Leaders in Pharmaceutical Business Intelligence (LPBI) Group

Posts Tagged ‘Polymerase chain reaction’

Deciphering Mode of Action of Functionally Important Regions

Posted in Proteins, Proteomics, tagged Crystal structure, Curve fitting, Fats, Focal adhesions, Intrinsically disordered proteins, Polymerase chain reaction, protein interactions, Solubility on March 19, 2016| Leave a Comment »

Deciphering Mode of Action of Functionally Important Regions

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Deciphering Mode of Action of Functionally Important Regions in the Intrinsically Disordered Paxillin (Residues 1-313) Using Its Interaction with FAT (Focal Adhesion Targeting Domain of Focal Adhesion Kinase)

Muniasamy Neerathilingam , Sneha G. Bairy , Sumukh Mysore    PLoS One   Feb 29, 2016    http://dx.doi.org:/10.1371/journal.pone.0150153

Intrinsically disordered proteins (IDPs) play a major role in various cellular functions ranging from transcription to cell migration. Mutations/modifications in such IDPs are shown to be associated with various diseases. Current strategies to study the mode of action and regulatory mechanisms of disordered proteins at the structural level are time consuming and challenging. Therefore, using simple and swift strategies for identifying functionally important regions in unstructured segments and understanding their underlying mechanisms is critical for many applications. Here we propose a simple strategy that employs dissection of human paxillin (residues 1–313) that comprises intrinsically disordered regions, followed by its interaction study using FAT (Focal adhesion targeting domain of focal adhesion kinase) as its binding partner to retrace structural behavior. Our findings show that the paxillin interaction with FAT exhibits a masking and unmasking effect by a putative intra-molecular regulatory region. This phenomenon suggests how cancer associated mutations in paxillin affect its interactions with Focal Adhesion Kinase (FAK). The strategy could be used to decipher the mode of regulations and identify functionally relevant constructs for other studies.

Neerathilingam M, Bairy SG, Mysore S (2016) Deciphering Mode of Action of Functionally Important Regions in the Intrinsically Disordered Paxillin (Residues 1-313) Using Its Interaction with FAT (Focal Adhesion Targeting Domain of Focal Adhesion Kinase). PLoS ONE 11(2): e0150153. doi:10.1371/journal.pone.0150153

Genomic data suggests that a large proportion of eukaryotic proteins appear to adopt disordered structures in physiological conditions [1, 2]. Mutations/modifications in such IDPs are shown to be associated with various diseases (like cancer) [3]; therefore, understanding their structural behavior is critical for various applications like drug-targeting, mapping protein interactions, deciphering mode of action and finding functional relevance. However, deciphering mode of action in IDPs has been challenging given that unstructured segments render poor chemical shift dispersions and electron density in major techniques like NMR and X-ray, respectively [4]. For example, it took almost 10 years to decipher the mode of action of Sic1, a disordered protein involved in inhibition of a cyclin-dependent kinase [5]. One way to map and study the functional regions is to make truncated constructs by dissecting the whole construct rationally. A limited number of dissection constructs are usually generated; this is due to the time-consuming and challenging process of generating soluble and functionally relevant constructs when studies are performed in-vivo and constructs are prepared and tested sequentially. Here we present a simple high throughput (HTP) screening strategy (Fig 1a), which focuses on finding functionally relevant regions in IDPs based upon its interaction with a binding partner. Close to thirty dissection constructs of the IDP were generated and studied in parallel to understand the importance and functionality of the various regions of the protein. We perform cell-free expression followed by solubility check and GST pull-down interaction study in HTP format. Though both cell-free expression and GST pull-down assay have been individually performed in HTP format [6, 7], we did not find previous studies that combine the two methods in HTP format. Although the nature of interaction of IDPs with respective binding partners may vary, our strategy may be used to derive crucial insights into “structural behavior” of the unstructured segments in modulating the interaction. The strategy can also be used to identify functionally important regions in the IDP that would be suitable for further structural studies.

Fig 1. Dissection of paxillin constructs (residues 1–313) followed by expression and interaction studies.

(a) Timeline for overall-strategy. (b) Illustration of solubility and activity level of linear dissected human paxillin (residues 1–313). (c) Phosphor screen image of filter assay for optimization of temperature for paxillin constructs (left). Tabular representation of paxillin constructs, negative and positive controls corresponding to each well in filter assay [1].(d) Phosphor screen image of 10% SDS PAGE of ³⁵S labeled cell-free expressed samples after GST pull-down assay of the paxillin constructs A1–E1; The right panel shows fraction of interaction of each construct with respect to B2 (since B2 showed maximum level of interaction) (e) Illustration of solubility and activity of dissected C3 constructs. All experiments were performed in triplicates and averaged. To rule out non-specific interactions that might occur with GST tagged FAT, GFP that was expressed in cell-free system and a reaction without DNA template were used as negative controls.    http://dx.doi.org:/10.1371/journal.pone.0150153.g001

Disorder/Intrinsic disorder seems to be a common feature of hub proteins in eukaryotes [2], thus highlighting the need for studying the mode of action of unstructured segments in such proteins. Here we used paxillin (residues 1–313), an intrinsically disordered construct, for demonstrating this approach. Paxillin (residues 1–313) consists of multiple protein interaction sites that are connected by flexible disordered sequences [8]. The disordered regions in paxillin have been detrimental in efforts to study the complete structure of the protein due to the demerits mentioned previously. This explains the lack of structural details of regulation of paxillin binding. Residues 1–313 of paxillin consist of five leucine-rich sequences LD1-LD5 (with consensus sequence: LDXLLXXL), termed LD motifs, which are highly conserved between species and other family members such as Hic-5, leupaxin and PAXB [8]. Paxillin interacts with multiple proteins involved in cell migration, actin rearrangements and cell proliferation [9]. Mutations in paxillin are shown to be associated with lung cancer [3, 10]; and the differential expression of paxillin is associated with various forms of cancer and other diseases such as Alzheimer’s and inflammation [11–13]. This implies the importance of studying the structural and functional characteristics of paxillin. Most paxillin studies focus on interactions of LD motifs with proteins such as focal adhesion kinase (FAK), vinculin and v-crk, providing clues towards their importance in deciphering the functionality of paxillin [8, 14, 15]. Though regions of paxillin that bind to various partners were deciphered through previous studies, the basis of effect of mutations in paxillin on binding its partners was not explained. Mutations in paxillin, some that were observed to be associated with cancer were positioned in the intrinsically disordered regions between the LD motifs and not on the motifs themselves [3, 10]. For example, P30S, G105A and A127T mutations lie between LD1 and LD2 motif; P233L and T255I mutations lie between LD3 and LD4 motifs. This shows that the LD motifs alone do not govern the functionality, but unstructured regions linking the LD motifs could play a major role. In normal conditions, FAT (Focal adhesion targeting domain of FAK) binds hydrophobically through its HP1 (Hydrophobic patch 1) and HP2 (Hydrophobic patch 2) sites to paxillin LD motifs—LD2 and LD4 [16, 17], which lead to activation of binding sites for other proteins on paxillin. LD2 preferentially binds to the HP2 site, whereas LD4 preferentially binds to the HP1 site [18]. In a state of cancer caused by mutations in paxillin, the LD interactions could be hindered, as mutations in the unstructured segments result in abnormal binding of FAK to either of the LD motifs [9]. Here we wanted to locate the region involved in the structural modulation of paxillin-FAT interaction by adopting a simple approach (Fig 1) that involves dissected proteins generated using cell-free protein expression coupled with protein-protein interaction study. We map the disordered proteins’ structural importance to understand the function and modulation of paxillin-FAT interaction in days rather than months (Fig 1a).

Dissection and identification of fragments of paxillin (residues 1–313) with functional relevance

We dissected paxillin (residues 1–313) (Fig 1b) into nested sets using PCR such that each of the constructs had either or both LD2 and LD4 motifs (S1 Fig and S1 Table). Further, these constructs were expressed in soluble form using small-scale cell-free expression system in a 96 well format (Fig 1c). However, all constructs except A6, B6, C4 and C5 expressed detectable amounts of protein (S2a Fig and S2 Table). The failure in expression of the above constructs could be due to the instability of the smaller peptide fragments that might be susceptible to proteolytic cleavage [19]. Soluble protein from small-scale expression of the dissected constructs namely A1, A2, A3, A4, A5, B1, B2, B3, B4, B5, C1, C2, C3, D1, D2, and E1 were pulled down and analysed (Fig 1d). Although constructs A1–A5, B1–B5, C1, C2, D1 and E1 interacted successfully, C3 (containing LD2) and D2 (containing LD4) failed to interact (Fig 2c) despite containing LD motifs. However, based on previous reports [8, 16, 17], we expected all constructs containing either LD2 and/or LD4 to interact with the FAT domain. Therefore, this led us to suspect that intra-molecular auto-inhibition in unstructured segments modulated binding of FAT to LD motifs in paxillin.

Fig 2. Regulatory and masking regions around paxillin’s LD2 and LD4 and their circular dichroism spectra.

(a) CD spectra of paxillin LD peptides (LD1-LD5) and constructs: B2, C3, C35 and D2. CD spectra of LD2, LD4, C35 and D2 constructs showed negative bands at 222nm and 206nm and a positive band at 192nm that confirms the presence of alpha helical content thus may behave as folded effector binding sites. However, LD1, LD3, LD5, B2 and C3 do not show the characteristic peaks of secondary structures, thus may behave as unfolded effector binding sites. (b) LD2 regulatory region (54–130) and masking region (167–224) evidenced by constructs B3, B4 and B5. (c) LD4 regulatory region (216–257) and masking region (280–313) evidenced by constructs D1, D2 and E1.
http://dx.doi.org:/10.1371/journal.pone.0150153.g002

Identification of regulatory regions and their mechanisms

To investigate the non-interaction of C3, a series of C3 deleted constructs (C31 –C310) (Fig 1e,S1 Table) were generated to determine the internal region that influenced the non-functioning of C3. C36 linear template could not be amplified for expression. As solubility of C3 could play a critical role in determining interaction, the homogeneity of the sample was confirmed by capillary electrophoresis under non-reducing conditions [20] (See S3 Fig). The linear templates—C31, C32, C33, C34 and C35 were successfully expressed in soluble form, The other C3 deleted constructs did not express due to issues related to small size as described earlier. Surprisingly, none of the C3 deleted constructs interacted with FAT despite the presence of the LD2 motif, although constructs such as B3, B4 and B5 that contain regions overlapping with C3 showed interaction (S2b and S2c Fig, Fig 1d and 1e). Here B3 that included the whole of C3 and unstructured segment 54–130 showed interaction (Fig 1b). Constructs B4 and B5 also containing residues 54–130 showed interaction despite differing from B3 by lacking regions 167–224 and 155–224, respectively. Interestingly, the non-interacting constructs C3 and C35 do not contain 54–130 residues, but include the regions 167–224 and 167–189, respectively (Fig 1b). Here constructs containing region 167–189 but lacking 54–130 did not interact with FAT despite LD motif alone showing interaction (switch off) (Fig 3a). Whereas, if 54–130 was included, interaction was reinstated (switch on) (Fig 3a). This clearly shows that interaction of LD2 in construct C35 is masked by residues 167–189 (masking region) (Fig 2b). The constructs B3 and B4 binding to FAT despite the presence of the masking region led us to conclude that the region 54–130 (regulatory region) acts to remove the masking effect (Fig 2b).

