Human iPS cells have been generated using a diverse range of tissues from a variety of donors using different reprogramming vectors. However, these cell lines are heterogeneous, which presents a limitation for their use in disease modeling and personalized medicine. To explore the basis of this heterogeneity we generated 25 iPS cell lines under normalised conditions from the same set of somatic tissues across a number of donors. RNA-seq data sets from each cell line were compared to identify the majority contributors to transcriptional heterogeneity. We found that genetic differences between individual donors were the major cause of transcriptional variation between lines. In contrast, residual signatures from the somatic cell of origin, so called epigenetic memory, contributed relatively little to transcriptional variation. Thus, underlying genetic background variation is responsible for most heterogeneity between human iPS cell lines. We conclude that epigenetic effects in hIPSCs are minimal, and that hIPSCs are a stable, robust and powerful platform for large-scale studies of the function of genetic differences between individuals. Our data also suggest that future studies using hIPSCs as a model system should focus most effort on collection of large numbers of donors, rather than generating large numbers of lines from the same donor. Human induced pluripotent stem (hiPS) cells are a potentially powerful model system for studying human disease and development, and a resource for personalized medicine. However, it has been reported that hiPS cells exhibit substantial heterogeneity which could limit their use as model systems. Clearly, knowledge of the source of heterogeneity is key for deeper understanding of the use of human iPS cells for basic and therapeutic applications. One source of this heterogeneity has been presumed to be “memory” of the adult somatic cell from which the hIPS cells were derived, but the evidence to support this view is scant. We have generated a set of human iPS cells from a set of somatic cell types from different donors. Our study shows that cell lines from different somatic sources but from the same donor (i.e. with the same genome) are more similar than cell lines isolated from the same tissue type but from different donors. Once genetic changes are accounted for, all aspects of gene expression, including mRNA levels, splicing and imprinting are highly similar between iPS cells derived from different human tissues. Thus, most of the previously described transcriptional variation between cell lines is likely to be genetic in origin. Induced pluripotent stem cells (iPSCs) are the subject of tremendous interest as model systems for studying human disease and development In this study we set out to understand the basis of this variation by establishing a set of iPS cells from a panel of tissues isolated in parallel from several different donors. RNA-seq data sets from these lines, the corresponding adult somatic cells and human ES cells have been systematically compared. This has enabled us to investigate patterns of expression, splicing and imprinting between these iPS cells, their adult cell progenitors and compare these with hES cells. Using a statistical model we estimated the relative contributions of genetic background and tissue of origin to transcriptional variability between human iPS cell lines.We established primary fibroblast, keratinocyte and endothelial progenitor cell (EPC) somatic cell lines from three healthy male organ donors, labeled S2, S5 and S7, and one healthy female donor (S4). From each primary adult tissue cell line, we derived at least three independent iPS cell lines for each donor. For the adult cell cultures we extracted RNA following each of three passages to give a total of 18 RNA samples from adult donor cells . We also extracted RNA from the iPS cell lines derived from each of these tissues to give a total of 9 RNA samples from fibroblast-derived iPSCs (F-iPSCs), 6 from keratinocyte-derived iPSCs (K-iPSCs) and 10 EPC-derived iPSCs (E-iPSCs). Finally, we also extracted 4 RNA samples from two ES cell lines, H9 and Val9. Throughout our study highly standardized conditions were used. This included the isolation, derivation and culture of primary cell cells as well as use of the same batches of reprogramming viruses, serum and media. Each of the primary cell lines were reprogrammed using the four Yamanaka factors to generate a series of 25 iPS cell lines derived from each of the three adult tissues . The differentiation properties of our iPS cell lines were checked by differentiating a subset to endoderm, mesoderm and neuroectoderm derived cell types . PolyadeInitially we examined the global pattern of transcription in the different cell lines. Previous work has suggested that embryonic stem cells are more transcriptionally active than differentiated cells Next, we sought to quantify the contribution of multiple biological and experimental factors to transcriptional variation in hiPSCs. Hierarchical clustering clearly placed adult somatic cells and pluripotent cells in two distinct clades . HoweverWe found that inter-individual transcriptional variation in hiPS cells (VE ∼38%) is considerably larger than that between somatic tissue of origin (VE∼4%) with an even smaller fraction of transcriptional variation (<1%) explained by differences between iPSCs and ESCs . StrikinWe also examined the amount of residual transcriptional variation that remained unexplained by any of the known factors using a heteroscedastic model . This exGiven that our variance component analysis was based on FPKM values, we also reanalyzed data excluding highly expressed genes, as this may impact our results. We found that our component estimates and correlation heatmap were qualitatively very similar when the top 1 and 5% of genes were removed from the data set , S8. We Although our variance components analysis suggested relatively small global effects of tissue of origin, this could mask effects at individual genes. We next sought to identify those genes whose expression in hiPS cells more closely resembled their somatic progenitors or were improperly silenced or activated, relative to ES cells . For eacWe used a hierarchical model , similar−5)  of 5% (estimated from permuted data), and that exhibited a 1.5-fold or greater change in expression level between hiPS cells and hES cells. Using these criteria we identified a total of 61, 5 and 103 transcribed regions that showed some form of differential expression in F-, K-, or E-iPS cells respectively , the maj−5) . Aberran−5) . Our dif−5) . HoweverAlthough whole gene expression levels appeared to be relatively stable across different tissues of origin, cellular memory could also manifest at the level of RNA splicing. Using the statistical framework we developed for gene expression levels, we next tested whether isoform abundance ratios showed evidence of memory of their cell type of origin . In thishttp://www.geneimprint.com/database. We found that many genes were expressed at a low level or lacked sufficient coverage of heterozygous SNPs, which were subsequently excluded. The remaining genes  exhibited allelic expression patterns in adult cells that were conserved in their derived iPS cell lines , Previous studies have suggested that imprinted gene expression patterns may be unstable in hES cells and hIPS cells Finally we returned to the effects of the genetic background in our hiPS cells. Extensive maps of genetic variants whose genotype correlates with gene expression  have been generated in adult human tissues and cell lines −68), as was the allelic imbalance at heterozygous ascertained eSNPs . Although our data set is small, we do find convincing examples where a correlation between genotype and gene expression replicated a known eQTL identified in LCLs such as the exonic eSNP, rs1059307, located within the noncoding RNA gene SNHG5. At this gene, we also observe clear allelic imbalance in iPS cells derived from S5 and S7 individuals, who are heterozygous for this eSNP  as our hiPS cells  and idenhis eSNP , which ihis eSNP . These gWe have shown that epigenetic memory of the adult progenitor cell is a rare phenomenon in hIPS cells, and that cellular heterogeneity between different hIPS lines is more likely to be driven by changing genetic background. Our study has important implications for future attempts to use iPSCs as cellular model systems for drug discovery and other applications. Encouragingly, our results suggest that genetic effects are readily detected in hIPSCs and that cell phenotypes are highly reproducible within individuals. Equally important, however, is the fact that the noise introduced by genetic background could potentially obscure small genetic signals of interest in small samples. A clear implication of this result is that, in iPSC-based studies of genetic disease, most effort should be expended on collection of samples from different donors rather than generation of large numbers of lines from the same individual. Collection of multiple individuals, perhaps with a shared genotype at a single locus of interest, will allow the effects of genetic background to be averaged over and separated from that of the putatively causal locus.Our study also highlights how the effects of genetic background cannot be ignored when considering cellular variability between pluripotent stem cell lines. Previous studies have attributed cellular variability in IPS to a range of sources, including epigenetic memory An important caveat for our study is that influential cellular differences may simply not manifest as transcriptional variation but reside at, for example, the epigenetic level as changes in methylation status or histone tail modifications. Such differences may harbor a “hidden” functional role that only becomes apparent upon differentiation into a specific cell lineage. Our study suggests that epigenetic differences are likely to be more plausible candidates as drivers of variation in IPS cell differentiation ability. However, our results also illustrate that current iPS cell technology is robust enough to enable detection of genetic effects on important cellular phenotypes such as mRNA levels. Although further technological hurdles remain, an exciting area for future work will be detection of regulatory variation that influences transcription during cell lineage specification and differentiation, employing iPS cells as a model system.All primary tissue samples and blood for this project were obtained from adult cadaveric organ transplant donors referred to the Eastern Organ Donation Services Team (part of NHS Blood and Transplant). Ethics approval was obtained from the local Research Ethics Committee (REC No. 09/H306/73).3 pieces. These were dispersed evenly on a 90 mm plate and incubated with fibroblast media (Knockout DMEM and 10% FBS). Outgrowths of fibroblasts and keratinocytes from the skin explants were usually apparent at around 14 days. The cells were separately harvested using 5 min treatment with Versene , which detached the fibroblasts leaving the keratinocytes on the plate. The fibroblasts were cultured on non-coated plates using fibroblast media and keratinocytes were