Posts Tagged ‘genomics’

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LPBI Update

Leaders in Pharmaceutical Business Intelligence (LPBI) Group, Newsletter #1 – February 2020

Welcome to the premier issue of LPBI Group News, where readers can find relevant news and updates about science, business and medical innovation. This newsletter is distributed as a service for our readers.

The Conference Forum Highlights Immuno-Oncology 360° in New York

The Conference Forum is hosting Immuno-Oncology 360°, which reports on current data and developments of immuno-oncology in the science and business communities. The summit takes place on February 26-28 at the Crowne Plaza Times Square in New York.

Please visit www.io360summit.com to register and use code LPBI20 for a 20% discount. 

Ahead of the conference, Immuno-Oncology 360° has created a series celebrating their women speakers in the work they are doing to fight cancer. To read the series, visit: https://theconferenceforum.org/conferences/immuno-oncology-360/io360%cb%9a-leadership-interviews/

This information is published in conjunction with the Immuno-Oncology 360° Summit.


Venture Summit Attracts Top Innovators in Silicon Valley

Leaders in Pharmaceutical Business Intelligence (LPBI) Group is one of the sponsors of Venture Summit | West, “Where Innovation Meets Capital.”

The meeting will be held on March 23-24 at the Santa Clara Convention Center, Silicon Valley.


Special offer:  Register Now & Save $450 off (Use discount code “LPBI-VIP”)

For more information, please visit: https://pharmaceuticalintelligence.com/2019/12/17/venture-summit-west-where-innovation-meets-capital-march-23rd-24th-2020-santa-clara-convention-center-silicon-valley/


e-Proceedings of 15th Annual Personalized Medicine Conference at Harvard Medical School

The 15th Annual Personalized Medicine Conference at Harvard Medical School, Boston last year [November 13-14, 2019], entitled  The Paradigm Evolves, explored the science, business and policy issues facing personalized medicine. In today’s world, scientists need to understand how molecular diagnostics augmented by artificial intelligence, data analytics and digital health empowers physicians and patients in their health care decisions.

Please visit for LPBI Group coverage of the meeting, including social media activities at the conference:



  •   3D Medical BioPrinting Technology Featured in Podcast

LPBI Group leaders, Aviva Lev-Ari, Ph.D., R.N., Stephen Williams, Ph.D., and Irina Robu, Ph.D., spoke with Partners in Health and Biz, a half-hour audio podcast that reaches 40,000 listeners, about the topic of 3D Medical BioPrinting Technology: A Revolution in Medicine.

Please click on this link to hear the podcast. https://www.youtube.com/watch?v=laozyrfi29c.

The topic is also the title of a recently offered e-book by the LPBI Group on 3D BioPrinting, available on Amazon/Kindle Direct [https://www.amazon.com/Medical-BioPrinting-Technologies-Patient-centered-Patient-Centered-ebook/dp/B078QVDV2W]. 

The 3D BioPrinting technology is being used to develop advanced medical practices that will help with previously difficult processes, such as delivering drugs via micro-robots, targeting specific cancer cells and even assisting in difficult eye operations.

The table of contents in this book includes: Chapter 1: 3D Bioprinting: Latest Innovations in a Forty year-old Technology. Chapter 2: LPBI Initiative on 3D BioPrinting, Chapter 3: Cardiovascular BioPrinting, Chapter 4: Medical and Surgical Repairs – Advances in R&D Research, Chapter 5: Organ on a Chip, Chapter 6: FDA Regulatory Technology Issues, Chapter 7: DNA Origami, Chapter 8: Aptamers and 3D Scaffold Binding, Chapter 9: Advances and Future Prospects, Chapter 10: BioInks and MEMS, Chapter 11: BioMedical MEMS, Chapter 12: 3D Solid Organ Printing and Chapter 13: Medical 3D Printing: Sources and Trade Groups – List of Secondary Material. 


New e-Book: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS & BioInformatics, Simulations and the Genome Ontology

LPBI Group’s latest e-book entitled, Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS & BioInformatics, Simulations and the Genome Ontology, offers the reader content curation with embedded videos and audio podcasts, real-time conference e-Proceedings by LPBI’s scientists and professors and archived tweets of quotes from speakers at leading biotechnology conferences.