Fig 3. Binding studies of paxillin constructs using Bio-layer Interferometry on OctetRed96.

(a) Switch off in C3 and D2 on LD2 and LD4 respectively; Hypothesis of partial switch on when regulatory region of LD2 is absent, as evidenced in C2. (b) Concentration calibration curves depicting binding of constructs B2, C35, ‘54–189’, ‘79–189’, ‘105–189’ with GST-FAT. The data is representative of a single experiment. Each experiment was performed at-least thrice. (c) Illustrations of C35, C35_1, C35_2 and C35_3.  http://dx.doi.org:/10.1371/journal.pone.0150153.g003

Similar to LD2, LD4 in construct D2 containing 216–257 (masking region) requires additional residues of paxillin 280–313 (regulatory region) for FAT binding (Fig 2c), which was demonstrated by showing the interaction with constructs D1 (spanning region 216–313) (Fig 1b) and E1 (spanning region 258–313). To visualize the non-binding of FAT to C35, in-silico methods were employed to model the C35 construct and docked with the crystal structure of FAT (1K05, residues 916–1050 [21]) (Fig 4). The docking results showed a clear masking effect in the C35 construct by the 167–189 (masking region) residues. The constructs B2, C3 and C35 were also structurally characterized using CD analysis (Large scale cell-free expression was performed for this purpose, see S3 Fig). The percentage of alpha helical content was found to be much higher in C35 (95.32%) as compared to B2 (12.43%) (Fig 2a, S3 Table). Therefore, the dissection(s) of B2 to C35 allowed the identification of structured regions (C35) as compared to the disordered B2. Further, it showed that the LD2 peptide and C35 have significant alpha-helical structures that do not translate into functional similarity as evidenced by the inability of C3, C35 and D2 to bind to FAT. Moreover, LD2 peptide binds to FAT while C35 does not (Fig 1e and S2b and S2c Fig). A similar observation was made when comparing the ability of LD4 peptide and the inability of D2 to bind to FAT despite both having detectable α-helical content (Fig 1b). Thus, these results confirm the existence of masking and regulatory regions (Fig 2b and 2c) that determine switch on and off and in turn, intra-molecular auto-inhibition. C2 showed activity despite missing regulatory regions for both LD2 and LD4 (similar activity observed in C1). This could be because the unfolded nature of LD3 effector binding site that is located between LD2 and LD4 is flexible to mask only a single LD motif but not both (Partial switch on, Fig 3a).

Fig 4. In-silico analysis of non-binding of C35.

(a) LD2 crystal structure from PDB id: 1K05 (left) being compared with the LD2 structure in the side view and top view of C35 structure showing the masking of the hydrophobic binding region predicted through HMM based SAM-T08 software. The LD2 binding region and the masking regions are depicted by the bracketed region. (b) Docking control showing FAT (co-ordinates from PDB id: 1K05) and LD2 (co-ordinates from PDB id: 2L6F, NMR model # 1) interaction using Hex 6.3 software. (c) Docking of C35 with FAT showing non-interaction due to masking effect. The sidechains of the active residues are shown as red sticks. The hydrophobic patch—HP2 in FAT molecule, which preferentially binds to LD2 is shown as a space filling model in orange (part of helix 1 of FAT) and grey (part of helix 4 of FAT) colors.   http://dx.doi.org:/10.1371/journal.pone.0150153.g004

To predict the influence of this structural modulation, the state of LD motifs structurally before and after binding to FAT had to be understood. CD spectra of LD1, LD3 and LD5 peptides showed characteristics of random coil (Fig 2a, S3 Table) thus validating that the LD1, LD3 and LD5 motifs could exist as unfolded effector binding sites (not available for interaction) in our study and could fold upon undergoing allosteric changes after binding to their respective targets.

Validation of protein-protein interaction study using bio-layer interferometry studies

Bio-layer interferometry studies were performed to further validate the interaction studies and also to get insights into the binding affinities. Here apart from constructs B2 and C35, three other constructs that include different lengths of the regulatory region along with the C35 region were used for the studies, namely—Construct C35_1(54–189); Construct C35_2 (79–189) and Construct C35_3 (105–189) (See Fig 3b and S4 Fig). As seen in Fig 3c and Table 1, B2 shows maximum binding with K_D value in the nano-molar range and the curves fit into a 1:1 binding model. C35 shows negligible binding and the rest of the constructs show binding lower than B2 with K_D values in micro-molar range and the curves fit into a 2:1 binding model (See S5 Fig).

According to previous reports, FAK has to bind to both LD2 and LD4, failing which phosphorylation during signalling is reduced [8], which is observed in case of cancer [3], thus resulting in abnormal functioning of paxillin. We investigated this by analysing B2, which showed higher interaction than B1, despite missing the regulatory region of LD4 (Fig 1b). Similarly, C2 showed activity despite missing regulatory regions for both LD2 and LD4 and the presence of masking regions (similar activity observed in C1). This suggests that the masking region that is located between LD2 and LD4 is flexible to mask only a single LD motif but not both (Fig 3a). Interestingly, paxillin mutations associated with lung cancer were observed in the unstructured segments, particularly the regulatory region of LD2 and masking region of LD4 [3]. We hypothesize that these mutations prevent proper functioning of the regulatory regions, thus resulting in masking of either of the LD motifs causing abnormal functioning of paxillin. Evidence that these regions regulate FAT-paxillin binding was further provided in our study in the form of the bio-layer interferometry results; where C35 did not show any binding, but the constructs that included different lengths of the regulatory region along with the C35 region showed binding with K_D values in the micro-molar range. This suggests that the LD2 region in these constructs is not masked, since it is seen in previous studies that the K_D value for FAT binding to a single LD motif of paxillin is in micro-molar range. It also suggests that the region between residues 105–131 is sufficient for preventing the masking of LD2 region, thus allowing interaction with FAT (See illustrations in Fig 3b). Except B2 (that had a 1:1 binding stoichiometry and higher binding affinity), all other constructs (C35_1, C35_2, C35_3) showed a 2:1 binding stoichiometry. This suggests that both LD motifs of B2 engage both the FAT HP sites thus resulting in higher affinity; whereas in the other 3 constructs (C35_1, C35_2, C35_3), each FAT HP site (HP1 and HP2) interacts with individual molecules thus giving a 2:1 stoichiometry. This is in agreement with previous studies where both the LD motifs were found to interact with both HP1 and HP2 hydrophobic patches of FAT [16]. The higher affinity of B2 to FAT could be due to presence of both LD2 and LD4; the proposed intra-molecular regulatory regions could also play a role in the increased affinity. Therefore, we understand that the abnormal modulation in cancer involves redirection of FAK to a single LD motif; and targeting drugs for re-establishing the function at regulatory regions could be critical.

Unlike many existing techniques like array based yeast two hybrid assay, phage display method and tandem affinity purification; the strategy used here (combination of cell-free expression, filter based solubility assay and interaction study in HTP format) facilitated quick identification of the role of unstructured regions involved in paxillin-FAT interaction in HTP format. Particularly, in paxillin-FAK interactions, which determine focal adhesion and cellular signalling, we understood the structural masking and unmasking behaviour of unstructured segments in paxillin to determine FAK interaction. The structure of paxillin is not yet elucidated due to difficulties with respect to its disordered nature. In this study, the templates that we generated using the high throughput dissection strategy allowed us to analyze various regions of paxillin, with respect to structure, solubility and function. To our knowledge, this study is the first report of switch on and off mechanisms working together in controlling allosteric modulation/auto-inhibition in a human hub protein. As many eukaryotic proteins are disordered, our study opens avenues for analyzing novel modulations at allosteric sites using appropriate interaction studies, which could lead to identification of new drug target sites. In this regard, we hope the above strategy will be instrumental in understanding mechanisms of other disordered proteins as well, in days rather than months. This strategy could also be used as an initial screening method for techniques like SAXS, smFRET and others.

Share this:

• Click to share on Twitter (Opens in new window)
• Click to share on LinkedIn (Opens in new window)
• Click to share on Facebook (Opens in new window)
• Click to print (Opens in new window)
• Click to email a link to a friend (Opens in new window)

Like this:

Like Loading...

Read Full Post »

Genomic Pathogen Typing

Posted in Clinical & Translational, Clinical Diagnostics, Computational Biology/Systems and Bioinformatics, CRISPR/Cas9 & Gene Editing, Curation, Genetics & Pharmaceutical, Genome Biology, Infectious Disease & New Antibiotic Targets, Microbiologial genetics, Microbiology, tagged Antibiotic resistance, Bacterial pathogens, Dwell time, Gene amplification, Methicillin-resistant Staphylococcus aureus, Mycobacterium tuberculosis, Pathogenesis, Polymerase chain reaction on November 16, 2015| Leave a Comment »

Genomic Pathogen Typing

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Genomic Pathogen Typing Using Solid-State Nanopores

Allison H. Squires, Evrim Atas, Amit Meller

PLOS  Nov 12, 2015   DOI: http://dx.doi.org:/1371/journal.pone.0142944

Citation: Squires AH, Atas E, Meller A (2015) Genomic Pathogen Typing Using Solid-State Nanopores. PLoS ONE 10(11): e0142944.   http://dx.doi.org:/10.1371/journal.pone.0142944

Editor: Niyaz Ahmed, University of Hyderabad, INDIA

In clinical settings, rapid and accurate characterization of pathogens is essential for effective treatment of patients; however, subtle genetic changes in pathogens which elude traditional phenotypic typing may confer dangerous pathogenic properties such as toxicity, antibiotic resistance, or virulence. Existing options for molecular typing techniques characterize the critical genomic changes that distinguish harmful and benign strains, yet the well-established approaches, in particular those that rely on electrophoretic separation of nucleic acid fragments on a gel, have room for only incremental future improvements in speed, cost, and complexity. Solid-state nanopores are an emerging class of single-molecule sensors that can electrophoretically characterize charged biopolymers, and which offer significant advantages in terms of sample and reagent requirements, readout speed, parallelization, and automation. We present here the first application of nanopores for single-molecule molecular typing using length based “fingerprints” of critical sites in bacterial genomes. This technique is highly adaptable for detection of different types of genetic variation; as we illustrate using prototypical examples including Mycobacterium tuberculosis and methicillin-resistant Streptococcus aureus, the solid-state nanopore diagnostic platform may be used to detect large insertions or deletions, small insertions or deletions, and even single-nucleotide variations in bacterial DNA. We further show that Bayesian classification of test samples can provide highly confident pathogen typing results based on only a few tens of independent single-molecule events, making this method extremely sensitive and statistically robust.