cultured on plates coated with matrix  and using EpiLife media plus Defined Growth Supplement .For each subject included in this study, a sample of skin was excised from the midline surgical incision. The skin was transported to the lab and washed in iodine and ethanol and was cut into approximately 1 mmEndothelial Progenitor Cells were derived from 100 mL of peripheral blood as previously described 5 EPCs were transfected with the 4 viruses in the presence of 10 ug/mL of polybrene . After 24 hrs the viruses were washed off with PBS and the cells were re-fed with EPC media that remained for the next 4 days. On day 5 after transduction, the cells were re-plated using trypsin onto a 10 cm dish of fresh MEF feeders. After 2 days the media was changed from primary cell-specific to hiPSC media (KSR + FGF-2). The media was changed every 2 days until colonies emerged after which the media was changed daily. Colonies were picked once they had reached sufficient size, typically from day 25 following transduction. The colony was split into quarters and the segments gently lifted off the plate and transferred to one well of a 12 well plate of fresh MEF feeders containing hiPS cell media (KSR + FGF2) supplemented with ROCK inhibitor .Four pseudotyped Moloney murine leukemia retroviruses containing the coding sequences each of human OCT-4, SOX-2, KLF-4 and C-MYC were obtained from Vectalys . A multiplicity of infection of 10 was used in all retroviral reprogramming experiments. For each hiPS cell derivation, 1×105 primary cells (fibroblasts or keratinocytes) were used at MOI 3 and polybrene was not used.Four Sendai viruses containing the coding sequences of each of human OCT-4, SOX-2, KLF-4 and C-MYC were obtained from DNAVec . The protocol for reprogramming was identical to that of retroviruses with the exceptions that 5×10hiPS cells were grown on irradiated MEF feeders, using human embryonic stem cell media (termed KSR + FGF-2): Advanced DMEM  was supplemented a follows: 10% Knockout Serum Replacement , 2 mM L-glutamine  and 4 ng/µL of recombinant human basic Fibroblast Growth Factor-2 . Media was changed daily and the cells were passaged every 5–10 days depending on the confluence of the plates. To passage hiPS cells, the plates were washed in PBS and colonies detached using collagenase and dispase . The colonies were washed in media and mechanically broken up before being re-plated onto fresh MEF feeders.Total RNA was extracted using the RNeasy Mini Kit protocol . RNA-seq libraries were constructed according the manufacturers guidelines, with minor modifications, using the Illumina mRNA-seq and TruSeq mRNA sample preparation kits . Briefly, mRNA was enriched from total RNA using oligo dT beads before fragmentation via zinc and heat hydrolysis. mRNA was subject to first and second strand cDNA synthesis before end repairing and A-tailing. Double-stranded cDNA was then adapter-ligated before size-selecting fragments with inserts ranging from 200–300 bp using a LabChipR XT . Size-selected material was then PCR-amplified using KAPA HiFi polymerase  before sequencing on an Illumina HiSeq2000 .j was modeled as a linear combination of five normally distributed random effects (b1–5) and a single error term:j in each of the 46 samples in our data set, b1 is an intercept term, b2 models variation in transcription between the three adult somatic tissues, hESCs and hiPSCs, b3 models differences between F-, K- and E-iPSCs, b4 captures transcriptional variation between different donors, b5 captures differences between the two sequencing batches in our data set, ε is the error term and Z1-Z5 are design matrices. For full details of computational and statistical analyses, see We mapped reads to assembly h37 of the human genome using Bowtie2 http://www.sanger.ac.uk/research/projects/genomicsofgeneregulation/) under the “Data” tab.Our raw sequence data are available from the European Genotype Archive under study ID EGAS00001000367. A variety of processed data, including raw read counts, log2 FPKMS and the results of our differential expression analysis are available from our lab website  were differentiated into neuroctoderm, endoderm and mesoderm using defined culture conditions as described previously (JPG)Click here for additional data file.Figure S2RT-PCR for Sendai viral genome and transgenes in a subset of lines. Gels show results of RT-PCR using viral primer sets as described in the CytoTune-iPS reprogramming kit (Invitrogen) in line S4SK4 (passage 3). Results are shown are for Sendai virus genome (SeV: 181 bp amplicon), Sendai-derived exogenous Oct3/4 (O: 483 bp amplicon), Sox2 (S: 451 bp am- plicon), Klf4 (K: 410 bp amplicon) and cMyc (M: 532 bp amplicon).(PDF)Click here for additional data file.Figure S3The number of sequenced reads for each sample. Polyadenylated RNA was extracted from each cell culture and multiplexed cDNA libraries were synthesized. For each sample, we performed 75 bp paired end sequencing on the Illumina HiSeq2000 platform. In total we generated 7.3 billion reads, with between 85.3 and 229.8 million reads sequenced in each sample.(PNG)Click here for additional data file.Figure S4The number of reads mapped onto the reference genome for each sample. We mapped reads to assembly h37 of the human genome using Bowtie2 and constructed spliced alignments using Tophat2. Following read alignment and QC filtering, between 49% and 89% of reads mapped uniquely to the human genome.(PNG)Click here for additional data file.Figure S5Distribution of FPKMs in adult, IPS and ESCs. Distribution of log10 (FPKM+1) for all known protein coding genes from ENSEMBL. Each line shows the distribution for a single sample, with the heavier line showing the mean for each cell type. Inset shows the probability that gene is classified as coming from the low/repressed mode of the FPKM distribution estimated using a two component Gaussian mixture model to classify genes into active or repressed. Left panel shows distribution for all genes, right panel excluding the top 1% expression genes.(PDF)Click here for additional data file.Figure S6Percentage reads mapping to LINE and LTRs elements Bars show the percentage of total mapped reads that map to LINE and LTR repetitive elements outside known transcribed regions as annotated in the UCSC repetitive elements track. Blue denotes adult cells, orange denotes IPS cells and green denotes ESCs.(PDF)Click here for additional data file.Figure S7Variance component analysis and differential expression (DE) analysis excluding highly expressed genes (upper 1%-tile). (a) Correlation heatmap without upper 1%-tile highly expressed genes (b) Result of variance component analysis without upper 1%-tile highly expressed genes. (c) P-value comparison with original DE analysis. Each panel shows scatter plot of the DE minimum P-values without upper 1%ile highly expressed genes (X-axis) against original minimum DE P-values (Y-axis) for each tissue. Gray vertical and horizontal lines show 5% FDR.(PDF)Click here for additional data file.Figure S8Variance component analysis and differential expression (DE) analysis with genes without highly expressed genes (upper 5%-tile). (a) Correlation heatmap without upper 5%-tile highly expressed genes (b) Result of variance component analysis without upper 5%-tile highly expressed genes. (c) P-value comparison with original DE analysis. Each panel shows scatter plot of the DE minimum P-values without upper 5%-tile highly expressed genes (X-axis) against original minimum DE P-values (Y-axis) for each tissue. Gray vertical and horizontal lines show 5% FDR.(PDF)Click here for additional data file.Figure S9Percentage of total fragments mapping to 13 mitochondrial protein coding genes. Bars show the percentage of total reads mapping to known mitochondrial genes in all samples in our data. Blue denotes adult cells, orange denotes IPS cells and green denotes ESCs.(PDF)Click here for additional data file.Figure S10Mitochondrial gene expression. Correlation heatmap of log2 FPKMs for 13 mitochondrial protein coding genes. Map elements show Spearman correlation coefficients.(PDF)Click here for additional data file.Figure S11Heatmaps of log2 FPKMs for partial transcriptional memory (PTM) genes deter- mined by the differential expression analysis. (a) Partial transcriptional memory genes in F- iPSCs, (b) K-iPSCs and (c) E-iPSCs. We note that, although patterns of expression across most lines are broadly consistent with one another, line K-iPSC-S2-1-1 forms an outlier from the other K-iPSCs(PDF)Click here for additional data file.Figure S12Coverage depth plots of core pluripotency marker genes. Plots show read coverage of three core pluripotency markers, SOX2, NANOG and OCT4 from left to right.(PDF)Click here for additional data file.Figure S1310(FPKM) in all genes (black lines) and in genes that were detected as differentially expressed  in our analysis.Mean expression levels of differentially expressed genes. Plots show the densities of log(PNG)Click here for additional data file.Figure S14Coverage depth plots of genes driving Gene Ontology enrichments in F- and K-iPS cells. Plots show coverage depth for four genes, KRT5, KRT14, MESP1 and MESP2, that were annotated with the most significant Gene Ontology term enrichments (“hemidesmosome assembly” and “mesoderm migration involved in gastrulation”) in K-iPS cells and F-iPS cells, respectively. Coverage depth was truncated at 500 reads per bp.(PDF)Click here for additional data file.Figure S15Scatterplot of transcript FPKMs between MISO and Cufflinks. Plotted is the distribution of FPKMs of all known annotated transcripts estimated by Cufflinks (X-axis) against MISO (Y-axis). Overall, the FPKM estimation is consistent so that many transcripts are seen on the diagonal line. However, there are also a certain amount of transcripts only enriched in one of the two methods.(JPG)Click here for additional data file.Figure S1610 of minimum P-values among the four alternative hypotheses against the maximum log2 fold-change of average transcript expression levels between iPSCs and ESCs. The colours of the points indicate the differential expression categories into which a gene was classified. Dashed lines show twofold enrichment of mean expression levels between iPSCs and ESCs. The FDR threshold was calculated by the permutation scheme as in the differential expression analysis.Volcano plots of differential isoform expression. Plots show log(PNG)Click here for additional data file.Figure S17Result of population stratification for our samples (S2/S4/S5/S9) with 1000 Genomes Project data. Principal component analysis was performed using Eigenstrat (PNG)Click here for additional data file.Figure S182 relative enrichment, ilF, against the mean GC content of bin l for all samples. The red line shows the fitted spline function, ilF (see GC content influences inference of expression levels from RNA-seq. Plotted is the log(JPG)Click here for additional data file.Figure S19Relationship between precision and expression level. Plotted is the distribution of log τj against average of log2 normalised FPKMs y ˜j across all samples .(PNG)Click here for additional data file.Text S1Supplementary methods.(PDF)Click here for additional data file.


