for rs12989701. These results showed that the Our initial analysis for disease susceptibility focused on individual SNPs without considering any potential interactions of multiple variants. The number of potential SNP combinations, however, increases exponentially and becomes impractical for our current GWAS sample size. We hypothesized that multiple variants in genes in the same pathway may jointly contribute to the association with disease status. To test this hypothesis, we employed GenGen, adapted from a pathway analysis tool originally developed to analyze gene expression by adjusting for different gene sizes and the LD between SNPs CLU = rs11136000, PICALM = rs3851179, CR1 = rs3818361, BIN1 = rs12989701) for their association with disease progression using CDR-sum of boxes (CDR-SB) measured up to 24 months . Progression analysis was done for 597 AD patients with sufficient CDR-SB data. We used a linear repeated measure mixed model and adjusted for study, age, gender, baseline MMSE, baseline CDR and APOE e4 status. In AD, baseline MMSE (p<10−4) and study (p<0.008) are the only covariates with significant contributions to change of baseline CDR over time. Note that these observations are consistent in all variants tested in our analysis. Among the four markers tested in our data set, only one marker, PICALM (rs3851179) showed nominal significant genotype effects on the change in CDR-SB over time for AD subjects , with the TC genotype showing a greater increase than either the TT or CC genotype. The CLU variant showed nominal significant genotype and time interaction (p = 0.02) which would not survive multiple test correction. The other variants are non-significant at the 0.05 level  in Japanese and European cohorts MAPT in PD). Future association studies in other ethnic groups may facilitate our understandings of the similarities and differences in the newly identified genetic loci contributing to Alzheimer's disease.We replicated associations with CLU association underscores the importance of genes involved in lipid metabolism as both CLU and APOE are related to this process . Both SNPs are replicated in the other study at the 0.05 level but only one reached genome-wide significance level in each individual study. The potential independent contributions of both SNPs were supported by additional haplotype conditional analysis. SNP rs12989701 is located at an evolutionarily conserved region, suggesting that it might be important for gene regulation. BIN1 (Bridging Integrator 1) was initially identified as a tumor suppressor with a MYC-interacting domain, a SH3 domain and a BAR (Bin1 Amphiphysin RVS167) domain BIN1 were identified in multiple individuals with autosomal recessive centronuclear myopathy BIN1 isoforms have been shown to associate with dynamin mediated synaptic endocytosis process PICALM, another gene strongly associated with AD. The important role of dynamin mediated endocytosis process was supported by the observations that dynamin-1 levels were reduced in hippocampal neurons in the Tg2576 mouse model of AD Amphiphysin 1 knock-out mice lacking BIN1 expression in the brain and demonstrated deficient endocytic protein scaffolds and synaptic vesicle recycling BIN1 may not be essential for endocytosis but may be important for vesicle trafficking BIN1 is a key component in endocytic endosome recycling in C. elegansBIN1 in endosome function. Endocytic process has been previously implicated in AD as APP, A-beta and ApoE proteins are all internalized through the endolysosomal trafficking pathway. These proteins were further sorted to endosomes. It will be interesting to further investigate the roles of BIN1 in endocytosis/trafficking and its potential contributions to synaptic function.Our study further strengthened genetic evidences to support the BIN1 locus which was recently identified in an independent study Most GWAS analysis focused on individual SNPs have a stringent threshold for significance that must be applied due to the number of tests conducted in the study. It is possible that multiple variants can jointly contribute to disease status. We therefore conducted pathway analysis which derived an enrichment score for all genes in a pathway and compared this with the distribution under null hypothesis based on random permutation. This analysis adjusts for differences in gene sizes and maintains the correlation structures among the SNPs. The apoptotic signal induced by DNA damage has an enriched distribution that significantly deviates from the null in both the Pfizer and GenADA sample sets. Interestingly, our unbiased scan based on pathways collected in Biocarta also indicated that the overall distribution for all the SNPs within the downstream genes targeted by Gleevec appears to be significantly different from the null distribution. Although none of the loci appear to be genome-wide significant, combinations of these SNPs provide evidence to support the involvement of the pathway. Gleevec, a cancer drug approved for the treatment of chronic myeloid leukemia, was recently shown to reduce gamma-secretase cleavage for APP e4 allele in APOE was not associated with progression in AD patients although it was shown to be significantly associated with a faster rate of progression in MCI patients in the previous study PICALM variant rs3851179 have a faster rate of progression compared with CC