Please click on this link on Amazon/Kindle Direct: https://www.amazon.com/dp/B08385KF87


The book integrates in a single volume four distinct perspectives: basic science, technologies and methodologies, clinical aspects and business and legal aspects of genomics research. “The materials in this book represents the scientific frontier in Biological Sciences and Medicine related to the genomics aspects of disease onset,” said Aviva Lev-Ari, Ph.D., R.N., and founder of LPBI Group.

The book addresses:

  • aspects of life: the Cell, the Organ, the Human Body and Human Populations;
  • methodologies of genomic data analysis: Next Generation Sequencing, Gene Editing, AI, Single Cell Genomics, Evolution Biology Genomics, Simulation Modeling in Genomics, Genotypes and Phenotypes Modeling, measurement of Epigenomics effects on disease, and developments in Pharmaco-Genomics.

Additionally, artificial Intelligence in medicine is covered in Part 3 of the e-Book, which represents the frontier in this emerging field, with topics, such as the science, technologies and methodologies, clinical aspects, business and legal implications as well as the latest machine learning algorithms harnessed for medical diagnosis.

This e-book is significant because it:

  • contains 326 articles on topics, such as gene editing, bioinformatics and genome ontology;
  • incorporates 74 e-Proceedings created in real time by the Book’s authors and editors
  • includes four collections of Tweets representing quotes from speakers at global leading conferences on Genomics
  • has 13 locations of Videos and Audio Podcasts that serve to enrich the e-Reader’s experience.

We welcome your comments and suggestions. Please send them to Aviva Lev-Ari at avivalev-ari@alum.berkeley.edu.

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Nano-guided cell networks as conveyors of molecular communication

Nature Communications
Article number:
07 March 2015
28 August 2015
12 October 2015


Advances in nanotechnology have provided unprecedented physical means to sample molecular space. Living cells provide additional capability in that they identify molecules within complex environments and actuate function. We have merged cells with nanotechnology for an integrated molecular processing network. Here we show that an engineered cell consortium autonomously generates feedback to chemical cues. Moreover, abiotic components are readily assembled onto cells, enabling amplified and ‘binned’ responses. Specifically, engineered cell populations are triggered by a quorum sensing (QS) signal molecule, autoinducer-2, to express surface-displayed fusions consisting of a fluorescent marker and an affinity peptide. The latter provides means for attaching magnetic nanoparticles to fluorescently activated subpopulations for coalescence into colour-indexed output. The resultant nano-guided cell network assesses QS activity and conveys molecular information as a ‘bio-litmus’ in a manner read by simple optical means.

At a glance


View all figures


  1. Nano-guided cell networks for processing molecular information.
    Figure 1
  2. Cells express functional, interchangeable protein components indicating both fluorescence and ability for streptavidin-linked surface coupling.
    Figure 2
  3. Cells equipped with magnetic nanoparticles (mNPs) via streptavidin-mediated interaction with surface-expressed proteins.
    Figure 3
  4. Affinity-based probing for functional analysis of AI-2-induced protein expression.
    Figure 4
  5. Single and multi-population cell responses to autoinducer-2.
    Figure 5
  6. Binning molecular information through cell-based parallel processing and magnetically focusing fluorescence into collective consensus output.
    Figure 6
  7. Extension of nano-guided cell networks for hypothetical regulatory structures.
    Figure 7



It has become increasingly apparent that a wealth of molecular information exists, which, when appropriately accessed, can provide feedback on biological systems, their componentry and their function. Thus, there is a developing niche that transcends length scales to concurrently recognize molecular detail and at the same time provide understanding of the overall system1, 2. An emerging scheme is to develop nano- to micro-scaled tools that intimately engage with biological systems through monitoring and interacting at the molecular level, with synthetic biology being one such tool3, 4, 5, 6, 7.