 

Subtle genetic changes in bacteria can produce large variations in factors affecting pathogenicity, such as toxicity, antibiotic resistance, and virulence. These genetic variations are not only used to trace the epidemic and phylogenetic relationships among strains of bacteria, but are also critically important in clinical settings for proper patient diagnosis and treatment. Most existing approaches require sample incubation and growth over the course of multiple days prior to testing, and nearly all require expert handling of samples and interpretation of results. Traditional phenotypic typing techniques such as serotypes, biotypes, phage-types, and antibiograms lack the necessary sensitivity to distinguish between closely related pathogen strains, and therefore fail to adequately capture these critical variations for clinical applications. Gel-based techniques such as restriction fragment length polymorphism (RFLP) or cleaved amplified polymorphic sequences (CAPS) require a large amount of time and results are not easily compared or transferred among labs. Next-generation sequencing is an increasingly popular method of fully characterizing bacterial strains [1] and may be used for typing strains according to the sequences of a panel of housekeeping genes, as in multi-locus sequence typing (MLST) [2], but this approach is more commonly used to trace post hoc epidemic and phylogenetic relationships among clinical isolates. Furthermore, the complexity and quantity of sequencing data far exceeds the minimum information required to efficiently and accurately diagnose a patient. For example, bioinformatics studies suggest that a panel of just 30–50 single nucleotide variations (SNVs) could be used to uniquely identify thousands of strains of Mycobacterium tuberculosis [3, 4]. Yet SNVs are not the only source of variation among pathogens; polymorphisms from SNVs and short indels up to genetic changes as large as whole plasmids or sets of genes may be responsible for critical changes to pathogenicity. Thus there exists a clear clinical need for a novel approach to molecular typing that can quickly and simply screen patient samples for a panel of widely varying known genetic polymorphisms of dangerous pathogens.

Solid-state nanopores may be used to discriminate the lengths of unlabeled individual biopolymers such as DNA molecules across a wide range of lengths [5, 6]. Biopolymers are electrophoretically attracted and threaded through a voltage-biased nanoscale pore drilled in an ultrathin freestanding SiN_x membrane [7, 8]. When a DNA molecule is threaded through a nanopore, it partially blocks the flow of ions moving through the pore, allowing real-time detection of the analyte by monitoring changes in the ion current. Nanopore sensing is biochemically simple, as it does not require labeling of the analyte with radioactive or fluorescent probes, yet it can be used to detect minute quantities of nucleic acid molecules, surpassing the sensitivity of bulk methods [8]. Moreover, nanopore sensing involves relatively simple instrumentation (primarily a current amplifier) and may be used to analyze thousands of molecules in just a few minutes, making this technique an ideal candidate for applications such as nucleic acid based diagnostics.

Here we describe and practice a novel detection scheme (Fig 1) for molecular typing of pathogens using solid-state nanopores, and demonstrate its ability to discriminate a wide range of critical genetic polymorphisms in closely related organisms with starkly different pathogenicities. In the first sensing mode of our approach (Mode I), large insertions or deletions are detected by directly classifying the length of DNA in the nanopore. In the second sensing mode (Mode II), small indels down to SNVs may be detected by sequence-specific digestion at the site of the polymorphism to produce either one or two DNA fragments, which are then detected in the nanopore. We first characterize the practical range of our nanopore system for detecting variation in DNA length, and show that fragment length differences are more readily apparent for shorter DNA lengths and for asymmetric cut sites. We then demonstrate that statistical analysis tools such as Bayesian classifiers, commonly used for automated classification, are highly effective for rapid and statistically robust discrimination among different lengths and combinations of DNA fragments translocating through a nanopore, even in cases where significant portions of these distributions overlap. We apply these techniques to demonstrate polymorphism discrimination down to the single nucleotide level in prototypical strains of Mycobacterium tuberculosis (virulent vs. avirulent) and Streptococcus aureus(methicillin-resistant vs. multi-drug resistant). This highly versatile combination of rapid length and digest discrimination, spanning several orders of magnitude of possible genomic variation size, in a single, parallelizable device, could be extended to probe a large panel of critical sites within a genome for point-of-care determination of critical pathogenic properties and sequence typing.

http://journals.plos.org/plosone/article/figure/image?size=large&id=info:doi/10.1371/journal.pone.0142944.g001

Fig 1. Two Principal Modes for Nanopore Discrimination of Pathogen Genomic Variation.

Mode I: Direct length detection according to analyte translocation dwell time and depth enables discrimination of longer vs. shorter fragments; i.e: whether or not an insertion or deletion is present (left). Mode II: Prior to translocation, samples are exposed to a restriction enzyme that cuts at the site of a SNV or short indel or mutation. Detection of cleaved vs. uncleaved DNA fragments in the nanopore reveals whether or not the critical genomic variation is present.

http://dx.doi.org:/10.1371/journal.pone.0142944.g001

Detection of DNA Sequence Polymorphisms in Solid-State Nanopores  

The simplest form of nanopore translocation analysis involves the measurement of the depth of each current blockade (ΔI_B) and the dwell time of each molecule within the pore (t_D). Both parameters have been shown to grow nonlinearly with DNA length, forming the basis for fragment length separation in the nanopore system. The statistical distributions of these independently measured quantities may be used to distinguish between analytes of different lengths, such as DNAs [5, 6, 9], or proteins having identical molecular weight but slightly different charge or 3D structure [10–13]. Variation in the translocation dwell-time (t_D) in solid-state nanopores measured for different DNA lengths (l), are empirically described by a power law: t_D ∼ l^α where α = 1.38±0.02, which has been reproduced by multiple experimental approaches [5, 9, 14]. Using a log-scale distribution of translocation times to estimate the distribution of t_D, note that the difference in log(t_D) for two sequences (lengths l₀ and l₀ + Δl) is more apparent for shorter length l₀ as compared with the insertions and deletions Δl (i.e. when Δl/l₀ ∼ 1) according to Eq 1:(1)

If the presence of two fragment lengths must be identified from within a single sample, it is desirable that their distributions of ΔI_B or t_D should be as well-separated as possible. Furthermore, if the presence of a cut sample must be distinguished from an uncut sample, then by Eq 1 the peak produced by the shorter part of a cut sample will appear farther away from the uncut peak than the longer part of a cut sample. To statistically distinguish the samples, it is desirable for the peak of the shorter part to be as dissimilar as possible from the uncut peak. Therefore, asymmetrically cut DNA pieces from a restriction digest are more readily distinguished from the original uncut length than those produced by symmetrically positioned restriction sites, provided that the shorter piece is of sufficient length to be detected by the nanopore. In cases where separation between two similar length biopolymers (Δl/l₀ ∼ 1) is required, the measured histograms of either ΔI_B or t_D may overlap significantly, making discrimination between these molecules difficult. Combinations of multiple fragment lengths within a sample pose additional challenges, as their more complicated distributions may overlap or otherwise preclude simple contour cluster separation.

In the context of sequence typing, identification of fragments by sizing will indicate the presence of specific insertions and deletions that may enhance or reduce pathogenicity or otherwise uniquely identify a pathogenic strain. Upper bounds on Δl are set by: 1) sample preparation parameters and limitations; for example, robust and fast PCR amplification is most easily achieved for fragment lengths of ~10²–10³ bp [15] and 2) nanopore stability considerations; for example, nanopores are more frequently clogged by very long DNA (>20 kbp). Lower bounds on l₀ are set by nanopore sensitivity; while several groups have demonstrated detection of small DNA fragments (<50 bp) [16] we find that a minimum l₀ on the order of ~100 bp is more reliable since it is readily detectable in small nanopores with no additional modifications [5], producing an extremely small fraction of missed events due to the finite system bandwidth. Thus a reasonable design range for sequence typing fragments is ~100 bp minimum length forl₀, ranging up to a few thousand base pairs maximum length for l₀ + Δl. Many types of common genetic variations used for strain typing fall within this size range. For example, one complete IS6110 (insertion-like sequence element) insertion in M. tuberculosis is 1358 bp [17]. At the other end of this range, multi-drug resistant strains of methicillin-resistant S. aureus (MRSA) have many insertions and deletions in the range 47 bp—643 bp that affect their pathogenicity [18]. To detect the smallest indels, which fall below the minimum detectable Δl, we turn to the exquisite sequence specificity of digestion by restriction enzymes, which can identify sequence polymorphisms down to a single nucleotide variation.

Using these design principles, we present here two alternative modes of detection that illustrate the wide range of genomic variations that may be detected using a single sensor. For large insertions or deletions (Fig 1: Mode I, left panel), a nanopore may be used to discriminate the raw change in DNA length caused by the presence or absence of this sequence according to the duration of translocation events. For short indels, mutations, or single nucleotide variations (SNVs) (Fig 1: Mode II, right panel), which are more difficult to identify solely by length as discussed above, we utilize a restriction enzyme. The sample is only cut in the presence (or absence) of the critical sequence, and subsequent detection in a nanopore reveals either one or two fragments in the nanopore according to the observed durations and blockage levels of translocation events.

Event Diagram Discrimination of Sample Length and Composition

We first experimentally illustrate the practical length resolution of the nanopore platform for identifying sample length and composition. We analyzed samples containing mixtures of DNA fragments composed of one or two well-defined lengths. The resulting event diagrams create unique fingerprints that can be used to distinguish different lengths of DNA (Mode I) or whether or not a fragment of DNA has been cut (Mode II). Fig 2A–2E show event diagrams for 100 bp, 200 bp, 900 bp, 1000 bp, and 100+900 bp DNA in a single nanopore (diameter 4.8 nm, effective height 7 nm) at +300 mV bias (for additional examples, see Figs B-E in S1 File). Here, each translocation event is represented by its corresponding ion current event amplitude (ΔI_B) and dwell time (t_D). From comparison of Fig 2A and 2D, it is evident that insertions and deletions Δl several times larger than the base length (here: Δl:l₀ = 9:1) are indeed easily distinguishable (Fig C in S1 File). Comparison of Fig 2A and 2B illustrates that Δl = 100 bp results in reasonably distinct event diagrams for l₀ = 100 bp, which may be distinguished to >95% confidence with just a few events each, taking both dwell time and current amplitude into consideration (Fig D in S1 File). However, at l₀ = 900 bp a minimum of several hundred events are required to confidently (>95%) differentiate l₀ (Fig 2C) from l₀ + Δl (1000 bp, Fig 2D), since their event diagrams overlap significantly (Fig E in S1 File). Returning to Eq 1, for Δl = 100 bp, we expect Δlog(t_D) = 0.415 for l₀ = 100 bp, and Δlog(t_D) = 0.063 for l₀ = 900 bp. For the data shown in Fig 2F, Δlog(t_D) = 0.1 for l₀ = 100 bp, and Δlog(t_D) = 0.03 for l₀ = 900 bp. The inability to easily and quickly discriminate the 900 bp DNA from the 1000 bp DNA demonstrates the practical limits set on Mode I sample identification according to the size of the insertion or deletion that must be detected.

http://journals.plos.org/plosone/article/figure/image?size=large&id=info:doi/10.1371/journal.pone.0142944.g002

Fig 2. Translocation Event Diagrams Uniquely Identify DNA Fragment Lengths in a Single Nanopore.

(a) 100 bp at 1 nM. (b) 200 bp at 1 nM. (c) 900 bp at 1 nM. (d) 1000 bp at 1 nM. (e) 1:1 combination of 100 bp and 900 bp, total concentration 2 nM. (f) Semilog(x) distributions of translocation dwell times for all samples (a)-(e). Translocations for all samples were collected in a single nanopore (4.8 nm diameter, effective thickness ~7 nm) with a +300 mV bias relative to trans (open pore current: 13 nA). To facilitate visualization of population density, a random white noise offset below the acquisition rate of this data (-2 μs < Δt < +2 μs, acquisition rate 250 kHz) has been added to each t_D.    http://dx.doi.org:/10.1371/journal.pone.0142944.g002

Fig 2E illustrates how Mode II may overcome these limitations by digesting DNA into fragments: here, a highly asymmetric ratio of lengths in a mixed sample (100+900 bp) clearly facilitates sample identification as compared to the full length 1000 bp DNA (Fig 2D). However, Mode II also presents a more challenging case for quantitative discrimination between an uncut and a cut sample. Whereas single-length samples can be identified using either their t_D or I_B distribution (as shown in Fig 2F), the longer fragment in a cut sample may share significant overlap with the uncut sample. This is particularly true in the case of a highly asymmetric cut site.