carriers. The rate of progression in the TT genotypes has a slight increase compared with CC carriers although far from statistical significance. We also observed that the variant at CLU has a nominal significant interaction with time. All the effects from PICALM and CLU variants are independent of the known risk factors such as APOE e4 allele, age and baseline MMSE scores but do not pass multiple test correction so it likely still represents a false positive signal. Our results indicated that the recently identified variants for AD susceptibility may have limited utility to predict disease progression in AD patients. Further unbiased GWAS studies using disease progression as endpoints may be fruitful if statistical power becomes sufficient. Follow up deep sequencing studies and functional experiments for these genetic loci may increase our understanding of the disease mechanisms for AD.We examined the association of the robust disease susceptibility loci in 597 AD patients with sufficient longitudinal clinical data. We observed that the http://www.loni.ucla.edu/ADNI/Data/). 300 AD subjects (including MCI subjects who had converted to AD) and 196 controls from ADNI were included in the analysis. Clinical information for these subjects was described previously http://www.ncbi.nlm.nih.gov/gap). 798 AD subjects from the GenADA collection were included in the analysis after completion of QC procedures. In total, our GWAS discovery analysis set for AD susceptibility comprises of 1831 AD cases and 1764 controls from Pfizer, ADNI and GenADA. The ADNI and GenADA studies were selected based on their sample size and availability at the time of the study. Among the 685 AD subjects who have longitudinal clinical data, 161 subjects from ADNI and 436 subjects from LEADe with sufficient CDR-SB data were included in the disease progression analysis.The Pfizer sample collection includes a total of 1034 cases and 1186 controls: 489 subjects from the Lipitor's Effect in Alzheimer's Dementia (LEADe) trial 1502 samples from the Quebec Founder Population (QFP) were included in the study as a replication set (case/control ratio = 1). All Alzheimer's disease subjects were 65 years old or older and presented with probable AD based on DSM-IV criteria or definite AD as confirmed by neuropathology findings on autopsy. The controls were matched to the patients for gender. The controls were 75 years and older and were absent of AD based on a Mini-Mental State Examination (MMSE) score test> = 26 (adjusted for age and education) and a Montreal Cognitive Assessment (MoCA) score test> = 26 (adjusted for education) at the time of recruitment.All genomic DNA samples for Pfizer and Genizon were extracted from blood and quantified using Picogreen (Invitrogen Inc). The first batch of Pfizer samples (∼300 cases from PrecisionMed/A3041005 and matched controls plus 489 cases from LEADe) were processed with the Illumina HumanHap550 array while all remaining samples were genotyped using the Illumina 610Quad array. All genotyping was performed at Genizon Biosciences Inc and genotype calls were generated after clustering all the data within each platform. Most of LEADe samples were processed on both 550 and 610 platforms and the genotype data concordance rates were greater than 99.99%. The ADNI genetic data set was downloaded from the ADNI web site and a similar initial QC process was performed at Pfizer . The GenADA data was downloaded from dbGap and the data were imputed based on the reference haplotypes from Hapmap III using Mach Data cleaning and Quality control were performed with PLINK using the identical criteria for all Pfizer, ADNI and Genizon sample sets obtained from Illumina platforms. SNPs with MAF <1% or more than 1% missing values were removed, as were samples with more than 1% missing values. Hardy-Weinberg equilibrium (HWE) was evaluated in the control population. SNPs that were out of HWE (−log (p)>5) were dropped. Sample sets were checked for genetic outliers and duplicated samples, which were removed. Only one of any group of samples that are strongly related (IBS distance <0.1) was kept. Reported gender was cross-checked with genetic gender to identify any possible sample identification errors. SNPs with an excess of heterozygosity were removed (Het Excess>0.1 and HWE p<0.01). Caucasians were identified based on multi-dimensional scaling (MDS) of the data compare to the CEPH samples in the HapMap dataset. We adapted the QC procedure from the original GenADA set to accommodate the Affymetrix 550 k platform http://www.sph.umich.edu/csg/abecasis/mach/, 2 less than 0.3) and minor allele frequency less than 1% were removed after imputation.GenADA genotype data (after QC) were imputed using Mach (http://pngu.mgh.harvard.edu/purcell/plink/). We checked the alleles in the association files to ensure that they are consistent across all data sets. The inflation factor, lambda, was estimated by dividing the median chi-square values by 0.455  for each data set. The resulting p-values were combined across datasets using a weighted z-score approach BIN1 locus, we combined association test results from all studies  across the 500 Kb regions upstream and downstream of BIN1 using the meta-analysis function in PLINK assuming a fixed effect model. To test whether SNPs in this region has contribution to disease susceptibility independent of each