Plasmodium falciparum contributes to ~200,000 child deaths annually, mainly due to low birth weight (LBW). Parasitized erythrocyte sequestration and consequent inflammation in the placenta are common attributes of placental malaria. The precise molecular details of placental changes leading to LBW are still poorly understood. We hypothesized that placental malaria may disturb maternofetal exchange of vitamins, lipids, and hormones mediated by the multi-ligand (n ~ 50) scavenging/signaling receptor megalin, which is abundantly expressed in placenta but was not previously analyzed in pregnancy outcomes. We studied abundance of megalin and its intracellular adaptor protein Dab2 by immunofluorescence microscopy in placental biopsies from Ugandan women with (n = 8) and without (n = 20) active placental malaria. We found that: (a) abundances of both megalin (p = 0.01) and Dab2 (p = 0.006) were significantly reduced in brush border of syncytiotrophoblast of infected placentas; (b) amounts of megalin and Dab2 were strongly correlated ; (c) abundances of megalin and Dab2 (p = 0.046) were reduced in infected placentas from women with LBW deliveries. This study provides first evidence that placental malaria infection is associated with reduced abundance of megalin transport/signaling system and indicate that these changes may contribute to the pathology of LBW.Placental malaria caused by  P. falciparum infection, pregnant women can suffer from placental malaria (PM), where parasitized erythrocytes (PE) sequester in the placenta2 for extensive molecular exchange47810During In this respect the glycoprotein megalin  is a particularly interesting candidate. It is a large (~600 kDa) multi-ligand single-spanning trans-membrane endocytic receptor with substantial physiological functions. It belongs to an ancient1615201916172216In epithelial cells, megalin is transferred with its cargo to the base of microvilli and is internalized through coated-pit endocytosis. We have demonstrated earlier that megalin interacts with a phosphotyrosine-interacting domain of the adaptor protein Dab22528283015193228Megalin interacts with an enormous suite of about 50 extracellular ligands which belong to several unrelated groups of proteinsA few interesting observations suggest that the megalin system might be affected in PM. When the megalin gene is knocked out, the number of microvilli and the amount of endocytosis are significantly reduced in kidneyTo get insights into the megalin system during PM, we determined the abundance of megalin and its intracellular adaptor protein Dab2 in formalin-fixed paraffin-embedded placental samples obtained from women living in malaria endemic areas of Uganda, with and without PM, who had normal and low birth weights of newborn babies .Samples were selected based on availability of samples from previous study35Immunofluorescence studies confirmed substantial expression of megalin in the syncytiotrophoblast of human term placenta as previously reported17The abundance of both megalin and Dab2 was reduced in the syncytiotrophoblast brush border in malaria-infected placentas by indirect fluorescence microscopy, with statistical significance for both proteins . Data onWhen data were stratified by the presence of placental inflammation in malaria-infected samples (n = 3) against samples without infection and inflammation, a similar decrease in megalin and Dab2 abundance was observed , with stAlso, irrespective of PM status of samples, statistically significant positive correlation of megalin and Dab2 expression in the brush border region was observed . A similModerate correlation of birth weight with abundance of megalin and Dab2 in the brush border of the syncytiotrophoblast was identified, though below the level of statistical significance . When Dab2 expression levels were stratified by median Dab2 abundance, birth weight was 350 g greater in those with high (above median) versus low (below median) placental expression, although the difference was not statistically significant (p = 0.095) (a woman with stillbirth and no detectable Dab2 expression was excluded). Abundance of both megalin and Dab2 is substantially reduced in the brush border of malaria-infected placentas obtained from LBW deliveries , with stPM-related changes in the maternofetal interface of the placenta are associated with LBW91012Specifically, the study provides evidence that megalin and Dab 2 abundance is reduced in placentas with active infections . SimilarFurther, amounts of megalin and Dab2 are positively correlated in the placental brush border . It has While all samples with placental infection and LBW had low or undetectable megalin and Dab2 (3/3), there were 4/11 and 3/11 normal controls with NBW that also had low or undetectable levels of megalin and Dab2, respectively . In addi20Nevertheless, categorical analysis of protein abundance data stratified by LBW and placental infection revealed statistically significant reduction for Dab2 abundance  compared to non-infected placental samples with NBW . This reMaternal cholesterol is essential for fetal growthvitamins in complexes with their carrier proteins in kidneys19Megalin also plays an important role in vitamin homeostasis by re-absorbing various Further, megalin co-expresses on the syncytiotrophoblast surface with several receptors specialized for interactions with hormones/growth factors  including insulin-like growth factor-1 (IGF-1), parathyroid hormone and epidermal growth factorHow does PM affect the megalin system? One potential mechanism might be that sequestration of PE through surface-expressed PfEMP1 parasite adhesins may cross-link the corresponding host adhesion receptor, chondroitin sulfate ATGFβ negatively regulates endocytosis of albumin through megalin and cubilinin vitro. These studies may contribute to development of therapy alleviating pathological effects of megalin system dysfunction, for example by affecting transcription of relevant genes, and/or by supplementation of nutrients and ligands.This study is limited by the number of samples investigated. A logical continuation would be a similar study with larger sample size and samples obtained from various geographical areas, taking into account a larger set of variables, including timing of malaria infection during pregnancy and nutritional status. Further, effects of PE adhesion to cytotrophoblast primary cells or cell lines, like BeWo cells, on megalin system functioning in the absence and presence of monocytes/macrophages may test our hypothesis In summary, it is likely megalin is involved in fetal growth via a complex network of regulatory activities participating in endocrine, paracrine, and autocrine signaling by interacting with and internalizing various ligands of maternal and fetal origin. We propose that dysregulation of these networks in the placenta affects development of the fetus. To our knowledge, this is the first report linking the abundance of placental megalin system proteins with the birth weight of newborn babies and associating PM infection with changes in abundance of megalin system proteins.Written informed consent was provided and specimens were collected under approval by the Uganda National Committee for Science and Technology (#HS207). The use of coded specimens was approved and deemed not human subjects research by the University of Washington Human Subjects Division (#35425) and by the Florida Atlantic University Institutional Review Board (#801504). All experiments were performed in accordance with the relevant guidelines and regulations.P. falciparum parasitemia greater than 250 × 103/μL, severe anemia (hemoglobin < 7 g/dL), or severe or complicated malaria needing parenteral treatment. As previously describedSpecimens were from a cohort of a nested randomized control trial of artemether-lumefantrine versus quinine to treat malaria in pregnancy conducted in 2006–2009 in Mbarara, Uganda, as previously describedFor the immunofluorescence assays, sections of 6 μm were prepared from paraffin embedded blocks of formalin-fixed placentas obtained from these women. Relevant clinical parameters of placenta samples are described in Primary antibodies used were monospecific purified rabbit antibodies, raised against megalin C-terminal 17-mer peptide (2 independently purified preparations) and Dab2 phosphotyrosine interaction domain, which were described and characterized previouslyThe paraffin embedded tissue samples were rehydrated by a series (3 min each) of graded xylene/ethanol washes , followed by a final wash in phosphate buffered saline (PBS) for 5 min. The rehydrated slides were then heated in Tris-EDTA buffer  and microwaved to boiling at full power and then maintained at 50% power for 8 minutes and cooled in PBS40Slides were imaged using a confocal microscope (Zeiss LSM 700 equipped with Plan-Apochromat 63x/1.40 Oil DIC M27 objective) and processed using Zen 2012 Blue software . Images were captured using identical conditions to allow for quantification of signal strength. Collected images of syncytiotrophoblast were selected for the brush border only (~1 μm from the surface to inside of the cell) or the entire syncytiotrophoblast (up to ~10 μm in depth and excluding nuclei). The process of selection is illustrated in Statistical analyses were performed using GraphPad Prism 6  statistical software using non-parametric statistics: Mann-Whitney tests (two-tail) for group differences and Spearman tests for analyses of correlations. P value of <0.05 was considered significant. Qualitative level of significance indicated by stars: *p < 0.05, **p < 0.01, ***p < 0.001.How to cite this article: Lybbert, J. et al. Abundance of megalin and Dab2 is reduced in syncytiotrophoblast during placental malaria, which may contribute to low birth weight. Sci. Rep.6, 24508; doi: 10.1038/srep24508 (2016).