While synthetic biology is often viewed as an innovative means for ‘green’ product synthesis through the genetic rearrangement of cells, their biosynthetic capabilities and their regulatory networks can instead be tuned for executive function8, 9, 10. That is, cells can be rewired to survey molecular space3, 11, 12 as they have sophisticated capabilities to recognize, amplify and transduce chemical information13. Further, they provide a means to connect biological systems with traditional microelectronic devices and in doing so present a potential interface between chemically based biomolecular processing and conventional vectors of information flow, such as electrons and photons14, 15, 16. Specifically, through engineered design, cell-based molecular processing can be further coupled to enable external abiotic responses. Cells, then, represent a versatile means for mediating the molecular ‘signatures’ common in complex environments, or in other words, they are conveyors of molecular communication17, 18, 19.

Further, beyond clonal cell-based sensors, there is an emerging concept of population engineering to establish microorganisms in deliberate networks that enable enriched system identification through a combination of distinctive yet coexistent behaviours, including, perhaps, competitive or cooperative features8, 20, 21, 22, 23, 24, 25. We posit the use of cell populations assembled in parallel¸ where multiple microbes with distinct molecular recognition capabilities work congruently. An advantage is that populations, as opposed to few cells, can facilitate thorough sampling since the presence of many cells increases their spatial breadth and per-cell data contributions (Fig. 1a). Each cellular unit undergoes independent decision-making and contributes a datum to its entire constituency. The prevalence of data provided within the population, then, substantiates a collective output by the system based on the molecular landscape. As follows in a multi-population system, molecular input thus influences the outcomes of each population, and elicits plural responses when the molecular input ranges overlap the ranges of the sensing populations21, which can define classification boundaries (Fig. 1a). Cell-mediated classification was posited in silico by Didovyk et al.21, where reporter libraries with randomized sensitivities to a molecular cue elicit concentration-dependent fluorescent patterns and these are elucidated by population screening . In the present construct, multiple populations enable multiplexed analysis, resulting, here, in a response gradation that is designed to index the molecular input ‘signature’. Consequently, the fed-back information becomes transfigured beyond a dose-dependent cell-by-cell analysis. That is, the output is predicated by the comparison between the populations rather than accumulation of response within a total population.

Figure 1: Nano-guided cell networks for processing molecular information.

Nano-guided cell networks for processing molecular information.

(a) Biotic (multicellular) processing is facilitated by cell recognition, signal transduction and genetic response. The genetically encoded response reflects the identity and prevalence of the target molecule(s). Biotic processing includes both increased cell number of responders and their genetically tuned response patterns. (b) Abiotic processing, used in conjunction with biotic processing, adds dimensionality to cell-based output by modifying through a physical stimulus (in our example, magnetic focusing). (c) Schematic of a cell population and nanomaterial-based network comprising both biotic (green/red axis) and abiotic (black axis) processing mechanisms. This conceptual system interprets molecular information by intercepting diverse molecular inputs, processes them autonomously through independent cell units within the system and refines output to include positive responders that are viewed via orthogonal means (visual classification). The system’s hierarchical structure allows molecular information to be refined into categorized collective outputs.

With population engineering as a premise for enriched molecular information processing, we engineered cell species, each to achieve an appropriate output through genetic means. There is conceptual basis for incorporation into networks, such as through mobile surveillance and position-based information relay26, 27. Hence, it is conceivable that, in addition to autonomous molecular recognition and processing afforded by synthetic biology, the use of physical stimuli to enable cell response could confer similar networking properties28, 29. For example, the complete information-processing ‘repertoire’ can be expanded beyond specific cell responses by the integration of external stimuli that serve to collate cell populations30. Specifically, we envision integration of nanomaterials that enable co-responses to molecular inputs, such that cell populations employ traditional reporting functions, that is, fluorescence marker expression, as well as responses that enable additional processing via the integration of stimuli-responsive abiotic materials (Fig. 1b).

In our example, cells are engineered to respond by permitting the attachment of magnetic nanoparticles (mNPs), such that each fluorescent cell becomes receptive to a magnetic field. Thus, the combination of cell-nanoparticle structures provides further dimensionality for the conveyance of molecular information (via magnetic stimulation). That is, without magnetic collation the fully distributed system would harbour diffuse responses; a magnetically stimulated system results in acute output due to a filtering and focusing effect (Fig. 1b)31, 32, allowing binned information to be readily, and fluorescently, conveyed.