….

http://journals.plos.org/plosone/article/figure/image?size=inline&id=info:doi/10.1371/journal.pone.0142944.g003

Fig 3. Gaussian Mixture Models for Mode II Classification of 1000 bp vs. 900+100 bp DNA Fragments.

(a) 2-D GMM for 1000 bp DNA fragment translocations. (b) 2-D GMM for 900+100 bp DNA fragment translocations. (c) Bayesian posterior estimates p(A|Θ) of correctly identifying a data set Θ as Case A, calculated for each increment of N points in Θ, repeated 1000 times (first 50 shown in gray) and averaged (blue), each using M = 1500 points in the model data set. (d) Bayesian posterior estimates p(B|Θ) of correctly identifying a data set Θ as Case B, calculated for each increment of N points in Θ, repeated 1000 times (first 50 shown in gray) and averaged (red), all using M = 1500 points in the model data set. (e) Bayesian posterior estimates p(A|Θ) for test data sets ofN points given a model based on data set size M. Each point represents the average of 1000 separate bootstrap simulations. (f) Bayesian posterior estimates p(A|Θ) for test data sets of N points given a model based on data set size M. Each point represents the average of 1000 separate bootstrap simulations. Insets: range of N for which p(A|Θ) reaches 0.95. See Methods and S1 File for complete numerical simulation details.    http://dx.doi.org:/10.1371/journal.pone.0142944.g003

……

http://journals.plos.org/plosone/article/figure/image?size=inline&id=info:doi/10.1371/journal.pone.0142944.g004

Fig 4. Gaussian Mixture Models of DNA Fragments for Actual Mode II Pathogen Typing at the SNV Level.

(a) Diagram of the main steps in sample preparation, detection, and classification: PCR fragments from isolated pathogens are subjected to a restriction digest, which recognizes and cuts only one genomic variant. Nanopore translocations are used to classify the pathogen according to the combination of fragment lengths detected. (b) ThemazG gene of the avirulent M. tuberculosis strain H37Ra is not cut by NaeI (942 bp), while the same gene in the closely related virulent strain H37Rv, which differs by only a single A-to-C mutation, is cut by NaeI (621bp + 321 bp). (c) Gaussian mixture model (one component) fit to translocations of mazG fragments from H37Ra. (d) Gaussian mixture model (two components) fit to translocations of mazG fragments from H37Rv. (e) Posterior probabilities for correctly identifying the H37Ra and H37Rv strains as a function of number of translocation events collected from an unknown sample, simulated using bootstrap sampling from nanopore translocation data. (f) The parC gene of the multi-drug-resistant MRSA strain FPR3757 is not cut by BseRI (886 bp) due to a single C-to-A mutation, while the closely related and less resistant strain HOU-MR is cut by BseRI (640bp + 245 bp). (g) Gaussian mixture model (one component) fit to translocations of parC fragments from FPR3757. (h) Gaussian mixture model (two components) fit to translocations of parC fragments from HOU-MR. (i) Posterior probabilities for correctly identifying the FPR3757 and HOU-MR strains as a function of number of translocation events collected from an unknown sample, simulated using bootstrap sampling from nanopore translocation data.    http://dx.doi.org:/10.1371/journal.pone.0142944.g004

Conclusion

Solid-state nanopore based biosensing is a rapidly growing field due to its practical and conceptual simplicity, portability and versatility. To date, few reports have demonstrated the utility of the method towards clinical diagnostic applications. Yet as we have shown here, nanopores are well-suited to make statistically robust diagnostic classifications among different DNA lengths with real single-molecule data, even in cases where the distributions significantly overlap. Utilizing a Bayesian statistical model, we have demonstrated that nanopore sensing can be used to discriminate among pathogens based on well-known genomic variations. Both large indels (Mode I) or short indels and single nucleotide variations (Mode II) can be targeted using proper sequence-specific digestion with off-the-shelf restriction enzymes. Furthermore, the Bayesian classifiers indicate the statistical confidence of each classification as a function of the number of nanopore events obtained in each measurement. Even at this preliminary stage of development we find that only a few tens of events (obtained in just a few minutes using a single pore) are sufficient to produce a statistically reliable result with well-defined and small error margins.

Our method is general and can be adapted to address many different “multiple-choice” clinical questions using a nanopore biosensor or other single molecule approaches. Future extensions of this work may seek to design and implement large panels of critical sites that represent the minimum sets necessary to characterize genomic variation for various applications in healthcare and research, and to develop additional sensing modalities. Although the primary design challenge currently remains linked to the location and availability of restriction digestion sites, we expect that the ongoing development of designer restriction enzymes, for example systems based on modular zinc fingers [27], TALENs [28], or CRISPR-like proteins will provide additional design flexibility for this technique.

The nanopore fingerprinting approach presented here addresses clear needs in clinical molecular diagnostics for a rapid and simple sensor that can identify a wide range of genomic variation in pathogens to inform treatment options. We have shown here discrimination of both large and small scale genomic variations between pathogen strains, down to single SNVs. The large, flexible sample design space for lengths, cut sites, and enzyme selection at each critical locus ensures that the technique is highly customizable for different genomic variation panels that could profile pathogenicity, antibiotic resistance, or even sequence type. The inherent scalability, minimal sample requirements, speed, and simple readout of the nanopore platform would all facilitate on-site and perhaps even automated use: As successive events are recorded, an increasingly clear fingerprint of translocation times and blockage levels will permit online software to “call” the sample as soon as enough events have been accumulated. Our technique is highly portable and customizable, and the binary data would be readily transferrable among different labs.

 

Share this:

• Click to share on Twitter (Opens in new window)
• Click to share on LinkedIn (Opens in new window)
• Click to share on Facebook (Opens in new window)
• Click to print (Opens in new window)
• Click to email a link to a friend (Opens in new window)

Like this:

Like Loading...

Read Full Post »

Zinc-Finger Nucleases (ZFNs) and Transcription Activator–Like Effector Nucleases (TALENs)

Posted in Cell Biology, Signaling & Cell Circuits, Computational Biology/Systems and Bioinformatics, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Genome Biology, International Global Work in Pharmaceutical, Molecular Genetics & Pharmaceutical, Personalized and Precision Medicine & Genomic Research, Pharmaceutical Industry Competitive Intelligence, Scientist: Career considerations, Technology Transfer: Biotech and Pharmaceutical, tagged DNA, Genome engineering, Howard Hughes Medical Institute, Nuclease, Polymerase chain reaction, RNA, University of California Berkeley, Zinc finger nuclease on March 4, 2013| Leave a Comment »

Zinc-Finger Nucleases (ZFNs) and Transcription Activator–Like Effector Nucleases (TALENs)

Reporter: Larry H Bernstein, MD, FCAP

 

TALENs and ZFNs are associated with different mutation signatures

Y Kim,  J Kweon  & Jin-Soo Kim

Zinc-finger nucleases (ZFNs) and transcription activator–like effector nucleases (TALENs) are of great interest for genome engineering in higher eukaryotic cells and organisms. These enzymes

1. contain the same FokI nuclease domain and
2. induce site-specific DNA cleavage.

http://www.nature.com/nmeth/journal/v10/n3/extref/nmeth.2364-S1.pdf

http://www.nature.com/nmeth/journal/v10/n3/full/nmeth.2364.html?WT.ec_id=NMETH-201303

English: Bacterial multi-drug resistance system: complex of dimeric transcription-activator protein BmrR with bound TPP, untwisting the DNA to position the two promoter sites (top) for transcription. Coordinates from PDB file 1R8E, Brennan lab; displayed in KiNG. (Photo credit: Wikipedia)

                                   

English: Diagram of a typical rAAV vector (Photo credit: Wikipedia)

Splicing activation (Photo credit: Allen Gathman)

Related articles

• Efficient Methods for Targeted Mutagenesis in Zebrafish Using Zinc-Finger Nucleases: Data from Targeting of Nine Genes Using CompoZr or CoDA ZFNs (plosone.org)
• Targeted nucleases (pharmaceuticalintelligence.com)
• Breakthrough technique for cutting and pasting genes into DNA works with human cells (moreinterestingthings.com)
• New Gene Therapy Tool Precisely Cuts DNA for a Fraction of the Cost (info.vgxii.com)
• MIT researchers crack cheap, precise gene therapy (extremetech.com)
• http://venturebeat.com/2013/01/27/the-personalized-medicine-revolution-is-almost-here/

00

Share this:

• Click to share on Twitter (Opens in new window)
• Click to share on LinkedIn (Opens in new window)
• Click to share on Facebook (Opens in new window)
• Click to print (Opens in new window)
• Click to email a link to a friend (Opens in new window)

Like this:

Like Loading...

Read Full Post »

Molecular pathogen identification comes to the bedside

Posted in Bio Instrumentation in Experimental Life Sciences Research, Biomarkers & Medical Diagnostics, Computational Biology/Systems and Bioinformatics, FDA Regulatory Affairs, Genome Biology, Genomic Testing: Methodology for Diagnosis, Infectious Disease & New Antibiotic Targets, Medical Devices R&D Investment, Molecular Genetics & Pharmaceutical, Personalized and Precision Medicine & Genomic Research, Pharmaceutical Analytics, Pharmaceutical Industry Competitive Intelligence, Regulated Clinical Trials: Design, Methods, Components and IRB related issues, Statistical Methods for Research Evaluation, Systemic Inflammatory Response Related Disorders, Technology Transfer: Biotech and Pharmaceutical, tagged Biochemistry and Molecular Biology, Biology, DNA, medicine, NK Tran, PCR, Polymerase chain reaction, PubMed, research, surgery on September 19, 2012| 2 Comments »

 

Molecular pathogen identification comes to the bedside

Reporter:  Larry H Bernstein, MD, FCAP

The developments in molecular diagnostics have been proceeding at a rapid pace.  Naturally it is not surprising that it would reach into clinical microbiology early.  Microbiology and virology have many methods for validation of type of pathogen, and the identification of new pathogens can require delay because of use of a State laboratory.  This will be less an issue with the consolidation of regional facilities and associated laboratories.

I present an example of point-of-care technology from the University of California, Davis developed by Gerald Kost and colleagues with UC Lawrence Livermore National Point-of-Care Technologies Center .

Tran NK, Wisner DH, Albertson TE, Cohen S, et al.  Multiplex polymerase chain reaction pathogen detection in patients with suspected septicemia after trauma, emergency, and burn surgery. Surgery 2012 Mar;151(3):456-63. Epub 2011 Oct 5.  nktran@ucdavis.edu

The goal of the study:  to determine the clinical value of multiplex polymerase chain reaction (PCR) study for enhancing pathogen detection in patients with suspected septicemia after trauma, emergency, and burn surgery.