other, we performed conditional haplotype analysis using PLINK through comparing the alleles/haplotypes that have a similar haplotype background as defined by the SNP of interest.We performed case/control allelic chi-square tests in Pfizer, ADNI and GenADA sample set separately using PLINK  score. Longitudinal data were available for 685 AD patients but only 597 subjects with sufficient CDR-SD data up to 24 months are included in the analysis. The genotypic effect of a variant on the change over time in the CDR sum of boxes was assessed using a repeated measures mixed model, with covariates of baseline CDR sum of boxes, baseline MMSE, sex, age at baseline and APOE4 status, with genotype and the genotype*time interaction as the factors of primary interest. A main-effects model, without the genotype*time interaction, was also fit to the data. Progression effects were modeled for four SNPS: http://www.biocarta.com/) is consistently deviated from the null hypothesis from our sample sets. Pfizer, ADNI and GenADA dataset (before imputation) were used for this analysis. 1000 permutations were conducted for each analysis.The current GWAS analysis is based on association tests in individual markers without considering the joint effects of multiple variants. We employed GenGen Table S1Summary statistics for all markers in Pfizer sample set. Note: Large file (41MB).(XLSX)Click here for additional data file.


Panicum virgatum L.) exists at multiple ploidies and two phenotypically distinct ecotypes. To facilitate interploidal comparisons and to understand the extent of sequence variation within existing breeding pools, two complete switchgrass chloroplast genomes were sequenced from individuals representative of the upland and lowland ecotypes. The results demonstrated a very high degree of conservation in gene content and order with other sequenced plastid genomes. The lowland ecotype reference sequence (Kanlow Lin1) was 139,677 base pairs while the upland sequence (Summer Lin2) was 139,619 base pairs. Alignments between the lowland reference sequence and short-read sequence data from existing sequence datasets identified as either upland or lowland confirmed known polymorphisms and indicated the presence of other differences. Insertions and deletions principally occurred near stretches of homopolymer simple sequence repeats in intergenic regions while most Single Nucleotide Polymorphisms (SNPs) occurred in intergenic regions and introns within the single copy portions of the genome. The polymorphism rate between upland and lowland switchgrass ecotypes was found to be similar to rates reported between chloroplast genomes of indica and japonica subspecies of rice which were believed to have diverged 0.2–0.4 million years ago.Switchgrass ( It is envisioned as a source of biomass for bioenergy production in marginal areas that would not compete with food production Switchgrass is a warm season CLowland accessions are mainly tetraploids (2n = 4x = 36) while most upland accessions are octaploid (2n = 8x = 72) In most land plants, cp genomes consist of a single circular chromosome with a quadripartite structure that includes a large single copy region (LSC) and a small single copy region (SSC) separated by two copies of inverted repeats (IR). The gene content, order, and organization of cp genomes are generally highly conserved and inheritance is primarily maternal BamHI RFLP polymorphism present in rbcL present in upland and absent in lowland cultivars et al. identified a deletion of 49 nucleotides in trnL-UAA intron sequences of lowland cp genomes trnL-UAA introns across several switchgrass accessions with unknown affiliation were able to resolve these into upland and lowland ecotypes, but bootstrap support was generally weak Chloroplast genetic variation between switchgrass ecotypes has been previously identified through the detection of a Panicum virgatum L. Our goal is to compare both an individual lowland ecotype (Kanlow) and an individual upland ecotype (Summer), with other completely sequenced grass cp genomes, and to one another. Complete cp genome alignments enabled the examination of gene content, gene order, and overall genome size. In addition, we determined the distribution and location of microsatellite repeat polymorphisms, insertions and deletions (indels), and single nucleotide polymorphisms (SNPs) among these cp genomes. Comparisons using a specific subset of protein-coding genes allowed for phylogenetic analyses of cp genomes and identified unique genetic qualifiers classifying switchgrass in the Panicoideae subfamily. Our analyses of two switchgrass cp genomes provide detailed genetic data differentiating upland and lowland ecotypes and support the utility of using plastid sequence information in breeding programs.In this article, we report the complete chloroplast (cp) nucleotide sequences of two reference individuals of The complete cp genome size of the lowland ecotype reference sequence (Kanlow Lin1) is 139,677 base pairs while the upland sequence (Summer Lin2) is 139,619 base pairs. The Kanlow Lin1 reference sequence includes a LSC region of 81,729 bp and a SSC region of 12,540 bp, which are separated by a pair of inverted repeat (IR) regions of 22,704 bp. A diagram of the Lin1 genome is represented in ycf68. Ycf68 may encode a functional protein in chloroplasts, or may be involved in the splicing