Here we describe a live-imaging system in emerging Arabidopsis thaliana leaves that uses a PIN1:GFP reporter to visualize auxin transport routes and an Athb8:YFP reporter as a marker for vascular commitment. Live-imaging revealed common features initiating the formation of all higher-order veins. The formation of broad PIN1 expression domains is followed by their restriction, leading to sustained, elevated PIN1 expression in incipient procambial cells files, which then express Athb8. Higher-order PIN1 expression domains (hPEDs) are initiated as freely ending domains that extend toward each other and sometimes fuse with them, creating connected domains. During the restriction and specification phase, cells in wider hPEDs are partitioned into vascular and non-vascular fates: Central cells acquire a coordinated cell axis and express elevated PIN1 levels as well as the pre-procambial marker Athb8, while edge cells downregulate PIN1 and remain isodiametric. The dynamic nature of the early selection process is underscored by the instability of early hPEDs, which can result in dramatic changes in vascular network architecture prior to Athb8 expression, which is correlated with the promotion onto vascular cell fate.Self-regulatory patterning mechanisms capable of generating biologically meaningful, yet unpredictable cellular patterns offer unique opportunities for obtaining mathematical descriptions of underlying patterning systems properties. The networks of higher-order veins in leaf primordia constitute such a self-regulatory system. During the formation of higher-order veins, vascular precursors are selected from a homogenous field of subepidermal cells in unpredictable positions to eventually connect in complex cellular networks. Auxin transport routes have been implicated in this selection process, but understanding of their role in vascular patterning has been limited by our inability to monitor early auxin transport dynamics  The vascular system of plants is a network of strands composed of xylem and phloem that transport water and photoassimilates, respectively Esau, . In leafPIN family control the magnitude  III family of putative homeobox transcription factors , followed by reproducibly positioned, looped side-branches (second order) and then by variable branches of 3, 4, and 5th order, the latter collectively referred to as the higher-order vein network . Seedling leaf primordia were visualized beginning at 3 DAG. “Days after germination”  for a 16-h light cycle at 22°C, followed by an 8-h dark cycle, 18°C.Seeds were sterilized as described . GFP and VENUS were visualized with the 488 nm line of an Argon laser at 25% of output and 4–10% transmission, and with either a 505–530 or a 500–550 nm band-pass filter. PIN1:GFP Athb8::YFPer samples were observed with a Zeiss LSM510 META laser module laser confocal microscope . GFP was visualized with the 488 nm line of an Argon laser at 23% transmission, with a 505–530 band-pass filter; YFP was visualized using the 514 nm laser line of the same Argon laser at 17% transmission, with a 535–590 band-pass filter. Signal-to-noise ratio was increased during image acquisition by 2-frame averaging  for 24 h at room temperature. Samples were stored in 70% (v/v) ethanol at 4°C. For microscopy, rehydrated samples were dissected under water, mounted abaxial side up in chloral hydrate:glycerol:water  and viewed under dark-field illumination . Images were acquired with a Canon EOS D60 digital camera .PIN1 intensity data was obtained from samples expressing only the PIN1:GFP transgene by measuring the GFP signal intensity values along the proximal-distal axis of each freely-ending hPED using the Profile tool of the imaging software for the Zeiss LSM510 confocal microscope . Only differences in signal intensity that were greater than 20 relative units were counted as different.Cell length data was obtained by measuring the length of cells along the long axis of each hPED, using Zeiss LSM Image Browser software . Only differences in cells of lengths that were greater than 1 μm were counted as different. The hPED strand was defined as the cells between (a) and (b), where (a) is the point of connection of the hPED to the lower order vein, and (b) can be either the: (i) point of connection to a second lower order PED in the case of a connected hPED, or (ii) the branch point from which a second hPED emerged or (iii) the free end of the hPED, whichever occurred first. It was noted that the cell linking the hPED to the connecting lower order PED remained shorter than its neighboring cell (by more than 1 μm) in 72% of cases (58/81 hPEDs). This suggests that this cell is under special developmental constraints due to its unique position as a linker and as such its vascular differentiation might be somewhat uncoupled from that of the rest of the strand. This is consistent with observations that cells in this linker position also differentiate into xylem later than the rest of the strand . Due to these observations, the length of the linker cell was omitted when determining the cell length gradient along the proximal-distal axis of the hPEDs.All images for figures were assembled using Adobe Photoshop 7.0 .PIN1 expression domains, preceding the emergence of higher-order veins, we developed a live imaging system to visualize PIN1 expression domains (PEDs) in individual, growing Arabidopsis first leaf primordia from 3 to 5 days after germination  vs. 15.67 ± 9.10 mm2 for non-imaged controls . As this raised the possibility that altered leaf growth dynamics could change PED dynamics, we compared the developmental outcome of conventional and live-imaging visualization of second-order PED formation. We examined its emergence under live imaging conditions and found that in 20 out of 22 live-imaged primordia, the ontogeny of the second loop PED was found unchanged relative to that observed in singly imaged samples  and five hPEDs (9%) showed no PIN1 expression gradient . A reverse distal to proximal (D-P) PIN1 expression gradient, wherein the most recently added cells at the free end of the hPED had stronger PIN1 signal intensity than older cells at the base of the hPED, was never observed. Similarly, fifty out of eighty-one freely-ending hPEDs (62%) showed a uniform proximal-distal cell length gradient, such that cell length increased progressively going from the oldest, proximal cells near the hPED base to the most recently added cells at the distal end of the hPED  that extended in only one direction. Instead, a hPED could be initially two or more cells wide and a new hPED could form on an existing hPED while the original hPED was still and freely ending Figure . Of the  Figures .PIN1 and localize it preferentially to the basal membrane  arose as wider PEDs (>2-cell wide) that narrowed over time, to a final width of only 1- to 2-cells Figures . Narrowi Figures  vs. 4M.  Figures . Cells a Figures . hPEDs t Figures , centrob Figures , indicat Figures . Taken tDR5 auxin response marker together with PIN1 across the leaf lamina. As shown in Figure DR5 expression at their associated epidermal auxin convergence points (CPs), while the first loop is not. While no DR5 expression was ever detected in the first loop CP .Intriguingly, wider and early branching hPEDs were not evenly distributed throughout the leaf lamina, but were significantly more frequent in the intercostal areas enclosed by the second and third loop than in the first loop Table . To test Figures , strong  Figures , and in  Figures . If one PIN1 expression that extends outwards and eventually connects to another hPED at each end—were never observed. Therefore, we conclude that connected hPED are usually formed via the fusion of an extending, freely ending hPED with a static, pre-existing PED or another extending hPED. Furthermore, newly formed connected hPEDs were on average only 7.1 cells long (n = 31), indicating that hPEDs become connected in an environment of relatively small cell numbers (see Discussion).The formation of closed vein networks via the fusion of incipient freely-ending vein segments has previously been observed in the lower order vein loops  and leaf thickness at the time of connected hPED formation made it difficult to clearly determine PIN1 polarity in all cells of newly connected hPEDs. However, in all 9 cases in which hPED fusion had occurred relatively early in leaf development and PIN1 polarity could be distinguished in the cells of the hPED pre-and post-fusion, it was observed that before fusion, all cells of the freely-ending hPED(s) had PIN1 polarity directed toward their connecting lower order vein Figures . Post-fuPIN1 expression dynamics of all subepidermal cells during vascular patterning, regardless of whether these cells are ultimately recruited into the final vein pattern. Our analysis surprisingly but consistently revealed that hPEDs are often unstable soon after their formation: PIN1 expression in cells of the hPED can be transient, resulting in connections that are made and then lost  visualized at intervals of 24 h or less over a period of 3 days. These dynamic changes in PIN1 expression were only observed in hPED cells that were polygonal in shape and had relatively low PIN1 expression , and conversely, most PEDs composed of polygonal cells with low PIN1 expression did not express Athb8::YFPer  and polygonal PED cells expressing Athb8::YFPer (13/147) were also observed at low frequency, indicating that Athb8::YFPer expression does not perfectly correlate with this cell shape transition. Nonetheless, the timing of onset and spatial pattern of Athb8::YFPer expression made it the best available marker of cells that have undergone vascular specification.In order to better understand the regulation of hPED dynamics, we asked whether increased hPED stability coincided with the onset of expression of one of the earliest markers of vascular identity, the preprocambial marker  Figures . The onsAthb8::YFPer was always strongly expressed in the central cells with basal PIN1 polarity that persisted over time (observed in 21/21 cases), and was almost always absent from the edge cells with central PIN1 polarity that disappear . Conversely, most hPEDs that disappeared during the same live imaging period did not express Athb8::YFPer (20/22). In two cases, however, Athb8::YFPer appeared to be expressed in an polygonal cell that later disappeared from the hPED (data not shown), indicating that Athb8::YFPer could not be used as a marker of irreversible vascular commitment. We nonetheless enquired whether expression of Athb8::YFPer in an incipient vascular strand was correlated with enhanced stability of PIN1 expression and persistence of the strand onto final vascular differentiation. Out of 292 hPEDs examined, we observed that 26 hPEDs (8.9%) did not differentiate as xylem strands in the final vascular pattern of the leaf. Out of these 26 disappearing hPEDs, 21 (80.8%) were hPEDs that expressed PIN1 alone without Athb8::YFPer, whereas 5 (19.2%) expressed both PIN1 and Athb8:YFPer, indicating that hPEDs expressing Athb8 are significantly more likely to differentiate as mature veins , that usually did not express Athb8::YFPer . Because variability among transgenic reporter genes can easily account for small differences, these data are compatible with a stringent link between expression of the Athb8::GUS marker and vascular commitment (Scarpella et al., PIN1, whose eventual elevated and sustained expression then leads to the expression of the vascular commitment marker, Athb8, generating a completely unpredisposed, yet continuous network pattern. These features characterize higher-order venation patterning as a highly informative self-regulatory process, in which important molecular determinants can be recorded at high spatial and temporal resolution and whose potential in the breadth of plant species remains to be explored.Our data demonstrate there is not only a reproducible ontogeny from wide to narrow hPEDs, but also one between high The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