Finding: PCR-based pathogen detection quickly reveals occult bloodstream infections in these high-risk patients and may accelerate the initiation of targeted antimicrobial therapy.

Type study: a prospective observational study

Population:  30 trauma and emergency surgery patients compared to 20 burn patients.

Method:  Whole- routine blood cultures (BCs) were tested using a new multiplex, PCR-based, pathogen detection system. PCR results were compared to culture data.

Arbitrated Case Review

Arbitrated case review was performed by a medical intensivist, 3 trauma surgeons, 3 burn surgeons, 1 microbiologist, and an infectious disease physician to determine antimicrobial adequacy based on paired PCR/BC results. The arbitrated case review process is adapted from a previous study. Physicians were first presented cases with only BC results. Cases were then represented with PCR results included.

Results:

• PCR detected rapidly more pathogens than culture methods.
• Acute Physiology and Chronic Health Evaluation II (APACHE II), Sequential Organ Failure Assessment (SOFA), and Multiple Organ Dysfunction (MODS) scores were greater in PCR-positive versus PCR-negative trauma and emergency surgery patients (P ≤ .033).
• Negative PCR results (odds ratio, 0.194; 95% confidence interval, 0.045-0.840; P = .028) acted as an independent predictor of survival for the combined surgical patient population.

CONCLUSION:

• PCR results were reported faster than blood culture results.
• Severity scores were significantly greater in PCR-positive trauma and emergency surgery patients.
• The lack of pathogen DNA as determined by PCR served as a significant predictor of survival in the combined patient population.
• PCR testing independent of traditional prompts for culturing may have clinical value in burn patients.

NK Tran, et al.  Multiplex Polymerase Chain Reaction Pathogen Detection in Trauma, Emergency, and Burn Surgery Patients with Suspected Septicemia.  Surgery. 2012 March; 151(3): 456–463. PMID: 21975287 [PubMed – indexed for MEDLINE] PMCID: PMC3304499 On-line 2011 October 5.
doi:  10.1016/j.surg.2011.07.030
PMCID: PMC3304499.  NIHMSID: NIHMS288960

Plymerase chain reaction, PCR (Photo credit: Wikipedia)

Related articles

• New Software Makes Synthesizing DNA As Easy As ‘Drag and Drop’ With Icons (blacklistednews.com)
• Cowin to Develop New Lab-On-A-Chip Pathogen Testing Range (azonano.com)

 

00

Share this:

• Click to share on Twitter (Opens in new window)
• Click to share on LinkedIn (Opens in new window)
• Click to share on Facebook (Opens in new window)
• Click to print (Opens in new window)
• Click to email a link to a friend (Opens in new window)

Like this:

Like Loading...

Read Full Post »

Female Fertility, Female Meiotic Recombination, and Neurological Disease – Chromosome 17 and New Markers

Posted in Bio Instrumentation in Experimental Life Sciences Research, Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, Chemical Genetics, Computational Biology/Systems and Bioinformatics, Genome Biology, Genomic Testing: Methodology for Diagnosis, Personalized and Precision Medicine & Genomic Research, Population Health Management, Genetics & Pharmaceutical, Uncategorized, tagged 1000 Genomes Project, Broad Institute, Copy-number variation, Copy-number variation, International HapMap Project, National Human Genome Research Institute, National Institutes of Health, Polymerase chain reaction, QuantaLife, QuantaLife on August 26, 2012| 1 Comment »

 

Harvard Group Using Bio-Rad Digital PCR System as Part of NHGRI-Funded Study of Multi-Allelic CNV

 

Reporter: Aviva Lev-Ari, PhD, RN

August 23, 2012

• By Ben Butkus

Researchers in the Department of Genetics at the Harvard University Medical School have been awarded $500,000 by the National Institutes of Health for the first year of a four-year project to study multi-allelic copy number variation in the human genome.

As part of the research, the Harvard team is using a Bio-Rad QX100 Droplet Digital PCR system as one of two methods to analyze multi-allelic CNVs in human cohorts. The researchers are also using a computational method that compares available whole-genome sequencing data.

Steven McCarroll, a professor of genetics at Harvard Med and director of genetics at the Stanley Center for Psychiatric Research at the Broad Institute, is principal investigator on the grant, which is being administered by NIH’s National Human Genome Research Institute.

According to a recently published grant abstract, McCarroll and colleagues seek to analyze multi-allelic CNVs, which involve genes and other functional elements for which three or more segregating alleles give rise to a wide range of copy numbers — between two and 10 — per diploid human genome.

These multi-allelic CNVs have been “refractory to widely used analysis methods and are not assessed in the genome-scale molecular or statistical approaches used to study genetically complex phenotypes in humans,” the researchers wrote.

The project builds on research that McCarroll’s group previously conducted on characterizing multi-allelic duplication CNVs of a megabase-long inversion polymorphism in a particular locus of chromosome 17 called 17q21.31, which contains markers previously associated with female fertility, female meiotic recombination, and neurological disease.

As part of that research, published in the August 2012 issue of Nature Genetics, the group analyzed read depth in the locus by applying an algorithm called Genome Structure in Populations, or Genome STRiP, to whole-genome sequencing data from 946 unrelated individuals sampled as part of the 1000 Genomes Project; and used droplet-based digital PCR to analyze 120 parent-offspring trios from HapMap.

http://www.nature.com/ng/journal/v44/n8/full/ng.2334.html

They found that their measurements of integer copy number varied from two to eight, and were 99.1 percent concordant across 234 genotypes in overlapping samples, thus validating both the computational and digital PCR methods.

More specifically, for the digital PCR assay, the group designed a pair of PCR primers and a dual-labeled fluorescence-FRET oligonucleotide probe to both the CNV locus and a two-copy control locus. Then they used a droplet generator from QuantaLife to compartmentalize the PCR reaction into uniform 1-nanoliter emulsion-based droplets containing zero, one, or very few template molecules for each locus; and a droplet reader from QuantaLife to count the number of positive and negative droplets, comparing the droplet counts of the CNV locus to the control locus to determine absolute copy number.

QuantaLife originally developed the droplet-based digital PCR system, but was acquired in October by Bio-Rad, which rebranded the platform as the QX100 Droplet Digital PCR system (PCR Insider, 10/6/2011).

Annette Tumolo, director of the digital biology center at Bio-Rad, told PCR Insider this week that McCarroll has access to two such platforms, one of which is in use at Harvard and was obtained from QuantaLife, and one of which Bio-Rad sold to the Broad Institute.

Tumolo said that Bio-Rad maintains “an active and positive relationship” with the McCarroll lab. “They’ve gotten great results [with the QX100], and were able to rapidly publish the Nature Genetics paper,” Tumolo said.

Under the new NHGRI grant, McCarroll and colleagues plan to “accurately analyze mCNVs in reference populations” using both the computational and digital PCR approach, the researchers wrote in their grant abstract.

“By analyzing these data in a statistical framework that incorporates information about genotypes, allele frequencies, inheritance, and haplotypes, we will place mCNV alleles onto the haplotype maps created by HapMap and 1000 Genomes, and render mCNVs accessible to genotype imputation to the fullest extent possible,” the grant abstract states.

In addition, McCarroll’s group hopes to “deeply characterize mCNVs at 10 biomedically important loci, to understand these polymorphisms at the levels of population genetics, mutational rates and histories, and relationships to clinical phenotypes. Finally, we will pilot inexpensive in silico genome-wide association studies for mCNVs based on statistical imputation into existing GWAS data sets.”

The end goal of the project is to discover relationships between disease risk and gene dosage, which will help reveal the molecular etiology of human disease, the researchers wrote.

Related Stories

• RainDance Launches Digital PCR Platform; Claims Sensitivity, Operating Cost Superiority
  March 29, 2012 / PCR Insider
• Bio-Rad Life Sciences Q2 Revenues Drop 4 Percent; Digital PCR Sales Slower than Anticipated
  August 9, 2012 / PCR Insider
• Trovagene Using Bio-Rad Digital PCR System for Trans-Renal Pancreatic Cancer MDx
  May 31, 2012 / PCR Insider
• Trovagene Bets on Digital PCR as Detection Technology for Urine-Based MDx Testing Platform
  May 3, 2012 / PCR Insider
• Bio-Rad Logs 3 Percent Q4 Growth as Company Aims for $20M in Digital PCR Sales in ’12
  March 1, 2012 / PCR Insider
  http://www.genomeweb.com/pcrsample-prep/harvard-group-using-bio-rad-digital-pcr-system-part-nhgri-funded-study-multi-all

Ben Butkus is senior editor of GenomeWeb’s premium content and the editor of PCR Insider. He covers technologies and trends in PCR, qPCR, nucleic acid amplification, and sample prep. E-mail him here or follow his GenomeWeb Twitter account at@PCRInsider.

 

Share this:

• Click to share on Twitter (Opens in new window)
• Click to share on LinkedIn (Opens in new window)
• Click to share on Facebook (Opens in new window)
• Click to print (Opens in new window)
• Click to email a link to a friend (Opens in new window)

Like this:

Like Loading...

Read Full Post »

• Follow Blog via Email

  Enter your email address to follow this blog and receive notifications of new posts by email.

  Email Address

  Follow

  Join 9,459 other subscribers

• Recent Posts

  • Creating a Twitter Space for @pharma_BI for Live Broadcasts March 14, 2023
  • The Vibrant Philly Biotech Scene: Recent Happenings & Deals March 11, 2023
  • Peak oxygen uptake (VO2peak) quantified fitness: Lifelong and late-onset athletes had higher VO2peak than non-athletes March 7, 2023
  • Merck’s sotatercept overachieves, PCSK9 inhibitor passes phase 2 March 6, 2023
  • Bacterial multidrug resistance problem solved by a broad-spectrum synthetic antibiotic March 1, 2023
  • Genetic variation causes human lupus, systemic lupus erythematosus (SLE) February 23, 2023
  • Mimicking vaginal cells and microbiome interactions on chip microfluidic culture February 14, 2023
  • Press Release for Five Bilingual BioMed e-Series in English and in Spanish January 29, 2023
  • 2022 FDA Drug Approval List, 2022 Biological Approvals and Approved Cellular and Gene Therapy Products January 17, 2023
  • Verily announced other organizational changes, 1/13/2023 January 16, 2023