of the trnI-GAU intron sequence rpl2 gene utilizes GCG and the rps19 gene utilizes GTG.When compared to Sorghum (131 genes) rpoC2 (the beta subunit of RNA polymerase) is a key diagnostic difference between the cp genomes of these two switchgrass ecotypes  regions have led to variation in sequence duplication at the IR/LSC and IR/SSC boundaries of cp genomes. In the order Poales, all members have expanded these boundaries to add tchgrass . Kanlow Mononucleotide microsatellite length polymorphisms have been used as markers in cp genomes for understanding evolutionary history due to their high rates of variability rpoC2 insertion  and Lin2 (upland) confirmed the previously described deletion of 49 nucleotides in trnL-UAA intron of lowland cp genomes Other polymorphic sites in the switchgrass cp genome have been recently assessed in the dS) of 0.0017, based on the method of Yang & Nielsen −9 synonymous substitutions per site per year in the cp single copy region, these sequences apparently diverged from a least common ancestor approximately 523–845 thousand years ago.In all, there were 116 SNPs identified between the two switchgrass cp genotypes . The subAs an independent confirmation of the presence of these SNPs in breeding pools of switchgrass, a total of 106.5 million Illumina short-read sequences derived from RNA-seq experiments conducted on upland genotypes and 101.3 million similar sequences derived from lowland genotypes were aligned to the Lin1 reference sequence. All together, 1.53 million (1.4%) reads from upland genotypes produced at least one alignment with a maximum of 1 mismatch, and these touched 99.4% of the genome to a coverage depth of at least 4. Overall, 184 sites with a sequence depth of >4 were identified as potential SNPs/indels. Of these 184 sites, 101 (61%) matched the 116 variable positions found between the two reference sequences. When sequences derived from lowland genotypes were aligned, a total of 82,656 (0.08%) matched under the same conditions, and 90% of the genome was covered at a depth of greater than 4 reads. There were 11 variable positions and 3 indels with a sequence coverage depth of >4 within the lowland Illumina data. One additional A to T difference (pos. 18114) relative to the Lin1 sequence was invariant within the Illumina data with a coverage depth of 5. Six of the sequence differences were shared by the upland groups of reads. These data are summarized in rpl2 start codon to AUG. In total, there were 35 predicted editing sites, of which 28 would result in alterations to the coding sequence at non-synonymous sites. We compared the predicted editing locations with alignments of the Illumina sequencing data derived from RNA-seq experiments. All of the 35 predicted editing sites were covered by the short read sequences at a depth of at least 4. However, only two of the predicted sites  appeared to support editing. In addition, these reads were a minor component of the total number of aligned reads at these sites, with 2/12 and 5/13 reads consistent with editing, respectively. When considered together with the general agreement of the Illumina SNP discovery results and the reference cp genomes, these data indicate that the vast majority of aligned reads were derived from cp genomic DNA rather than cp RNA.Post-translational modifications such as RNA editing can alter the amino acid sequence of a protein, causing it to differ from that predicted by the genomic DNA sequence. We find that predicted RNA editing sites occur in the switchgrass cp genome, using CURE-chloroplast v1.0 software Bambusa oldhamii with Oryza species in the ML tree, but support for monophyly with the Pooideae and Panicoideae subfamilies in the MP tree  than from lowland short-read data (0.08%). We believe this was due to intrinsic differences in the source library tissue. The lowland switchgrass libraries were produced from non-green (crown) tissue sources. These tissues are known to have fewer and less well-developed plastids, in comparison to the green tissues used for libraries derived from the upland genotypes Chloroplast transformation has proven to be of considerable importance to many aspects of plant biotechnology, trait introgression, and breeding programs clpP and rpoC1, and the entire loss of the three genes accD, ycf1, and ycf2trnH-GUG and rps19 in the IR ndhF gene, which is consistent with other members of the Panicoideae . In contrast, expansion of the IRa to duplicate a region of ndhH is noticeably lacking from the switchgrass plastid genome, separating it from the Ehrhartoideae and Pooideae subfamilies Panicum virgatum L. plastid genomes with other Panicoideae genera and are consistent with analyses of other cp genomes Genome-wide comparisons of the two switchgrass cp genomes with other members of Poales demonstrate conservation of monocot and grass-specific phylogenetic indicators. Earlier studies have identified several features of Poaceae plastid genomes, including three inversions, the loss of introns from genes Individual switchgrass genotypes were selected for cp genome sequencing that were derived from cv. “Kanlow” and “Summer” and were designated LIN1 and LIN2, respectively. DNA was isolated from immature leaves using a CTAB procedure A PCR strategy was employed due to the highly conserved nature of cp genomes. This strategy is a compromise as it avoids the need for cp gDNA