BMPR2, BMPR1B, ACVRL1, ENG, SMAD9, CAV1, and KCNK3) from Finnish PAH patients. This study represents the first clinical study with OS-Seq technology on patients suffering from a rare genetic disorder. We analyzed DNA samples from 21 Finnish PAH patients, whose BMPR2 and ACVRL1 mutation status had been previously studied using Sanger sequencing. Our sequencing panel covered 100% of the targeted base pairs with >15× sequencing depth. Pathogenic base substitutions were identified in the BMPR2 gene in 29% of the Finnish PAH cases. Two of the pathogenic variant-positive patients had been previously tested negative using Sanger sequencing. No clinically significant variants were identified in the six other PAH genes. Our study validates the use of targeted OS-Seq for genetic diagnostics of PAH and revealed pathogenic variants that had been previously missed using Sanger sequencing.The genetic basis of pulmonary arterial hypertension (PAH) among Finnish PAH patients is poorly understood. We adopted a novel-targeted next-generation sequencing (NGS) approach called Oligonucleotide-Selective Sequencing (OS-Seq) and developed a custom data analysis and interpretation pipeline to identify pathogenic base substitutions, insertions, and deletions in seven genes associated with PAH ( The role of genetic diagnostics in the evaluation of patients with Mendelian disorders is increasing rapidly in all fields of medicine. Understanding the underlying genetic causes of inherited diseases can contribute to the treatment and follow-up strategies, and may bring psychological comfort for the patient. Moreover, genetic diagnosis of the proband can allow effective risk assessment of family members, rationalize follow-up strategies, and enable earlier interventions.Pulmonary arterial hypertension (PAH) is a severe and progressive disease characterized by vascular remodeling of small pulmonary arteries resulting in an increase in pulmonary arterial pressure, and eventually leading to right heart failure (Runo and Loyd BMPR2) gene, a member of the transforming growth factor beta (TGF-β) superfamily, are currently considered as main causes for the pathogenesis of PAH . The disease penetrance could be even higher as the disease manifestation may be delayed for 75 years and express phenotypic variability , activin-like kinase-type 1 , endoglin (ENG), smad family member 9 (SMAD9), caveolin 1 (CAV1), potassium channel subfamily K, member 3 (KCNK3), and NOTCH3 have been described in the development of the disease  technology  and HPAH (N = 3) patients were selected for the study.Blood samples for genetic studies were obtained from Finnish patients diagnosed with PAH under the approval from the Ethical Committees of the five University Hospitals and by the Ministry of Social Affairs and Health of Finland. All participants provided the written informed consent for genetic analysis as previously reported  and all validated information about the mutations were incorporated into the bioinformatics pipeline . Genomic DNA samples were fragmented using Fragmentase  and sequencing libraries were prepared using NEBNext Ultra (New England Biolabs) and PCR amplified. Target-specific oligonucleotides were designed to capture all coding exons, exon-intron boundaries, and known intronic mutations in the PAH-associated target genes and obtained from Integrated DNA Technologies . Exon definitions for target genes were derived from the CCDS. The targeting and sequencing of DNA was automated using the MiSeq Desktop Sequencer . Pathogenic and likely pathogenic mutations found in the patient samples were confirmed using Sanger sequencing.Primary data analysis was carried out by Illumina’s MiSeq Reporter software using the generateFASTQ workflow, that generates a single FASTQ file with base calls and Phred quality scores for R1 and R2 reads, separately. The data were transferred to a dedicated pipeline server for downstream analysis, which started with extraction of data for individual samples using a proprietary demultiplexing algorithm that analyses the 9 bp long index adapter sequence of each read to determine sample identity. Sequence reads were then trimmed to exclude adapter and capture oligo sequences using a proprietary algorithm that estimates the size of captured target DNA based on the genomic coordinates of read mate-pairs. Low-quality data were identified and removed using the Prinseq package (Schmieder and Edwards http://evs.gs.washington.edu/EVS) and the Exome Aggregation Consortium (ExAC), Cambridge, MA (http://exac.broadinstitute.org). PolyPhen, SIFT  in PAH-associated genes. Mutations that have been shown to be associated with PAH were collected from original research publications and existing mutation databases and queried using patients’ variants. Population frequencies for detected variants were derived from the Exome Variant Server (NHLBI GO Exome Sequencing Project (ESP), Seattle, WA  located in a kinase domain and c.2696G>C (p.Arg899Pro) located in C-terminal cytoplasmic domain   is a practical solution for diagnostic-grade and comprehensive analysis of the clinically relevant genomic regions. While sequencing whole genomes or exomes are powerful tools for discovery and research, they fall short meeting the quality requirements of genetic diagnostics. Typical genome with 30× coverage or exome even with 100× coverage contains gaps and regions where sequence information remains unreliable  sequencing depth was 791, which illustrates the confidence in calling the genotypes. Sequencing coverage defines the breadth of the genomic area, which has sequencing depth exceeding a specific threshold. Sequencing coverage is related to sensitivity of detecting variants in the target regions  of pathogenic or likely pathogenic variants in nts Table, furtherACVRL1 or ENG mutations are often found in PAH patients with family history of hereditary hemorrhagic telangiectasia (HHT)  also carried a pathogenic deletion in the BMPR2 gene  , from two patients is interesting as it leads to the replacement of threonine by methionine resulting a suppression of ENG expression (Bourdeau et al. nts Table. The patBMPR2 mutations are associated with an earlier age of disease onset, regardless of familial history, and with more severe disease phenotype. Furthermore, patients with BMPR2 mutations are less likely to response to vasodilators than mutation-negative patients (Austin et al. ACVRL1 gene show more rapid disease progression than patients with BMPR2 mutations, despite responding to vasodilators at the time of diagnosis (Girerd et al. BMPR2 and ACVRL1 mutations have a shorter survival time or earlier need for lung transplantation (Austin et al. BMPR2 gene are diagnosed in much younger age and seem to have a significantly shorter survival time compared to patients with truncating mutations (Austin et al. BMPR2 mutation carriers had a worse prognosis compared to female mutation carriers (Liu et al. BMPR2 mutation, show distinct response to vasodilators than adults, and are more likely to associate with other genetic syndromes (Ma and Chung Characterization of mutations in PAH enables establishing estimates on prognosis. Utilization of novel targeted OS-Seq approach increases the diagnostic efficacy by offering better quality and faster turnaround time. As shown in this study, OS-Seq technology proved to be a practical and effective tool for genetic profiling of Finnish patients with PAH. As genetic testing is increasingly applied in clinical practice, it is important to acknowledge the requirements of quality and performance of available genetic tests. Diagnostic-grade deep sequencing with 100% covered target base pairs is becoming a standard requirement in today’s clinical genetic testing. The high incidence of disease mutations in patients with IPAH and HPAH and elevated estimates of disease penetrance are supporting the utilization of genetic testing as routine procedure in the evaluation of patients with IPAH or HPAH. As in other hereditary cardiovascular diseases genetic diagnosis can significantly rationalize the risk stratification and follow-up strategies in the family and can have impact in estimating index patient’s prognosis. New sequencing strategies and bioinformatics tools are enabling diagnostics with faster turnaround times and decreased cost.


This study explored inter-relationships between vertebral fracture, thoracic kyphosis and trunk muscle control in elderly people with osteoporosis. Osteoporotic vertebral fractures are associated with increased risk of further vertebral fractures; but underlying mechanisms remain unclear. Several factors may explain this association, including changes in postural alignment (thoracic kyphosis) and altered trunk muscle contraction patterns. Both factors may increase risk of further fracture because of increased vertebral loading and impaired balance, which may increase falls risk. This study compared postural adjustments in 24 individuals with osteoporosis with and without vertebral fracture and with varying degrees of thoracic kyphosis. Trunk muscle electromyographic activity (EMG) associated with voluntary arm movements was recorded and compared between individuals with and without vertebral fracture, and between those with low and high thoracic kyphosis. Overall, elderly participants in the study demonstrated co-contraction of the trunk flexor and extensor muscles during forwards arm movements, but those with vertebral fractures demonstrated a more pronounced co-contraction than those without fracture. Individuals with high thoracic kyphosis demonstrated more pronounced alternating flexor and extensor EMG bursts than those with less kyphosis. Co-contraction of trunk flexor and extensor muscles in older individuals contrasts the alternating bursts of antagonist muscle activity in previous studies of young individuals. This may have several consequences, including altered balance efficacy and the potential for increased compressive loads through the spine. Both of these outcomes may have consequences in a population with fragile vertebrae who are susceptible to fracture. Osteoporosis is a significant public health problem particularly in women, with vertebral fractures recognised as one of the hallmarks of the condition. The “vertebral fracture cascade” refers to the 4–7 fold increased risk of subsequent vertebral fractures and increased risk of fracture in the appendicular skeleton  muscles, which increase spinal load, in individuals with vertebral fractures than those without Investigation of postural adjustments is an ideal model to investigate changes in trunk muscle activation in osteoporosis. Analysis of anticipatory postural adjustments in association with arm movements This study aimed to investigate differences in activity of the trunk muscles using electromyography (EMG) during postural adjustments in older individuals with primary osteoporosis. Participants were divided into groups based on; (i) the presence or absence of osteoporotic vertebral fractures, and (ii) the magnitude of thoracic kyphosis (high vs. low).Twenty-four community-dwelling women with primary osteoporosis and more than five years post menopause were recruited for this study. Women were recruited from the community via local advertising  and by approaching osteoporosis peer support groups in metropolitan Melbourne. Participants were also recruited from local private medical specialist clinics and public outpatient clinics and bone densitometry units at the Royal Melbourne Hospital and Broadmeadows Hospital.Subject to meeting the inclusion criteria , participants were divided into fracture (n = 10), and no-fracture (n = 14) groups. Participants were also independently grouped based on low (n = 12) and high (n = 12) thoracic kyphosis. Exclusion criteria included any other medical conditions that affect bone metabolism or balance, or participation in any high intensity exercise that could affect trunk control. Physical activity was assessed using the Physical Activity Scale for the