• Archives

  Archives Select Month March 2023  (5) February 2023  (2) January 2023  (3) December 2022  (2) October 2022  (6) September 2022  (2) August 2022  (1) July 2022  (3) June 2022  (3) May 2022  (8) April 2022  (10) March 2022  (7) February 2022  (7) January 2022  (3) December 2021  (3) November 2021  (4) October 2021  (11) September 2021  (5) August 2021  (8) July 2021  (21) June 2021  (8) May 2021  (10) April 2021  (8) March 2021  (15) February 2021  (17) January 2021  (20) December 2020  (10) November 2020  (12) October 2020  (26) September 2020  (17) August 2020  (22) July 2020  (27) June 2020  (33) May 2020  (26) April 2020  (42) March 2020  (22) February 2020  (11) January 2020  (7) December 2019  (20) November 2019  (13) October 2019  (11) September 2019  (3) August 2019  (8) July 2019  (25) June 2019  (47) May 2019  (40) April 2019  (27) March 2019  (29) February 2019  (17) January 2019  (50) December 2018  (14) November 2018  (17) October 2018  (18) September 2018  (17) August 2018  (8) July 2018  (20) June 2018  (22) May 2018  (17) April 2018  (12) March 2018  (20) February 2018  (26) January 2018  (16) December 2017  (6) November 2017  (20) October 2017  (13) September 2017  (16) August 2017  (16) July 2017  (19) June 2017  (20) May 2017  (32) April 2017  (21) March 2017  (10) February 2017  (24) January 2017  (21) December 2016  (43) November 2016  (8) October 2016  (40) September 2016  (67) August 2016  (59) July 2016  (75) June 2016  (37) May 2016  (95) April 2016  (152) March 2016  (175) February 2016  (196) January 2016  (139) December 2015  (90) November 2015  (287) October 2015  (237) September 2015  (115) August 2015  (96) July 2015  (63) June 2015  (44) May 2015  (53) April 2015  (76) March 2015  (95) February 2015  (83) January 2015  (75) December 2014  (75) November 2014  (88) October 2014  (135) September 2014  (117) August 2014  (88) July 2014  (88) June 2014  (109) May 2014  (60) April 2014  (85) March 2014  (58) February 2014  (72) January 2014  (107) December 2013  (104) November 2013  (136) October 2013  (62) September 2013  (32) August 2013  (46) July 2013  (74) June 2013  (110) May 2013  (112) April 2013  (86) March 2013  (92) February 2013  (63) January 2013  (63) December 2012  (46) November 2012  (71) October 2012  (84) September 2012  (82) August 2012  (122) July 2012  (59) June 2012  (34) May 2012  (46) April 2012  (2)