isolation, but also introduces the possibility of mistakes due to lack of fidelity of polymerases. Primers used for sequencing of the switchgrass cp genome are listed in Atropa belladonna, Phalaenopsis, Pine, Pea, and Sugarcane.RNA editing (C-U) sites were predicted with CURE-chloroplast v1.0 Panicum virgatum L. cp genome was performed using DOGMA  Initial annotation of the Mononucleotide microsatellite markers were predicted using MISA Whole genome comparisons were performed between Lin1 and Lin2 with MUMmer x 56-bp reads were produced from cDNA libraries of P. virgatum cv. ‘Kanlow’ crown and rhizome tissue prior to a killing frost. Another 106.5 million Illumina GAII 36-bp reads were downloaded from the National Center for Biotechnology Information (NCBI) sequence read archive that were annotated from a variety of upland ecotypes. These reads were aligned to the Lin1 cp reference sequence using Burrows-Wheeler Aligner A total of 101.3 million Illumina GAIINo specific permits were required for the described field studies.A set of 61 protein-coding genes included in the analysis of several other cp genomes Table S1Taxa included in phylogenetic analyses with GenBank accession number and reference. aNumbers in brackets correspond to the manuscript reference list, unless indicated otherwise. bCahoon AB, Sharpe RM, Mysayphonh C, Thompson EJ, Ward AD, et al. (2010) The complete chloroplast genome of tall fescue  and comparison of whole plastomes from the family Poaceae. Am. J. Bot. 97: 49–58. cMasood SM, Nishikawa T, Fukuoka S-ichi, Njenga PK, Tsudzuki T, et al. (2004) The complete nucleotide sequence of wild rice (Oryza nivara) chloroplast genome: first genome wide comparative sequence analysis of wild and cultivated rice. Gene 340: 133–139.(DOC)Click here for additional data file.Table S2Sequencing primers used in this study.aPrimer sequences listed with both a “+” and “−” strand position are located in the inverted repeat region. bChloroplast genome positions listed for: Oryza sativa, Osa; Sorghum bicolor, Sbi; Triticum aestivum, Tae; Panicum virgatum, Pvi. Primers that do not have 100% sequence identity to a given chloroplast genome do not have positions listed for that genome. Primers with positions listed only under Pvi are finishing primers chosen using consed's autofinishing function.(DOC)Click here for additional data file.Table S3Primers used to amplify inverted repeat (IR) region.(DOC)Click here for additional data file.


A universal biomarker panel with the potential to predict high-risk pregnancies or adverse pregnancy outcome does not exist. Transcriptome analysis is a powerful tool to capture differentially expressed genes (DEG), which can be used as biomarker-diagnostic-predictive tool for various conditions in prenatal setting. In search of biomarker set for predicting high-risk pregnancies, we performed global expression profiling to find DEG in Ts21. Subsequently, we performed targeted validation and diagnostic performance evaluation on a larger group of case and control samples. Initially, transcriptomic profiles of 10 cultivated amniocyte samples with Ts21 and 9 with normal euploid constitution were determined using expression microarrays. Datasets from Ts21 transcriptomic studies from GEO repository were incorporated. DEG were discovered using linear regression modelling and validated using RT-PCR quantification on an independent sample of 16 cases with Ts21 and 32 controls. The classification performance of Ts21 status based on expression profiling was performed using supervised machine learning algorithm and evaluated using a leave-one-out cross validation approach. Global gene expression profiling has revealed significant expression changes between normal and Ts21 samples, which in combination with data from previously performed Ts21 transcriptomic studies, were used to generate a multi-gene biomarker for Ts21, comprising of 9 gene expression profiles. In addition to biomarker’s high performance in discriminating samples from global expression profiling, we were also able to show its discriminatory performance on a larger sample set 2, validated using RT-PCR experiment (AUC=0.97), while its performance on data from previously published studies reached discriminatory AUC values of 1.00. Our results show that transcriptomic changes might potentially be used to discriminate trisomy of chromosome 21 in the prenatal setting. As expressional alterations reflect both, causal and reactive cellular mechanisms, transcriptomic changes may thus have future potential in the diagnosis of a wide array of heterogeneous diseases that result from genetic disturbances. Progressive development and improvement of methods for investigation of global transcriptional alterations in human disease is now enabling reproducible and consistent selection of genes that best differentiate samples originating from disease-affected or healthy individuals . AlthougIdentification of high-risk pregnancy with its heterogeneous aetiology and complex pathogenesis remains complicated since there is no single investigation deemed to be best in all circumstances. As there already are successful implementations of expression biomarkers into clinical practice  we optedGlobal transcriptome profiling studies on prenatal samples of amniotic fluid and chorionic villi with trisomy of chromosome 21 ,5 revealThese studies mainly