Elderly (PASE) Osteoporosis was diagnosed from bone densitometry results using World Health Organization criteria p>0.092) (p = 0.660) or Cobb (p = 0.125) measurements (p>0.103) (p>0.05). Ethical approval was granted by the Institutional Human Research Ethics Committee and all participants provided written, informed consent.Participants were grouped into two categories based on the: i) presence of vertebral fracture; and ii) degree of thoracic kyphosis. For the first analysis, based on fracture, there were no differences in demographic characteristics between groups categorised in this manner  . Seventeurements . For thep>0.103) . Pain wand order Butterworth 12dB/octave filter) and sampled at 2000 Hz. A notch filter was used at 50 Hz. Data were recorded and stored using a Power1401 data acquisition system and Spike 2 (v 4.10) software , and exported for analysis with Matlab 6.5.0 .Electromyographic activity (EMG) was recorded using pairs of Ag/AgCl adhesive electrodes , placed along the muscle (inter-electrode distance - 2 cm). Recordings were made of obliquus internus (OI), and externus abdominis (OE), rectus abdominis (RA), and erector spinae (ES) at L3 and T7 EMG Participants rapidly moved their right arm to disturb balance while standing with feet shoulder-width apart, toes aligned forward, and equal weight through both feet. Participants move their arm forward (shoulder flexion to ∼60°) or backwards (shoulder extension to ∼40°) as rapidly as they could in response to a light that indicated the direction of movement. Participants were instructed to relax between arm movements. The light was triggered manually and recorded with EMG data. Direction of arm movements was randomized for 10 trials in each direction (forward or backward), resulting in a total of 20 unique arm movements performed in a random sequence. An accelerometer  was fixed to the dorsum of the right hand to provide information regarding movement onset and arm displacement. Five practice trials were undertaken with each participant to ensure they understood the protocol.p = 0.232) or groups based on thoracic kyphosis (p = 0.203) we had no reason to suspect any systematic difference in subcutaneous tissue between groups. We argue that, with some caution, interpretation using the non-normalised EMG data could be made for an exploratory analysis in conjunction with the analysis of EMG pattern from data normalised to peak EMG.Deltoid EMG provided information about the direction and timing of arm movements to which trunk EMG could be related. The time of onset of deltoid EMG was identified visually as this method has been shown to be reliable and less affected by background EMG than many statistical based methods pattern of muscle activation and enabled identification of the epochs during which EMG activity increased above baseline (epoch 0). The pattern of trunk muscle activation was qualitatively compared between groups. In addition we undertook an exploratory analysis on non-normalised EMG data to compared EMG amplitude between groups for each muscle and each arm movement using independent t-tests. As this study was exploratory in nature, it was not considered appropriate to apply adjustments for multiple comparisons as this has been argued to mask potentially important differences p<0.05.Repeated measures analysis of variance (ANOVA) was used to compare normalised EMG amplitude between Epochs and arm movement Directions for each muscle. This analysis provided detailed information about the p<0.05, filled shapes in epochs 3 to 5 (150-0 ms prior to deltoid) for both trunk flexor and extensor muscles.When participants moved their arm forwards, bursts of EMG activity of the trunk flexor and extensor muscles were initiated almost simultaneously. Such overlapping activity of antagonist muscles .Backwards movement of the arm was not accompanied by co-contraction of the trunk muscles. Instead, there was a burst of trunk flexor muscle EMG and either no change or decreased activity of the extensor muscles (down-going panels in epoch 4 vs. 3).Some differences in muscle activation were evident between fracture and no-fracture groups . Irrespeepochs 5 to 7 (50 ms prior to and 100 after deltoid onset) in the no-fracture group. RA EMG increased from baseline during epochs 7 to 10 (100 to 250 ms after deltoid) in the fracture group, but there was a decrease in RA EMG in epoch 4 in the no-fracture group. Activity of ES T7 decreased below baseline during epochs 3 to 6 (150 ms prior to and 50 ms after deltoid onset) in the fracture group, while there was no change in ES T7 activity from baseline in the no-fracture group. Non-normalised EMG amplitudes was not different between fracture groups .When participants were grouped based on the magnitude of thoracic kyphosis, activity of trunk flexor muscles .Analysis of non-normalised EMG amplitude revealed less OE EMG in the group with high kyphosis, compared to low kyphosis  within the base of support Although co-contraction was increased in forwards arm movements across groups, this overlapping pattern of flexor and extensor muscle activity was not evident during backwards arm movements. This finding may be related to the argument that a forwards arm movements induces a riskier perturbation, as it creates a posterior displacement of the COM. Several authors argue posterior COM displacement induces greater falls risk because of compromised ability to generate ankle torque to counteract the COM displacement in that direction In the present study, the participants with vertebral fracture demonstrated differences in trunk muscle activation compared with those without fracture . This fiDuring backwards arm movements the fracture group reduced paraspinal muscle activity at T7 to below the baseline amplitude . This reWhen participants were grouped according to kyphosis angle the co-contraction strategy during forwards arm movements was evident in both low and high kyphosis groups . HoweverThoracic spine curvature did not differ between those with and without fracture using two radiographic measures of kyphosis . This was surprising as thoracic curvature is expected to increase with fracture. One interpretation is that existing methods lack sensitivity to detect small differences in curvature, and thus alternative measurement approaches have been proposed The main EMG analysis involved normalisation to the peak EMG among trials. This provides a sensitive measure of pattern of activity and formed the primary analysis. In addition, we compared the non-normalised EMG amplitude. Although this has the potential for error due to differences in electrode placement and properties of subcutaneous tissues The results of this study demonstrate postural adjustments of trunk muscles activity with arm movements differ in elderly participants with osteoporosis and patterns of trunk muscle responses varied according to the presence of vertebral fracture and degree of thoracic kyphosis. The tendency to increase trunk muscle co-contraction in this elderly cohort may be detrimental for vertebral loading, especially in individuals with compromised bone strength. Further research is required to establish a temporal relationship between vertebral fracture and differences in trunk control strategies to determine cause and effect. In addition, future research is needed to evaluate whether rehabilitation of trunk control strategies to reduce reliance on trunk stiffening is clinically effective in prevention of subsequent vertebral fractures and falls.


Avian bornavirus (ABV) has been identified as the cause of proventricular dilatation disease in birds, but the virus is also found in healthy birds. Most studies of ABV have focused on captive birds. We investigated 86 free-ranging psittacine birds in Brazil and found evidence for natural, long-term ABV infection. During December 2009–January 2010, we collected samples from psittacine birds brought to rehabilitation centers (Centro de Rehabilitação de Animais Silvestres [CRAS]) in the federal states of São Paulo (CRAS Parque ecológico Tietê–SP) and Mato Grosso do Sul (CRAS Campo Grande–MS) in Brazil. The birds had been rescued or confiscated from illegal animal trade or holding by environmental police. The 86 birds came from 7 species: The 49 birds from CRAS do Parque ecológico do Tietê–SP were euthanized for humanitarian reasons because of severe lesions that impeded reintroduction into the wild. The 37 birds from CRAS Campo Grande–MS were in good condition and were submitted for sampling purposes; however, 1 bird died suddenly. These birds had no signs of digestive or neurologic diseases and were anesthetized with inhalational isoflurane before organ sampling or euthanasia.t) values >36 were considered negative. Genotyping was done according to the method described by Kistler et al. (From dead birds (n = 50), we collected tissue samples from brain, eye, crop, proventriculus, ventriculus, adrenal gland, and heart. From live birds (n = 36), we collected crop biopsy samples from 30. Blood samples (n = 77) and tracheal (n = 78) and cloacal swab (n = 83) specimens were also collected. The collected tissue samples were rapidly embedded in RNALater RNA Stabilizations Reagent  for further molecular analysis. Real-time reverse transcription PCR (rRT-PCR) was used to detect ABV RNA from cloacal swab and crop biopsy specimens from live birds and from brain or proventriculus samples from dead birds  sampled psittacine birds; all were identified as type ABV-4. Of these birds, 12 originated from CRAS Tietê and 14 from CRAS Campo Grande. Most (84.6%) of the infected birds were adults, but nestlings (15.4%) were also affected. Antibodies against ABV were found in 19 psittacine birds, but for 15 of them, titers were low (1:10–1:20) and thus the birds were classified as probably, but not clearly, positive. The remaining 4 birds had titers >1:40 and were classified as ABV-antibody positive. By evaluating hematoxylin and eosin–stained sections from the crop, brain, and proventriculus of 50 birds and crop biopsy specimens from 30 birds, we determined that 11 (14%) birds had mononuclear infiltrates in the ganglia of the proventriculus or crop, which were diagnosed as suspected PDD . Seven oCyanopsitta spixii) and the Lear’s macaw (Anodorhychus leari). The spread of infectious pathogens such as ABV is a constant threat for the survival of these species in the wild and to species conservation projects. Hence, identification of disease agents and prevention of infection is critical. Brazil is known for its variety in free-ranging psittacine birds and is the origin country of endangered species such as the critically endangered Spix’s macaw  had ABV antibodies in blood and ABV RNA in tissue samples (n = 2), cloacal swab specimens (n = 1), or both (n = 1). Other authors have reported ABV RNA and antibodies against ABV in birds without signs of disease  that was introduced into the CRAS Mato Grosso do Sul rehabilitation center 1 day before sampling. Likewise, 6 birds of 3 species  that arrived in the centers 2 months to 1 week before sampling showed either antibodies against ABV or ABV RNA (ABV RNA in cloacal swab specimens reinforces the suggestion of a gastrointestinal/urogenital location for the virus in the free-ranging psittacine bird population in these areas of Brazil. Unless infection during captivity in the rehabilitation centers can be proven, infection in the wild must be considered. We found ABV RNA by rRT-PCR in a white-eyed conure nestling ( ABV RNA . In summary, our findings indicate the apparent existence of ABV infection in the free-ranging psittacine bird population in 2 areas of Brazil. The presence of antibodies in the birds we tested may indicate long-term infection in which ABV viral shedding and transmission are not unusual. Our findings provide evidence of natural infection with ABV in free-ranging psittacine birds, the development of histopathologic lesions typical of PDD, and the occurrence of this disease in the natural habitat of these birds in South America.