• Categories

  Categories Select Category 3D Printing for Medical Application  (231)    3D Plotting Scaffolds  (43)    BioInks  (33)       Biopolymer Blend Open Porous  (17)       Dental Applications  (10)    BioPrinting in Regenerative Medicine  (73)       Cell Level  (13)       MicroEngineering Cell-Tissue & Systems  (30)       Tissue Engineering  (35)    Cardiovascular and Vascular Systems  (27)       Artificial Vascular Structures  (9)       Cardiovascular Tissue  (8)    Drug Development using MultiOrgan Chip  (11)    Drug Development/Formulation using 3D Printing  (8)    MEMS  (16)       Bio-MEMS  (12)    Organ-on-a-Chip  (11)    Programmable Sensors (Carbon Nano Tubes)  (5) 3rd Party IP: Drug DIscovery  (1) 4D Printing and Meta Materials  (9) Academic Publishing  (222)    Key Opinion Leaders in eScientific Publishing – Interviews with  (8) Advanced Drug Manufacturing Technology  (92)    Antimalarial Preparation  (8)    Autologous Cell Therapy  (11)    Automated Cell Processing  (12) Allergy and Infectious Diseases  (12) Alzheimer’s Disease  (158)    Etiology  (76)    Medical Device Therapies for Altzheimer’s Disease  (14)    Pharmacotherapy and Cell Activity  (48) Anticancer Resistance  (28) Art Exhibits on the Human Condition  (1) Art Inspires Science  (3) Artificial Heart  (2) Artificial Intelligence – General  (182)    An executive’s guide to AI  (9)    Artificial Intelligence – Breakthroughs in Theories and Technologies  (91)    Artificial Intelligence Applications in Health Care  (90)       Artificial Intelligence in CANCER  (25)       Artificial Intelligence in Health Care – Tools & Innovations  (52)    Artificial Intelligence in Medicine – Application for Diagnosis  (50)       Artificial intelligence applications for cardiology  (22)          AI-assisted Cardiac MRI  (9)       Artificial Intelligence in Psychiatry  (5)       Deep Learning in Pathology  (13)    Artificial Intelligence in Medicine – Applications in Therapeutics  (44)    Machine Learning  (39)       Deep Learning  (5)       Natural Language Processing (NLP)  (20)    Machine learning in predicting type 2 diabetes  (1) Auditory and vision  (13) Autism Spectrum Disorders  (10) Autoimmune Inflammatory DIseases  (19)    Crohn’s disease  (5)    Tolerance-inducing Autoimmune Disease Therapeutics  (2)    Ulcerative Colitis  (3) Autophagosome  (2) Bacterial Resistance  (25) Behavior  (26) Behavioral Genetics  (21) Big Data  (75)    Intelligent Information Systems  (46) Bio Instrumentation in Experimental Life Sciences Research  (364)    Analytical Instruments Industry  (5)    Microfuidics  (7) Bio-Ethics  (14) BioBanking  (33) Biodegradable Drug-eluting Material  (7) Bioengineering & reverse engineering design  (41) BioIT: BioInformatics  (142) BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics  (123)    Advanced Computing Platform  (30)       Blockchain Transactions System  (6)          Platform for Content Monetization embedded Content Promotion  (1)    Pancreatic adenocarcinoma classifier  (3)    Simulation Modeling in NGS  (3) Biological Engineering  (39) Biological Networks  (189) Biological Networks, Gene Regulation and Evolution  (472)    Virology  (10) Biomarkers & Medical Diagnostics  (564)    VOC – Volatile Organic Compounds as BioMarkers  (2) Biomedical Measurement Science  (171) BioSimilars  (111) BioTechnology – Venture Creation  (125)    Foundations for supporting Science and Education  (12)    Philanthropy to Academic Institution  (2) BioTechnology – Venture Creation, Venture Capital  (105)    Seeking Talent  (3) BiVAD  (1) Blindness  (6) Bone Disease and Musculoskeletal Disease  (82) Brain Basis of Emotion  (5) Business Career Consideration  (1) Ca2+ triggered activation  (59) Calcium  (19) Calcium Signaling  (67) Calmodulin Kinase and Contraction  (34) Cancer – General  (175) Cancer and Current Therapeutics  (395)    interventional oncology  (42)       Breast Cancer – impalpable breast lesions  (17)       Prostate Cancer: Monitoring vs Treatment  (8) CANCER BIOLOGY & Innovations in Cancer Therapy  (1,090)    Anaerobic Glycolysis  (51)    Cachexia  (18)    Cancer Genomics  (69)       Circulating Tumor Cells (CTC)  (19)          Liquid Biopsy Chip detects an array of metastatic cancer cell markers in blood  (14)             mRNA  (5)          MagSifter chip  (1)       KRAS Mutation  (6)       Li-fraumeni syndrome.  (3)       TP53 – Germline mutations  (5)    cancer metabolism  (20)    Funding Opportunities for Cancer Research  (11)    Genomic Expression  (143)    Glioblastoma  (7)    Hexokinase  (14)    Loss of function gene  (18)    Metabolic Immuno-Oncology  (15)    Metastasis Process  (12)    Methylation  (31)    Microbiome and Responses to Cancer Therapy  (5)    Monoclonal Immunotherapy  (27)    mtDNA  (13)    Oxidative phosphorylation  (56)    Pancreatic cancer  (9)    Pyruvate Kinase  (27)    The NCI Formulary  (1)    tumor microenvironment  (13)    Warburg effect  (50) Cancer Informatics  (93) Cancer Prevention: Research & Programs  (125) Cancer Screening  (102) Cancer Vaccines: Targeting Cancer Genes for Immunotherapy  (25)    Engineering Enhanced Cancer Vaccines  (3) cancer-general  (1) Cardiac and Cardiovascular Surgical Procedures  (318)    Aortic Valve: TAVR, TAVI vs Open Heart Surgery  (86)       Prosthesis-Patient Mismatch (PPM) in TAVI vs SAVR  (1)       TAVI vs Open Heart Surgery  (5)       Transcatheter Aortic Valve Replacement via the Transcarotid Access  (1)       Transcaval TAVR Procedure  (2)       Valve-in-Valve Procedure  (3)    Atrial Fibrilation (a-Fib), Cardiac Pacing and Arrhythmias  (17)    BackBeat’s Cardiac Neuromodulation Therapy (CNT) bioelectronic therapy  (1)    CABG  (46)    Cancer Surgery of the Heart  (10)    Cardiovascular Fluid Management: algorithms  (1)    Heart Transplant  (14)    Heart-Lung Transplant  (9)    Implantation  (2)    Left Main Coronary Artery Disease (LMCAD)  (3)    Mechanical Assist Devices: LVAD, RVAD, BiVAD, Artificial Heart  (18)    Mitral Valve: Repair and Replacement  (63)       TMVR – Transcatheter Mitral Valve Repair  (4)    PCI  (104)       Bioresorbable Vascular Scaffold (BVS)  (3)       Invasive FFR for PCI  (2)       Multivessel PCI FFR Guidance  (1)       Robotic-assisted percutaneous coronary intervention  (1)       Stent Retriever in endovascular thrombectomy  (2)    Peripheral Arterial Disease & Peripheral Vascular Surgery  (62)       Abdominal Aorta  (19)       Carotid Artery  (15)       Limb ischemia  (4)       Thoracic Aorta  (15)    Pulmonary Valve Replacement and Repair  (2)    Renal Denervation  (20)    Replacement  (1)    Robotically assisted Cardiothoracic Surgery  (1)    Tricuspid Valve Repair  (6)    Vena Caval Filters: Device for Prevention of Pulmonary Embolism and Thrombosis  (1) Cardiovascular Pharmacogenomics  (168) Cargo  (3) Cell Biology  (318) Cell Biology, Signaling & Cell Circuits  (661)    Apoptosis  (16)    Autophagy-Modulating Proteins  (5)    “Antibody–enzyme conjugates”  (4)    Cell Processing System in Cell Therapy Process Development  (24)    Endoplasmic reticulum  (2)    Enzymatic mechanism underlying the synthesis of adenosine triphosphate (ATP)  (3)    Ubiquitin  (9) Cerebrovascular and Neurodegenerative Diseases  (122)    Stroke  (8) Chemical Biology and its relations to Metabolic Disease  (460)    #BIGAxisSummit  (1)    Gut Microbiome and Obesity  (7) Chemical Genetics  (349) Child and Adolescent Psychiatry  (13) Childhood cancer  (14) Childhood malnutrition  (3) children  (1) Circulating Progenitor Cells  (27)    Bone marrow derived cells  (17)    Endothelial cells  (7)    Umbilical cord cells  (6) Clinical & Translational  (234) Clinical Diagnostics  (215)    Mass automation of plasma proteins  (13) Clinical Genomics  (135) Coagulation Therapy and Internal Bleeding  (83) Cognition  (46) Commercialization  (20) Components and IRB related issues  (4) Computational Biology/Systems and Bioinformatics  (464)    Computational Histopathology  (2)    Meta-analysis of transcriptome data  (8) Conference Coverage with Social Media  (475)    MassBio  (3) coronavirus  (18) COVID-19  (20) CRISPR/Cas9 & Gene Editing  (188)    CRISPR alternative for editing genes without cutting  (14)       CAST – Alternative to CRISPR/Cas9  (3)       Transposon-encoded CRISPR–Cas systems direct RNA-guided DNA integration  (4)    CRISPR applied to Human Germ Line  (10)       Gene Editing Impact on Longevity  (4) CT  (19) Cytokines  (19) Cytoskeleton  (84) Developmental biology  (105) Diabetes Mellitus  (141)    Artificial Pancreas for Type 2 Diabetes  (1)    Artificial Pancreas for Type1 Diabetes  (5)    Gestational diabetes  (2) Diagnostic Immunology  (56) Diagnostics and Lab Tests  (126)    Liquid Biopsy: Circulating Tumor Cells in Urine and Blood  (8) Digital HealthCare – biotech & internet joint ventures  (46) Disease Biology  (331) Disease Biology, Small Molecules in Development of Therapeutic Drugs  (483) DNA repair  (71)    Apoptosis  (11)    Autophagy  (12)    Cell death pathways  (18)    Proteolysis  (8)    Proteosome  (8) Drug Delivery Platform Technology  (154)    Drug Carrier Design  (19)       Medication Remote Compliance Monitoring  (3)    Exosomes: Natural Carriers for siRNA Delivery  (9) Drug Toxicity  (74) Ecosystems & Industrial Concentration in the Medical Device Sector  (172)    Cardiac & Vascular Repair Tools Subsegment  (128)    Exec Compensation in the Cardiac & Vascular Repair Tools Subsegment  (14)    Massachusetts Niche Suppliers and National Leaders  (20) Electronic Health Record  (9) Embryology  (24) Empathy  (6) Endocrine Diseases  (59) Enzyme Induction  (45)    ion-transporting enzyme  (1)    K + -ATPase.  (1)    Na +  (2) Epigenetics and Environmental Factors  (31) Ethics and Leadership  (10) Executive Compensation – Big Pharma  (3) Fat soluble vitamins  (4) FDA  (68) FDA Regulatory Affairs  (355)    FDA, CE Mark & Global Regulatory Affairs: process management and strategic planning – GCP, GLP, ISO 14155  (52)    ISO 10993 for Product Registration: FDA & CE Mark for Development of Medical Devices and Diagnostics  (30) Fetal Ultrasound  (2) Fiction and medicine  (5) Frontiers in Cardiology and Cardiovascular Disorders  (916)    Acute Myocardial Infarction  (60)       Cardiogenic Shock  (4)    Anemia in CVD Patients  (6)    Cardio-Oncology  (4)    Cardiomyopathy  (57)    Cardiovascular Research  (183)       Myocardial metabolism, Myocardial ischemia, myocardial perfusion, Myocardial adenine nucleotide metabolism  (62)       Pre-Clinical Animal Model Development  (23)    Chronic Thromboembolic Pulmonary Hypertension (CTEPH) and Pulmonary Arterial Hypertension (PAH)  (19)    Cost of Inpatient Stay for Cardiovascular Diagnosis for Admission  (1)    Electrophysiology  (107)       Ablation Procedure vs Drug Therapy  (2)       Arrhythmia Detection with Machine Learning Algorithms  (6)       Cardiotoxic Risks of Immune Checkpoint Inhibitors  (1)       Genomic Analysis of EKG Electrophysiology Genomics  (2)       implantable cardioverter defibrillator (ICD) and External Defibrillation  (1)       implantable pacemaker  (2)       Rare heart rhythm disorders and Long QT syndrome (LQTS)  (5)    Epigenetics and Cardiovascular Risks  (55)    Heart Failure (HF)  (46)       Acute coronary syndrome  (6)       congestive heart failure  (17)    Medical Devices R&D and Inventions  (207)       Assist Devices: LV  (3)       RV  (1)       Stents & Tools  (89)       Valves & Tools  (64)    Origins of Cardiovascular Disease  (416)       Atherogenic Processes & Pathology  (177)       Congenital Heart Disease  (34)          Genetic Mutations in Congenital Heart Disease  (4)       inflammation independent of lipid levels  (11)       Systemic Inflammatory Diseases as CVD Risk  (10)    Pharmacotherapy of Cardiovascular Disease  (352)       HTN  (172)       HTN in Youth  (7)       Resident-cell-based  (43)       Subarachnoid Hemorrhage (SAH)  (1)       Transthyretin amyloid cardiomyopathy (ATTR-CM)  (1)    Spontaneous Coronary Artery Dissection (SCAD)  (6)    Stroke  (13)       endovascular thrombectomy  (5)    Vascular Diseases  (20)       mesenteric ischemia  (3) Future Pandemics Inform-graphics  (1) Gastroenterology  (37) Gene Regulation  (226) Gene Regulation and Evolution  (134) Genetics & Innovations in Treatment  (170) Genetics & Pharmaceutical  (199) Genome Biology  (956)    Exosomes  (24)    Gene Therapy & Gene Editing Development  (37)    mRNA Therapeutics  (17)    Mutagenesis  (26)    Single Cell Genomics  (22)    Single-cell sequencing  (18)    Variation in human protein-coding regions  (30) Genomic Endocrinology  (41) Genomic Testing: Methodology for Diagnosis  (412) Genomics Pharmacy  (37) Global Market of Medical Devices Technology  (2) Global Medical Publishers by Country  (3) Global Partnering & Biotech Investment  (45) GLP  (4) Glycobiology: Biopharmaceutical Production  (14) Glycobiology: Biopharmaceutical Production, Pharmacodynamics and Pharmacokinetics  (26) Health Care System by Country  (45)    AI Models in Healthcare  (6)    Centers for Medicare & Medicaid Services  (7)    Health in Israel  (2)    United States  (21)       Hospital-based Medical Innovations  (7)       Medical Recertification and Maintenance of Certification (MOC)  (1) Health Economics and Outcomes Research  (376)    Women Health  (13)       Heart and Brain Disease in Women  (3) Health Law & Patient Safety  (160)    and Bioethics  (13)    Biotechnology  (11)    Health Law Policy  (10) HealthCare IT  (136) Healthcare Reform  (109)    Accountable Care Organizations  (25)    Affordable Care Act  (29)       Repair  (1)       Repeal ACA  (1)       Replace  (1)    Federal Budget Appropriations  (25)    Healthcare costs and reimbursement  (54)    Indigent Nutrition  (13)    Medicare and Medicaid  (20)    Prescription Drugs Costs  (21)    Skilled Nursing Facilities  (6)    Technology Capital Expenses  (15)    Uninsured and Underinsured  (19) Hematology  (99)    Acute lymphocytic leukemia  (18)    Acute myelocytic leukemia  (21)    Hematopoiesis  (48)    Lymphoma  (21) History and physical exam  (6) Human aging  (27) Human Immune System in Health and in Disease  (235) Human Sensation and Cellular Transduction: Physiology and Therapeutics  (20) Image Processing/Computing  (41) Imaging-based Cancer Patient Management  (116) Immuno-Oncology & Genomics  (125)    mRNA platform in Drug DIscovery  (6) Immunodiagnostics  (126)    Cancer-Immune Interactions  (20)    CNS-Immune Interface  (3)    Neuronal Circuits  (4) Immunology  (112)    Adaptive Immune Response to Biomaterials and Tissue Repair  (9)    Antibody Responses Predict Antigen Exposure  (15)    Cytokine Receptor Structure  (7)    Human Antibody Response  (15)    Human Circulating Antibody Repertoire  (9)    Human Naive B Cell Repertoire  (8)    MHC Repertoires for Antigen Prediction  (8)    Protection Against Autoimmune Disease  (7)    Synthetic Immunology: Hacking Immune Cells  (12) Immunotherapy  (119)    CAR-T  (13)    combination immunotherapies.  (11)    Efficacy of Anti-Tumor T Cells  (11)    Engineering Better T Cells  (7)    Immune Engineering  (13)    Immune Modulatory  (13)    Integrin-targeted Combination Immunotherapy  (5)    Modulating Macrophages in Cancer Immunotherapy  (11)    NK Cell-Based Cancer Immunotherapy  (16)    Synergistic Innate and Adaptive Immunotherapy  (13) Income Disparity  (2) Infectious Disease & New Antibiotic Targets  (161)    Antibiotic resistance  (6)    Viral diseases  (37)       Myocarditis  (1) Infectious Disease Immunodiagnostics  (56)    Virology – Vector-borne DIsease  (17) Inflammasome  (26)    Anti-tumor necrosis factor drugs (TNF inhibitors)  (2) Innovation in Immunology Diagnostics  (105)    Universal Immune Cell Therapies (uICT)  (9) Innovations  (185)    R&D Expenditure  (8) Innovations in Neurophysiology & Neuropsychology  (142)    Age and Life Expectancy  (1)    autism spectrum disorder  (1)    Circadian Rhythm and Sleep  (6)    hNPCs  (1)    Nervous System Function  (2)    Relapsing Multiple Sclerosis  (1) Intellectual Property  (75)    Disputes and Settlements  (8)    IP Valuation Models – Pricing Intangible Assets  (5)    Patent Law in Biotech  (12) Intellectual Property, Innovations, Commercialization, Investment in technological breakthrough  (106) International Global Work in Pharmaceutical  (188) Interventional Oncology: Radiofrequency Ablation, Transarterial Chemoembolization, Microwave Ablation and Irreversible Electroporation (IRE)  (12) Interviews with Scientific Leaders  (297)    Albany Medical Center Prize in Medicine and Biomedical Research  (1)    Annual Breakthrough Prize  (2)    Annual Lewis S. Rosenstiel Award  (1)    Awards in Cardiology and Cardiovascular Medicine  (3)    CEOs of Biotech Companies  (2)    Gerald D Aurbach Award for Outstanding Translational Research  (1)    Interviews with Key Opinion Leaders (KOLs)  (6)    James Prize in Science and Technology Integration  (1)    Jessie Stevenson Kovalenko Medal – Medical Sciences  (1)    Lemelson-MIT Prize  (1)    Life Sciences Breakthrough Prize  (2)    Mosteller Statistician of the Year Award  (1)    Mourning the Loss of a Scientific Leader  (2)    National Academy of Sciences AWARDS  (2)    Nobel Prize WInners  (20)    Noble Prize (Not Nobel Prize)  (1)    Paul Marks Prize for Cancer Research  (1)    Russ Prize recognizes bioengineering achievements worldwide  (1)    The Dan David Prize  (5)    Warren Alpert Foundation Prize Recipients  (3)    Wolf Prize  (1)    Wolf Prize in Medicine  (1)    women in science  (3) Investment in Technological Breakthrough  (59) Ionic Transporters Na+  (25) IP Development by LPBI Group Team  (9) IP Development by LPBI Group Team & Other Organizations  (7) ISO 14155  (3) Joint Venture – SBH & M3DP: SOP and Arrangements  (1) Justice & Law  (1) K  (15) Lab-on-a-chip  (2) Landscape  (1) Lasers and photonics  (18)    Midrange IR spectroscopy  (1) Law and Medicine Conflicts  (13) Leadership, Power, Social Interlocking Connections  (13) Lipid metabolism  (108)    Fatty acids  (37)    Lipids  (37)    PCSK9 Inhibitor Therapy  (19) Lipidomics  (10) Liposome  (3) Liver & Digestive Diseases Research  (119) LPBI Group, e-Scientific Media, DFP, R&D-M3DP, R&D-Drug Discovery, US Patents: SOPs and Team Management  (128)    Award Nominations  (5)    BioMed e-Books e-Series by LPBI Group  (7)    Feedback from Investors: LPBI Portfolio of Five Businesses  (2)    LPBI Group Founder – Aviva Lev-Ari  (21)    PhD  (1)    RN  (1) LPBI Management  (15) Massachusetts Academy of Sciences (MAS)    (2) Materials Science & Engineering  (36)    Organic BioElectronics  (1) Math  (13)    Great Discoveries  (7) Mechanical Assist Devices: LVAD  (4) Medical and Population Genetics  (190) Medical Devices R&D Investment  (239)    21st Century Cures Act  (2)    CE Mark & Global Regulatory Affairs: process management and strategic planning – GCP  (13)    Rhythm management device hardware: dual-chamber pacemakers  (1)       Systolic HTN  (1) Medical Imaging Technology  (55) Medical Imaging Technology, Image Processing/Computing, MRI, CT, Nuclear Medicine, Ultra Sound  (160)    Circumferential-Intravascular-Radioluminescence-Photoacoustic-Imaging (CIRPI)  (3)    Echocardiography  (2)    MRI  (17)    Noninvasive Diagnostic Fractional Flow Reserve (FFR) CT  (5)    Ultra Sound  (7) Melatonin & Circadian Regulation  (9) memory loss  (1) Metabolism  (235)    Biochemical pathways  (141)       Enzymes and isoenzymes  (71)          Dehydrogenase  (15)          Kinase  (27)          Phosphatase  (8)          Phosphorylase  (21)          tyrosine decarboxylases  (2)    Inosine nucleotides  (8)    Leloir pathway  (6)    microbiome  (6)    Pentose monophosphate shunt  (14)    Pyridine nucleotides  (28)       Pyridine nucleotide transhydrogenase  (7) Metabolomics  (317) Methods  (37) Mg++  (10) Microbiologial genetics  (33) Microbiology  (67)    Virology  (14) Micronutrients  (15) Microvesicle  (6) Microwave Ablation and Irreversible Electroporation (IRE)  (1) Mobile Healthcare  (4) Molecular Genetics & Pharmaceutical  (374)    Organoids  (3) Monoclonal antibody therapy  (13) Mutant Gene Expression  (41) Myocardial adenine nucleotide metabolism  (5) Myocardial Ischemia  (25) Myocardial Metabolism  (35) Na-K transport  (39) Na-K-ATPase  (26) Nanotechnology for Drug Delivery  (96) Nephrology  (46) Nephrology & Regenerative medicine  (9) Neurodegenerative Diseases  (86)    hNPCs  (3)    MS  (5) Neurohumoral Transmission  (66)    Ca2+ triggered activation  (19)    Calmodulin  (12)    Parkinson’s disease dyskinesia levodopa  (3)    PKC  (13)    Synaptic vesicle  (20) Neurological Diseases  (120) Neuroscience  (126) Next Generation Sequencing (NGS)  (24)    Nanopore sequencing  (1) NIH Common Fund  (1) Nitric Oxide in Health and Disease  (135) Nuclear Medicine  (11) Nutrigenomics  (67) Nutrition  (193)    Nutritional Supplements: Atherogenesis, lipid metabolism  (48) Nutrition and Phytochemistry  (60)    Minerals in Medicine  (4)    Plant extract  (22) Nutrition Disorders  (29) Nutritional Supplements: Atherogenesis  (30) Obesity  (17) Oligonucleotide Therapeutics & Delivery  (6) Oncolytic virus & OncoViro-Therapy  (21)    Synthetic Virology  (1) Organ-on-a-Chip & 3D Printing in Life Sciences  (7) Pain: Etiology, Genetics & Innovations in Treatment  (24) Parasitology  (5)    Malaria  (4)    Tropical diseases  (1) Patents  (34) Patient Experience  (64)    Art therapy  (1)    Hospital reputation  (7)    Humor  (1)    Music therapy  (2)    Pain Alleviation  (7)    Patient Outlook  (28)    Reputation of Interventionist  (4)    Support Staff  (13)    Supportive therapies  (6)       laughfter  (1)    Surgical Procedure  (7) Patient Experience: Personal Memories of Invasive Medical Intervantion  (30) Patient’s Voice: Personal Experience with Invasive Medical Procedures  (18) Perioperative Statins at Noncardiac Surgery  (1) Personal Health Applications: Tech Innovations serves HealhCare  (35) Personalized and Precision Medicine & Genomic Research  (715)    Longevity  (1)    New Drug Approval  (4)    Patient-centered Medicine  (34)       cell-based therapy  (8)       stem cell biology and patient-specific  (6)    Personalized Medicine Coalition  (8)    Precision Cancer Medicine  (17) Phagophore  (1) Pharmaceutical Analytics  (258)    Pharmacologic toxicities  (61) Pharmaceutical Discovery  (106)    Rapid automation of plasma protein pools  (16) Pharmaceutical Drug Discovery  (194)    Drug Development Process  (51)       Drug Discovery Chemistry  (27)    drug repurposing  (8) Pharmaceutical Industry Competitive Intelligence  (338)    Pharmacovigilance  (6)    Value-based Drug Pricing  (8) Pharmaceutical R&D Informatics  (42) Pharmaceutical R&D Investment  (297) Pharmacodynamics and Pharmacokinetics  (23) Pharmacogenomics  (156) Photography Collection  (1) Physics  (12) Placenta  (7) Population genetics  (25) Population Health Management  (189)    Evolution of Biology Through Culture  (13)    U.S. Employment-to-Population Ratio  (8) Population Health Management, Genetics & Pharmaceutical  (620)    COVID-19  (155)       Biomarkers: Inflammation  (36)       Cancer Researchers Fighting COVID-19  (14)       COVID-19 effects on Human Heart  (15)       COVID-19: Pandemic Surgery Guidance  (19)       Economic Impact of Coronavirus Pandemic  (41)       number of asymptomatic infections  (27)       Treatment Protocols for COVID-19  (62)    SARS-CoV-2  (138)       Coronavirus Gene Expression  (65)          SARS-CoV-2 circulating variants   (14)          SARS-CoV-2 Viral Variants  (18)       Glycosylation and its Role in SARS-CoV-2 Viral Pathogenesis  (2)       Immune-Mediation (independent immunopathology: lung and reticuloendothelial system)  (29)       Mechanisms of infection by SARS-CoV-2  (21)       SAR-Cov-2 a vasculotropic (blood vessels) RNA Virus  (35)          SARS-COV2 Hijacking the Complement and Coagulation Systems  (19)       Seasonality & Environmental Factors in Resurgence  (26)       Serology tests for coronavirus antibodies  (44)       T-cell response to SARS-CoV-2 infection  (14)       Vaccinology  (51)          Mechanism of Thrombosis with AZ & J&J COVID-19 Vaccines  (5)       Virtual Drug Molecule Screening targeting SAR-CoV-2 proteins  (8)    Virus Infective Acute Respiratory Syndrome: SARS-CoV  (92) Population Health Management, Nutrition and Phytochemistry  (150) Preimplantation Genetic Diagnosis and Reproductive Genomics  (12) Protein-energy malnutrition  (11) Proteomics  (359)    Amino acids  (51)    Proteins  (133) Pulmonary diseases  (16)    Lung and pulmonology  (15) Pulmonary pathology  (8) Radio Frequency (RF)-based Surgical Solutions  (2) REAL TIME Conference Coverage Twitter’s Hashtags and Handles per Presentation/session  (30) Regenerative Biology and Medicine  (91) Regulated Clinical Trials: Design, Methods, Components and IRB related issues  (265) Reproductive Andrology, Embryology, Genomic Endocrinology, Preimplantation Genetic Diagnosis and Reproductive Genomics  (111) Reproductive Biology & Bio Instrumentation  (76) RNA Biology  (79)    RNA interference (RNAi) therapeutic  (4) RNA Biology, Cancer and Therapeutics  (61) RVAD  (1) SBIR, SBA, NIH, NSF  (3) Schizophrenia  (16) Scientific & Biotech Conferences: Press Coverage  (172) Scientific Publishing  (606)    Curation  (583)       Curation methodology  (40)       De novo synthesis  (16)       Dialectic  (17)       Discovery process  (71)       Evolutionary cognition  (52)       Experimental validation  (90)       Explanatory  (91)       Historical relevance  (71)       Technology Advance Assessment of  (69)       Theoretic convergence  (27)    Open Access Journals  (36) Scientist: Career considerations  (193)    Big Data & Analytics  (14)    Data Science  (10)    Women in Life Sciences  (11) Scoop.it  (19) Sensors & Analytics  (17) Sepsis  (2) Severe Autism  (3) Signaling  (99)    Phosphorylation  (41)    S-nitrosylation  (12)    Ubiquitinylation  (32) Signaling & Cell Circuits  (128) single cell sequencing  (1) Small Molecules in Development of Therapeutic Drugs  (69) Social Development  (15) Specialized 3D BioPrinters (Cornea  (1) Statistical Methods for Research Evaluation  (136) Stem Cells for Regenerative Medicine  (174)    Cardiac muscle regeneration  (18)    Competition in regenerative stem cell science  (17)    Human neural progenitor cells  (7)    Skeletal muscle regeneration  (8) STORM  (2) Stress Disorders  (18) Substance Abuse  (4) Systemic Inflammatory Response Related Disorders  (158)    Inflammatory Pathophysiology  (6) Technology Transfer: Biotech and Pharmaceutical  (351) therapeutics  (2) Tissue Engineering and Regenerative Medicine  (21) Tissue Microenvironment  (3) Transarterial Chemoembolization  (2) Transcriptomics  (42) Transformative Technologies in Healthcare  (12) Translational Science  (223)    Best evidence  (52)    Bias measurement tools  (2)    Evidence-based decision-making  (26)    Inferential analysis  (25)    Intra-rater error  (1)    Quality Assurance  (8)    Random Error  (2)    Systematic Error (Bias)  (6)    Total error  (2)    Translational Effectiveness  (49)    Translational Research  (88) Trends in Global Economy  (6) Uncategorized  (1,007) Unfolded Protein Response (UPR)  (5) US and Global Economy: Trends and Findings  (8) Venture Capital  (44)    Income Geographic Distribution  (2)    Institutional Capital Raised by Female Founders  (5) virology  (9) Vision  (6) Voices of Patients and Healthcare Providers  (21) Water Transporters  (7) Wearable Tech + Digital Health  (3) Anemia  (21) Neutropenia  (8) Neutrophilia  (9) Platelet count disorder  (9) Folate and B12  (7) Iron deficiency  (5) Myelodysplasia  (12) Myelofibrosis  (10)

• Meta

  • Log in
  • Entries feed
  • Comments feed
  • WordPress.org

• • 2012pharmaceutical
  • Amandeep Kaur
  • Aashir Awan, Phd
  • Abhisar Anand
  • Adina Hazan
  • Alan F. Kaul, PharmD., MS, MBA, FCCP
  • alexcrystal6
  • anamikasarkar
  • apreconasia
  • aviralvatsa
  • David Orchard-Webb, PhD
  • danutdaagmailcom
  • Demet Sag, Ph.D., CRA, GCP
  • Dror Nir
  • dsmolyar
  • Ethan Coomber
  • evelinacohn
  • Gail S Thornton
  • Irina Robu
  • jayzmit48
  • jdpmd
  • jshertok
  • kellyperlman
  • Ed Kislauskis
  • larryhbern
  • Madison Davis
  • marzankhan
  • megbaker58
  • ofermar2020
  • Dr. Pati
  • pkandala
  • ritusaxena
  • Rick Mandahl
  • sjwilliamspa
  • Srinivas Sriram
  • stuartlpbi
  • Dr. Sudipta Saha
  • tildabarliya
  • vaishnavee24
  • zraviv06
  • zs22