addressed the issue of transcriptomic changes in trisomy of chromosome 21 in different human tissues mainly with the aim to dissect Down syndrome phenotype and to explain the pathogenic mechanisms underlying variability of Down syndrome. However, they did not primarily aim to assess the diagnostic potential of transcriptomic changes in trisomy of chromosome 21 samples.One problematic aspect of aforementioned studies was also that the reported results overlapped only partially and were therefore difficult to reproduce, probably due to small study power stemming from low numbers of biological replicates investigated and large heterogeneity of analysed tissues across various studies. However, a recent meta-analysis from 45 heterogenous publicly available Ts21 datasets succeeded to organize these results and identify a pattern of consistency in dysregulated genes from Ts21 studies .Among the aforementioned transcriptomic studies only a few investigated transcriptomic alterations in prenatal Ts21 samples, including chorionic villi, amniotic fluid cell-free mRNA or amniocytes ,5,12-14.discovery stage, we performed detection of differentially expressed genes (DEG) by global gene expression profiling and evaluated these results in the context of existent body of literature. Based on this step, a subset of genes was incorporated in the core gene set for further analyses. In the second, validation stage, the performance of biomarker gene set was evaluated on an independent study sample. Additionally, the biomarker set performance was tested on processed datasets from the GEO database.We aimed to design a discriminatory gene expression signature by a 2-stage approach. Firstly, in the -14), according to hypergeometric distribution test. This subset of genes was characterized by average fold change of 1.59 (95% CI 1.39-1.82). A considerable proportion of HSA21 genes (87.1%), however, appeared to resist the dosage effect and their expression level, while showing slight tendency towards up-regulation , did not display significant changes in comparison to controls after significance values were corrected for multiple testing. Altogether, 240 genes satisfied .In addition to differential expression of genes on chromosome 21, we have detected a widespread transcriptional response of 932 non-HSA21 genes , which wSubsequently, the classification properties of obtained microarray results were inspected. The clustering analyses have shown spontaneous separation of samples into two larger groups, with separation clearly corresponding to Ts21 status . We veriBased on results obtained in the discovery phase in addition to information from previously performed study a core set of classifying genes was selected and included in the core biomarker set . ExpressPerformance of the core gene expression set in the validation sample was estimated afterwards. The performance of SVM estimated using a leave-one-out cross-validation approach (LOOCV) resulted in AUC values of 0.97 for discrimination between euploid and trisomic samples .Further support for the true performance of gene expression biomarker for classification was obtained from previously performed global gene expression profiling studies of trisomy 21 performed on prenatal samples. The predictive performance of our 9-gene set was high in each dataset, reaching AUC values of 1.0 for both Altug-Teber and Chou dataset . The higIn this study we demonstrate the potential of gene expression signature, consisting of 9 genes, in prediction of Ts21 status in the prenatal setting.In contrast to previously performed studies investigating transcriptional alterations in Ts21 which mainly focused on detecting transcriptome alterations or explaining the variability of DS phenotype ,5,12-14,While initial studies on expression alterations in Ts21 reported discordant results, Vilardell and coworkers have recently performed a meta-analysis of several Ts21 expression studies , includiThe effect of an additional copy of HSA21 has previously been thought to result in 1.5 times upregulation of gene expression of HSA21 genes. This has been in part refuted by large-scale studies investigating whole chromosome 21 and whole genome expression perturbances in trisomy 21, when a requirement for a better pathogenetic model has surfaced to explain complex transcriptional alterations detected on global scale. To rectify these drawbacks and to facilitate the selection of genes for best discrimination of prenatal samples with trisomy 21 from those with euploid chromosomal constitution, a two-tiered gene expression profiling study was performed, consisting of a discovery and validation phase. The discovery phase was performed utilizing global microarray expression profiling on the largest collection of trisomy 21 and control euploid prenatal amniocyte samples investigated to date. To optimize gene selection, we observed data originating from studies previously performed on amniocytes ,4,10. AdWe also recognize some limitations of our study. Firstly, the classification performance of the proposed gene expression signature was high, AUC values reaching 0.97. However, we believe that with introduction of additional genes in the signature, the performance could likely be further improved. Secondly, the expression profiling experiments in our study were performed on the samples of cultivated amniocytes, which might influence