Our in vitro data indicate that thiazolidinediones dose-dependently inhibit osteoclastogenesis from bone marrow macrophages, but the inhibitory effect is considerably reduced when bone marrow macrophages are pretreated with RANKL. In vitro mechanistic studies reveal that thiazolidinediones inhibit osteoclastogenesis not by impairing RANKL-induced activation of the NF-κB, JNK, p38 and ERK pathways in bone marrow macrophages. Nonetheless, thiazolidinediones inhibit osteoclastogenesis by suppressing RANKL-induced expression of NFATc1 and c-Fos, two key transcriptional regulators of osteoclastogenesis, in bone marrow macrophages. In addition, thiazolidinediones inhibit the RANKL-induced expression of osteoclast genes encoding matrix metalloproteinase 9, cathepsin K, tartrate-resistant acid phosphatase and carbonic anhydrase II in bone marrow macrophages. However, the ability of thiazolidinediones to inhibit the expression of NFATc1, c-Fos and the four osteoclast genes is notably weakened in RANKL-pretreated bone marrow macrophages. These in vitro studies have not only independently demonstrated that thiazolidinediones exert inhibitory effects on osteoclastogenesis but have also revealed crucial new insights into the molecular mechanism by which thiazolidinediones inhibit osteoclastogenesis.Thiazolidinediones are synthetic peroxisome proliferator-activated receptor γ agonists used to treat type 2 diabetes mellitus. Clinical evidence indicates that thiazolidinediones increase fracture risks in type 2 diabetes mellitus patients, but the mechanism by which thiazolidinediones augment fracture risks is not fully understood. Several groups recently demonstrated that thiazolidinediones stimulate osteoclast formation, thus proposing that thiazolidinediones induce bone loss in part by prompting osteoclastogenesis. However, numerous other studies showed that thiazolidinediones inhibit osteoclast formation. Moreover, the molecular mechanism by which thiazolidinediones modulate osteoclastogenesis is not fully understood. Here we independently address the role of thiazolidinediones in osteoclastogenesis  Types 2 diabetes mellitus (T2DM) accounts for 90–95% of all diabetes cases and causes significant economic and societal burdens globally However, clinical evidence has established that TZD treatments increase the risk of fracture in patients with T2DM Osteoclasts, the multinucleated bone-resorbing cells, not only play a crucial role in skeletal development and adult bone remodeling but are also implicated in the pathogenesis of various bone disorders RANKL promotes osteoclastogenesis in part by committing BMMs into the osteoclast lineage in vitro assays to further investigate the effects of TZDs on osteoclastogenesis. In particular, we performed the assays with rosiglitazone and pioglitazone, which are two common forms of TZDs used to treat T2DM, in parallel to obtain more convincing data. Moreover, given that RANKL-mediated osteoclast lineage commitment of BMMs has been shown to determine the effects of LPS, IFN-γ, IL-1 and TNF-α on osteoclastogenesis, we also examined whether RANKL-mediated osteoclast lineage commitment alters the effect of TZDs on osteoclastogenesis. Finally, we carried out mechanistic studies to gain insights into the molecular mechanism by which TZDs modulate osteoclastogenesis.In this work, given the controversy on the role of TZDs in osteoclastogenesis, first we independently and thoroughly carried out a series of 4 µM) of rosiglitazone and pioglitazone were prepared in DMSO monthly and stored at 4°C. Recombinant purified glutathione S-transferase-RANKL was purified as described previously Rosiglitazone (R2408), pioglitazone hydrochloride (E6910) and dimethyl sulfoxide (DMSO) (D2438) were purchased from Sigma-Aldrich . Stock solutions . The images of osteoclastogenesis assays were obtained using Olympus IX70 Fluorescence Microscope .BMMs were isolated from long bones of 4-to-6-week-old male and female C57BL/6 mice. To generate osteoclasts, 5×104 cells per/well, were seeded on bovine cortical bone slices in 24-well tissue culture plates. Cells were treated under conditions indicated in individual experiments to promote osteoclastogenesis and bone resorption for up to 10 days. Then, bone slices were harvested, and cells were removed from bone slices with 0.25 M ammonium hydroxide and mechanical agitation. Bone slices were subjected to scanning electron microscopy (SEM) using a Philips 515 SEM . Bone resorption surfaces were quantified by measuring the percentage of the pits area in 5 random areas using the ImageJ analysis software obtained from National Institutes of Health.BMMs, 5×10For the assays performed to assess the activation of IκBα/NF-κB, ERK, JNK, p38 pathways, BMMs were washed twice with ice cold PBS and then lysed with the lysis buffer from Cell Signaling Technology (#9803s), supplemented with protease inhibitor (#8340) and phosphatase inhibitor cocktail 1 (#2850) and 2 (#5726) from Sigma-Aldrich. For NFATc1, RANK and c-Fos expression assays, BMMs were washed twice with ice cold PBS and then lysed with the lysis buffer from Cell Signaling Technology (#9803), supplemented with protease inhibitor (#8340). Cytoplasmic and nuclear extracts were prepared using the NE-PER Nuclear and Cytoplasmic Extraction Reagents  from Thermo Scientific . Lysates were then subjected to Western blot analysis as described in our previous study Total RNA was isolated from BMMs using TRIzol reagent from Invitrogen . Reverse transcription was performed using the Superscript III RT-PCR system (Invitrogen) according to standard protocol. In brief, 2 µg of total RNA was reverse-transcribed to cDNA with oligo (dT) in a 20 µl volume at 50°C for 60 min. The reverse transcription reaction was then followed by enzyme inactivation and RNA H digestion. PCR amplification of the MMP9, Ctsk, TRAP, Car2, and GAPDH genes was carried out as in our previous t-test. p values less than 0.05 are considered significant.Bone resorption data are expressed as mean ± S.D. of the percentage of resorbed bone surface and osteoclastogenesis assay data are expressed as mean ± S.D. of the number of TRAP-positive cells/view area at 40× magnification. Statistical analyses were carried out using ANOVA with Bonferroni adjustment or Student’s Given the controversy on the effect of TZDs on osteoclastogenesis, in this study we seek to perform a series of experiments to fully and independently investigate the role of TZDs in osteoclast biology. It has been established that M-CSF and RANKL are two essential and sufficient factors for osteoclastogenesis Previously, we showed that whereas TNF-α or IL-1 cannot stimulate osteoclastogenesis in the presence of M-CSF, they can do so with low/permissive levels (10 ng.ml) of RANKL or RANKL pretreatment Having shown that TZDs are incapable of inducing osteoclastogenesis in the absence of RANKL, in the presence of low levels of RANKL, or with RANKL pretreatment, we further investigated their effects on osteoclastogenesis with optimal levels (100 ng/ml) of RANKL. We found that both rosiglitazone and pioglitazone dose-dependently inhibited RANKL-mediated osteoclastogenesis with complete inhibition of osteoclastogenesis at 40 µM . Bone suTo elucidate the molecular mechanism by which TZDs inhibit osteoclastogenesis, we first examine whether TZDs exert an effect on known RANK signaling pathways  involved in osteoclastogenesis. BMMs were treated with M-CSF and RANKL only, or M-CSF and RANKL plus vehicle (DMSO), rosiglitazone or pioglitazone for 5 or 10 minutes. Activation of NF-κB, JNK, ERK and p38 pathways was determined using Western bot analysis as the levels of phosphorylated form of IκB, JNK, ERK and p38, respectively . RANKL tNFATc1 and c-Fos are two important transcriptional regulators of osteoclastogenesis Moreover, osteoclastogenesis involves differential regulation of the expression of numerous genes Given that previous studies demonstrated that TZDs inhibit osteoclastogenesis by decreasing the expression of RANK in bone marrow cells and RAW264.7 cells We and others previously established that RANKL-mediated osteoclast lineage commitment dictates the effects of various factor including LPS, IFN-γ, IL-1 and TNF-α on osteoclastogenesis To further substantiate this notion, we carried out additional assays to investigate whether RANKL-mediated osteoclast lineage commitment affects the role of TZDs in osteoclastogenesis on bone slices since bone surfaces represent the physiological substratum for osteoclastogenesis and assays with bone slices are more physiologically relevant. Similar to our assays above in To delineate the molecular mechanism by which RANKL-mediated osteoclast lineage commitment modulates the action of TZDs in osteoclastogenesis, we examined the effect of TZDs on the expression of NFATc1 and c-Fos in RANKL-pretreated BMMs. Similar to our above assays in Effects of TZDs on osteoclastogenesis were initially investigated almost 15 years ago by Okazaki and coworkers who carried out their study using mouse whole bone marrow cultures and demonstrated that rosiglitazone, pioglitazone and troglitazone all inhibited osteoclastogenesis in the whole bone marrow culture system Intriguingly, in 2007 Wan and coworkers demonstrated that rosiglitazone enhances RANKL-induced osteoclastogenesis in an PPARγ-dependent manner, proposing a concept that TZDs induce bone loss in patients with T2DM in part by promoting osteoclastogenesis Several factors prompted us to carry out this study to further address the role of TZDs in osteoclastogenesis. First, the role of TZDs in osteoclastogenesis remains highly contentious and this controversy raises a concern about the notion that TZDs induce bone loss in T2DM patients in part by enhancing osteoclastogenesis in vitro osteoclastogenesis assays in this study to independently and thoroughly investigate the effect of TZDs on osteoclastogenesis using primary BMMs  and two TZDs (rosiglitazone and pioglitazone). More importantly, we also validated key osteoclastogenesis data obtained from tissue culture plates by performing bone resorption assays to assess the formation of functional osteoclasts on bone slices, which represent the physiological substratum for osteoclastogenesis. We found that both rosiglitazone and pioglitazone dose-dependently inhibit osteoclastogenesis in vitro from normal BMMs , replicating the findings from these seven previous studies In light of the controversy, we carried out a series of Obviously, the inhibitory effect of TZDs on osteoclastogenesis is counterintuitive given that clinical data indicate that TZD treatments cause bone loss and increase fracture risks in T2DM patients. So, how can our findings explain the clinical observation? To address this issue, we would like to highlight following important observations: a) TZDs inhibit osteoblast differentiation and bone formation We extended our study to investigate the molecular mechanism by which TZDs suppress osteoclastogenesis. Our data indicate that rosiglitazone and pioglitazone inhibit osteoclastogenesis not through impairing RANKL-induced activation of the NF-κB, JNK, p38 and ERK pathways in BMMs. However, both rosiglitazone and pioglitazone inhibit RANKL-induced expression of NFATc1 and c-Fos, two key transcriptional regulators of osteoclastogenesis in vivo using a mouse model in which PPARγ is deleted in endothelial cells and multiple hematopoietic lineages in vitro studies demonstrate that TZDs inhibit osteoclastogenesis in PPARγ-dependent fashion, new animal models in which PPARγ is selectively deleted in osteoclast precursors  should be developed to better address the role of PPARγ in osteoclastogenesis in vivo.Our study has also raised several crucial issues which need to be further addressed to gain a complete understanding of the role and mechanisms of TZDs in osteoclast biology. The first question is whether TZDs mediate the inhibitory effects on osteoclastogenesis through PPARγ. Although TZDs were established as agonists of PPARγ, these synthetic compounds may also bind to other intracellular molecules. So, future studies are needed to address whether TZDs inhibit osteoclastogenesis in PPARγ-dependent or -independent manner. Secondly, if TZDs inhibit osteoclastogenesis in PPARγ-dependent fashion, a question arises regarding the molecular mechanism by which TZDs suppress osteoclastogenesis. Elucidation of the molecular mechanism underlying the inhibitory role of TZDs in osteoclastogenesis may guide the future development of new and better PPARγ agonists for therapy. Thirdly, it is noted that Wan et al investigated the role of PPARγ in osteoclastogenesis in vitro is required for designing proper animal model studies to delineate the mechanisms by which TZDs induce bone loss in vivo. Furthermore, our findings in this study highlight the importance of further investigation of the role of PPARγ in osteoclastogenesis in vitro and in vivo. These future studies will not only provide a better understanding of the role and mechanisms of TZDs and PPARγ in osteoclast biology but, more importantly, also facilitate development of better PPARγ agonists with no or fewer side effects for T2DM patients.In summary, the findings from this study combined with those from seven previous studies collectively support the notion that TZDs inhibit osteoclastogenesis. Moreover, our study also reveals new insights into the molecular mechanism by which TZDs inhibit osteoclastogenesis. These findings are significant since the precise understanding of the role of TZDs on osteoclastogenesis


Targeted sequence enrichment enables better identification of genetic variation by providing increased sequencing coverage for genomic regions of interest. Here, we report the development of a new target enrichment technology that is highly differentiated from other approaches currently in use. Our method, MESA , isolates genomic DNA fragments in microfluidic droplets and performs TaqMan PCR reactions to identify droplets containing a desired target sequence. The TaqMan positive droplets are subsequently recovered via dielectrophoretic sorting, and the TaqMan amplicons are removed enzymatically prior to sequencing. We demonstrated the utility of this approach by generating an average 31.6-fold sequence enrichment across 250 kb of targeted genomic DNA from five unique genomic loci. Significantly, this enrichment enabled a more comprehensive identification of genetic polymorphisms within the targeted loci. MESA requires low amounts of input DNA, minimal prior locus sequence information and enriches the target region without PCR bias or artifacts. These features make it well suited for the study of genetic variation in a number of research and diagnostic applications. Next-generation sequencing technologies have brought about a steep decline in the per-nucleotide cost of sequencing. Nonetheless, the cost of sequencing larger eukaryotic genomes is still substantial and the quantity of sequence data generated can be complicated to process and analyze . Beyond The most common target enrichment methods rely on hybridization-based capture (either in-solution or on-array) or highly multiplexed polymerase chain reaction (PCR) . HybridiDroplet-based microfluidic methods are well suited for a variety of ultrahigh-throughput molecular biology applications –19. ThesIn this study, we developed and characterized a new microfluidic technology for sequence enrichment that does not rely on hybridization-based capture or PCR amplification to perform target enrichment. Instead, our approach physically encapsulates, without bias, diluted nucleic acids into millions of microdroplets where the target sequence is unambiguously identified with TaqMan PCR. Once identified, microfluidic sorting enables isolation and enrichment of the target nucleic acid for downstream sequencing. This novel approach to targeted sequence enrichment addresses many of the shortcomings of current methods.2 in Roswell Park Memorial Institute (RPMI) 1640 supplemented with 15% fetal bovine serum (FBS) and L-Glycine. The cells were grown to ∼800 000 cells/ml. To arrest the cells in metaphase, colcemid was added to a final concentration of 0. 1 μg/ml and the cells were cultured for another 4 h before harvest. The cells were then washed twice with pre-chilled phosphate buffered saline (PBS) and re-suspended in warm PBS. The cells were then mixed with equal volume pre-warmed 2% SeaPrep LMP agarose solution, which has a gelling point of ∼20°C and a melting point of ∼50°C. The mixture was poured into PCR tube molds and cooled on ice to gel. In this way, the cells were embedded and immobilized in the gel plugs. To isolate cellular genomic DNA, the gel plugs were treated with lysis buffer  at 37°C for 24 h (37°C was used so that the gel plugs were not melted). The gel plugs were further washed with ice-cold Tris-EDTA (TE) containing 0.1 mM phenylmethanesulfonyl fluoride (PMSF) to remove proteinase K. The resulting gel plugs contained largely intact genomic DNA and were stored at 4°C . AmpliTaq Gold DNA polymerase mix (Applied Biosystems) was used for the microdroplet TaqMan PCR reactions. Hundred nanogram of purified genomic DNA was used for each emulsified PCR reaction. Thermocycling conditions were 93°C for 2 min followed by 45 cycles of: 92°C, 15 s, 56°C, 10 s and 60°C, 50 s.Thermocycled droplets were transferred to a 1-ml syringe and reinjected into the microfluidic droplet sorter. Droplets were spaced with carrier oil with a lower 0.25% surfactant concentration. The droplet fluorescence was excited with a 473-nm laser (CNI lasers) and measured with a photomultiplier tube (Thorlabs). An FPGA card  running custom LabVIEW code interpreted the fluorescence signal in real time, sorting droplets falling within user-defined ranges of fluorescence amplitude and size . The dielectrophoretic sorting pulse output by the FPGA was amplified with a high-voltage amplifier (Trek) to 1 kV and applied to an on-chip salt water electrode .Positively sorted TaqMan emulsions were broken using perfluoro-1-octanol and the aqueous fraction was diluted in 10 mM Tris pH 8.0. The aqueous fraction containing the enriched genomic DNA was then treated with uracil-DNA glycosylase (UDG) (New England Biolabs) for 2 h at 37°C to prevent carry over TaqMan PCR amplicons in the downstream sequencing library preparation steps. Glycosylase reactions were subsequently ethanol precipitated to purify and concentrate the DNA prior to whole genome amplification (WGA). WGA was performed using a REPLI-g mini kit from Qiagen that relies on Multiple Displacement Amplification (MDA) technology. Fifty percent of the MESA  sorted genomic DNA from eight enrichment experiments was amplified with this method. Following WGA, the amplified DNA was purified using a QIAamp DNA mini kit (Qiagen).For next-generation sequencing, 1 ng of REPLI-g-amplified DNA was subsequently used for sequencing library preparation with a Nextera XT library kit (Illumina). Six replicate libraries were prepared from the pooled DNA sample and appropriately barcode indexed for partitioning during sequence data analysis. For the control reference genome, 1 ng of pre-enriched genomic DNA was used directly as input for Nextera XT library preparation. Quality and size distributions of all libraries were analyzed using a High Sensitivity DNA Assay run on a Bioanalyzer (Agilent). Sequencing was performed with a HiSeq2500 sequencer with 50-bp reads. The six individually prepared MESA sequencing libraries were run on two lanes (three libraries per lane) of the HiSeq2500. Total sequencing reads obtained for the libraries ranged from 22 296 448 to 83 174 506 (Table http://broadinstitute.github.io/picard/). For Figures Reads passing quality control were aligned to the hg19 human genome using the Burrows–Wheeler Aligner . CoveragBcftools was used for single nucleotide polymorphism (SNP) calling, disregarding duplicated reads and filtering out variants with a phred-scaled quality score and read depth lower than 10 . The SNPAlthough microfluidic droplet-based PCR reactions have been used to amplify target molecules for sequencing , the MESCLEC18A, CLEC18B and CLEC18C genes. All three TaqMan probes were labeled with fluorescein (FAM) fluorescent dye although, if desired, different colors can be used to identify which of these regions is present in a given droplet.TaqMan PCR affords single molecule sensitivity and high specificity making it ideal for accurately identifying droplets containing target nucleic acids ,34. ThisEight replicate multiplex TaqMan digital droplet PCR reactions, each containing 100 ng of purified genomic DNA, were emulsified and thermocycled. Following thermocycling, the droplets were reinjected into the microfluidic sorting device and examined for FAM fluorescence. Droplets were analyzed and sorted at a rate of 1 kHz. The average target detection rate with the TaqMan probes in the digital droplet PCR reactions across the eight experimental replicates was 1.3% (±0.6). A representative scatterplot of one of these replicates is shown in Figure Six replicate Nextera libraries generated from MESA-enriched target-DNA were sequenced. A sequencing data set from non-enriched genomic DNA was also generated to serve as a reference sample. To evaluate our method, we considered an equal number of reads per sample and analyzed the first 22 million reads of each. This produced a combined data set that contained 130 million reads, equivalent to the output of less than one lane from a standard Illumina sequencing run. The sequencing data generated from MESA-enriched DNA was of high complexity, with a minimal number of duplicate and unaligned reads, as shown in Table CLEC18A, CLEC18B and CLEC18C genes that were simultaneously identified with the single CLEC18 TaqMan assay. Unexpectedly, several areas of ‘enhanced’ coverage from the non-enriched reference genome control sample roughly coincided with the peaks observed in the MESA samples. To establish whether this coverage was significant, we calculated the mean and standard deviation of the average coverage values across the chromosome. None of the average coverage values in the region were above three standard deviations from the mean . This analysis suggested that an average fragment size of 52 400 (±5706) bp was encapsulated in the droplets.To further evaluate the efficacy of our method, we calculated the uniformity of coverage within the regions of interest. This is a useful metric employed to determine the amount of sequencing necessary to reach a desired threshold of coverage, and is especially important when analyzing SNPs. Several groups have attempted to calculate the depth of coverage needed to reproducibly call SNP variants in the human genome –41, withTo demonstrate the ability of MESA to improve SNP calling with low input DNA, we analyzed the genomic region containing the five targeted loci (∼6 Mb) and considered polymorphisms with both a minimum read depth and phred-scaled quality score of 10. In the MESA-enriched samples, we detected a total of 370 variant nucleotides, while only 18 were called in the non-enriched genomic sample Figure . Equal nOur technical advance has been to develop a robust microfluidic workflow that enables a completely new approach to targeted sequence enrichment. This method relies on a small number of TaqMan primer/probe sets that are easy to design and economical to purchase. The TaqMan reactions provide exquisitely specific detection of target sequences in our workflow, thus overcoming the specificity issues common to hybridization-based capture enrichment. Importantly, unlike other droplet-based PCR target enrichment methods, with MESA, the nucleic acids to be sequenced are not produced by PCR amplification . Rather MESA can simultaneously target and enrich multiple 50–200-kb loci. This capability is enabling for numerous applications such as targeted sequencing of a panel of tumor suppressors ,14. HoweA key feature of MESA is the scalability provided by the microfluidics. This scalability enables a large number of digital PCR droplets to be generated for a given concentration of target DNA. Correspondingly, fewer ‘off-target’ background DNA molecules are co-encapsulated in the same droplet as the target DNA, resulting in a higher overall enrichment factor achieved with the MESA workflow. In this study, droplets were processed at ∼1 kHz; however, with slight modifications, sorting devices have reached rates of 30 kHz using similarly sized droplets . MESA taa priori. MESA might also enable the enrichment of targets from other populations of mixed nucleic acids, such as cell-free DNA or exosomes from blood. We therefore anticipate that MESA, due to its unique ability to capture long molecules using minimal sequence information, will provide a general, new sequence enrichment capability that will prove useful in a variety of applications, especially studies of genetic variation.The MESA method is, in its most fundamental form, a sorter of nucleic acids and is not limited to enriching target regions within complex genomes. It could also be used to identify and sort RNA, viruses or even uncultivable microbial genomes from mixed populations . This apSupplementary Data are available at NAR Online.