actual expression alterations taking place in Ts21. Nevertheless, since we could demonstrate high accuracy of the same signature on the uncultivated chorionic villus biopsy tissue sample set , it coulAlthough this research was focused on the delineation of consistent alterations present in trisomy 21 and definition of a biomarker with potential in clinical use, the utility of expression profiling may pervade other pathological entities encountered in the prenatal setting. As gene profile is not solely determined by the genetic constitution of an individual, but is the sum of genomic, epigenomic and environmental influences, its utility may surpass detection of Ts21 presented here and may present a possible multifaceted biomarker of high risk pregnancies.This study was approved by Slovenian National Ethics Commitee. All participants have signed an informed consent prior to participation in the study and clinical investigations have been conducted in accordance with principles expressed in Helsinki declaration.th and 18th week of gestation for routine cytogenetic analysis. Primary cell cultures of amniotic fluid were performed according to standard protocols using tissue culture flasks . Cell cultures were grown in Amnio Max C100 Basal Medium and Amnio Max C100 Supplement  at 37°C in 5% CO2 environment. Cytogenetic analysis was performed according to standard protocol with a resolution of 450-550 bands per haploid set. Following a routine diagnostic analysis a second passage of amniotic cell culture was grown in the same condition as primary cell culture. The gene expression discovery group (sample set 1) comprised of 19 samples, namely 10 derived from fetuses with trisomy 21 and 9 coming from normal pregnancies , using columns in the Fujifilm RNA Cultured Cell kit to capture purified RNA samples. The purity and yield of isolated RNA samples was determined using NanoDrop 2000c spectrophotometer .The integrity of RNA samples isolated from subjects selected for genome-wide profiling of gene expression was investigated on Agilent’s Bioanalyzer using RNA 6000 Nano Kit, where only samples attaining RNA integrity number (RIN) values greater than 8.0 were used for downstream array experiments.In the present study we have constructed and evaluated potential gene expression signature for Ts21 in two separate stages.discovery stage of our study, we aimed to identify the gene expression signature for Ts21, consisting of a minimal set of genes with predictive diagnostic power for discrimination of Ts21 cases from controls.Initially, in validation stage of this study, the expression signature of genes selected in the discovery stage was validated on an independent group of samples. We employed two different approaches to validation of samples – firstly, we proposed validation of gene signature on an independent sample of Ts21 cases and controls by means of quantitative PCR and secondly, we have validated the discriminatory performance of the signature on data from two previously performed independent genome-wide expression profiling studies [Subsequently, in the  studies ,13.Agilent’s 4x44 two-colour Whole Human Genome Expression arrays, containing 41.001 feature probes for interrogation of over 19.644 human genes, were selected to estimate the extent of global transcriptional alterations in investigated cells. Preparation of RNA samples, their labelling and hybridization were performed according to manufacturer’s instructions. For the purposes of microarray study, test samples and control samples from sample set 1 were hybridized against a common reference, obtained by pooling all samples. Here, the reference RNA pool was labelled with Cy3 and each sample was labelled separately with Cy5 .Microarray slides were scanned using GenePix 4100A microarray scanner. Post-processing steps included intra-array loess and inter-array quantile normalization to correct for potential bias resulting from differential stability of cyanine dyes. Fluorescent values were offset by 100 units to reduce the anomalous dispersion of fold change (FC) values at lower signal intensities. MA and multidimensional scaling (MDS) plots were inspected for each array to detect any systemic error resulting from preceding steps.Subsequently, results were statistically analyzed using a linear model fit in limma package for Bioconductor in an R statistical environment. To account for multiple testing, obtained significance values were corrected using the Benjamini-Hochberg method and the adjusted significance threshold set at α<0.05.Genes that would be included in the core biomarker gene set were selected based on multiple lines of evidence. For inclusion, the gene would have to be differentially expressed in the discovery phase, with significance values below the adjusted p-value threshold. Additionally, to technically facilitate subsequent validation, the gene’s expression levels should be altered by at least 50% (under-expressed or over-expressed).To expand the information landscape used for gene selection, data from previous studies were incorporated into the gene selection process. Only genes, detected in at least 3 other studies and our study were considered to have enough consistent differential expression to be included in the core gene set .A core set of genes selected in the discovery phase was investigated in a validation step on an independent set of samples using real-time PCR quantification of gene expression. For this purpose, RNA samples passing quality checks were subjected to reverse transcription using the Superscript Vilo reverse transcriptase  according to manufacturer’s instructions. Afterwards, gene expression was quantified using pre-designed TaqMan assays  with assay identification numbers: Hs00533490_m1 for SOD1, Hs00426889_m1 for ATP5O, Hs00165078_m1 for LAMB3, Hs00739248_m1 for SUMO3, Hs00199064_m1 for BTG3, Hs00371372_m1 for SON, Hs00170791_m1 for USP16, Hs01120954_m1 for RCAN1, Hs00161676_m1 for ITSN1. Reactions were performed in volumes of 25µL, consisting of 12.5µLof 2x Universal Master Mix (Applied Biosystems), 1.25µL of a specific assay mix, 2µL of cDNA sample and 9.25µL of bidestilated water. Input cDNA samples were diluted so that an amount of cDNA equivalent to 50ng of RNA was used in each reaction. To minimize stochastic effects, all reactions were run in triplicates. RT-PCR reactions were performed on ABI Prism 7000 Sequence detection system (Applied Biosystems). Thermal cycling conditions were as follows: 50°C for 2 min, 95°C for 10 min and 40 cycles of 95°C for 15s and 60°C for 1 min. The threshold cycle (Ct) values were then determined for each assay and were normalized to internal control  that was co-run with each sample. Differences in gene expression between samples could then be calculated using the delta-deltaCt method, as previously described [To further investigate the discrimination properties of our biomarker gene set, the complete  datasets from two studies were obtained from Gene Expression Omnibus (GEO) repository , namely Statistical evaluation of the expression biomarker’s classification performance was performed using the classification toolset from CMA package for R . In all Evaluation of global gene expression profile based classification on microarray data was performed using automated feature prioritization based on t-test statistic, and 5-fold cross validation for repeated separation of samples into training and testing subsets. To avoid over-training of classifier function on microarray samples, the feature selection step was repeated in each cross-validation sample. To further exclude the possibility of over-fitting, another test was performed, where sample status was permuted randomly and the distribution of prediction performances was investigated in this setting. In the absence of over-fitting bias, the prediction performance of the classifier should on average be non-discriminatory, with expected AUC values averaging at approximately 0.5.The predictive performance of the gene set taken to the validation stage was also investigated using SVM function. Due to differences in platform utilized in gene expression profiling, a new classifier, based on RT-PCR results was constructed for this dataset. As the set of genes in the validation was predefined, no feature selection on the validation dataset was performed to avoid artificially inflating performances by injection the knowledge obtained from validation stage. The performance here was estimated by the leave-one-out cross validation technique and performance was again estimated using AUC.The same procedural flow was also taken for evaluation of data originating from publicly available gene expression profiling studies, where classification performance was again evaluated using the 9 gene set included in our biomarker.Figure S1Unsupervised hierarchical clustering of samples, based on expression profiles of top 500 differentially expressed genes. A clear spontaneous separation between trisomy 21 and control samples may be observed, with red color representing up-regulation and green color representing down-regulation.(TIFF)Click here for additional data file.Figure S2Estimation of classification accuracy based on microarray data before continuation to RT-PCR validation step. The figure represents performance of classifier model based on top differentially expressed genes, estimated by 5-fold cross-validation. Red colored dots represent performance of classifier generated on actual samples status information, while blue dots represent performance of classifier learned on permuted sample classification. Classifier performance was also evaluated on progressively increasing number of genes included in the model (x-axis), where the best classification performance was attained in the range of 5-25 genes included in the model.(TIFF)Click here for additional data file.Table S132 chromosome 21 genes found differentially expressed in trisomy 21 in comparison with euploid amniocyte samples.(DOCX)Click here for additional data file.Table S2240 candidate genes for inclusion in the core biomarker gene set, satisfying 2 criteria: attaining false-discovery rate values below 0.05 and 50% reduction or increase in expression in DS amniocytes.(DOCX)Click here for additional data file.Table S3932 non-chromosome 21 genes found differentially expressed in trisomy 21 in comparison with euploid amniocyte samples.(DOCX)Click here for additional data file.Table S4Clinical characteristics of the amniotic fluid samples  used for global expression profiling.(DOCX)Click here for additional data file.Table S5Clinical characteristics of the amniotic fluid samples with T21 samples used for validation in real-time quantification step.(DOCX)Click here for additional data file.