The detection and interpretation of signalling molecules in our example is based on a microbial communication process known as quorum sensing (QS). The molecules, autoinducers (AIs), are secreted and perceived within a microbial community; once accumulated, the AI level indicates that the population size has reached a ‘quorum’33, 34. By surpassing a threshold concentration, the AI signalling coordinates population-wide phenotypic changes35. We have designed a QS information processor that utilizes two cell populations to independently interrogate natural microbial communities and generate information about QS activity by accessing AI-2 (ref. 36). Each cell population becomes ‘activated’ in response to a characteristic AI-2 level by expressing a fluorescent marker and a streptavidin-binding peptide (SBP) on the outer membrane38. SBP provides a means for collating data by binding mNPs that are introduced into the community. Using a post-processing magnetic sweep, the system as a whole interprets a molecular landscape and refines output into colour-categorized, or ‘binned,’ states (no fluorescence, red, or red and green) through (1) parallel population processing and (2) acute focusing (Fig. 1c).

The use of engineered cells as data-acquiring units and selectively equipping each with functional nanomaterials to form a redistributable processing system merges two paradigms: decentralized, active probing at a molecular scale and self-organization of units through structured dependencies on stimuli42. The population-based system overall contributes categorized feedback about a biological environment.


Surface expression of SBP and fluorescent protein fusions

First, we established expression of a fusion protein consisting of a fluorescent marker (enhanced green fluorescent protein (eGFP) and variants) and SBP. Importantly, for SBP to function as a coupling agent between cells and mNPs, we used AIDAc (kindly shared by J. Larssen)40 to export the chimeric protein to Escherichia coli’s outer surface. Translocation to a cell’s surface utilizes a signal peptide (for inner membrane translocation) and AIDAc as an outer membrane autotransporter pore38, 39, 40, 41, with the passenger protein linked to each. In Fig. 2a, we depict expression of three different constructs using Venus, eGFP and mCherry for optical transmission, and the AIDAc translocator domain for surface localization. These constructs are mapped inSupplementary Fig. 1. After induction with isopropyl B-D-1-thiogalactopyranoside (IPTG), cultures were probed for surface expression of the SBP portion of the tagged fluorescent protein. Cells were incubated with fluorescently labelled streptavidin; the fluorophore of the streptavidin probe was orthogonal to the expressed fluorescent protein. The multiple fluorescence emissions were analysed by confocal microscopy without spectral overlap. The fraction of cells (fc) that exhibit colocalized fluorescent protein and the fluorescently-labeled streptavidin is reported in Fig. 2b, showing that SBP–Venus cells bound streptavidin at a slightly lower frequency than SBP–mCherry and SBP–eGFP, which exhibited statistically similar fractions (fc=0.7).

Figure 2: Cells express functional, interchangeable protein components indicating both fluorescence and ability for streptavidin-linked surface coupling.

Cells express functional, interchangeable protein components indicating both fluorescence and ability for streptavidin-linked surface coupling.

(a) A T7 cassette was used to express chimeric proteins consisting of a membrane autotransporter domain (AIDAc), one of several fluorescent proteins and a streptavidin-binding peptide (SBP). Fluorophore-tagged streptavidin (SA) was used to bind SBP. (b) Of cells expressing fluorescent proteins (FP), those also marked by SBP coupling are represented as a ‘colocalized fraction (fc),’ plotted with image analysis-based s.d. of at least five replicates. The asterisk ‘*’ denotes fc that +SBP–eGFP and+SBP–mCherry are statistically equivalent (fc~0.7) by t-test and greater than +SBP–Venus cells. (c) Composite images show cell fluorescence (Column I) from the fluorescent protein (FP); labelled streptavidin using orthogonal filter sets (Column II); and an overlay of both (Column III). Arrows indicate representative cells with strong colocalization. Plotted in Column IV are the fluorescence mean grey values (y-axis) from a representative horizontal slice of the composite image (x-axis). Vertical bars displayed between Columns III and IV identify the position of each analysed slice. Arrows indicate peaks that match the highlighted cells in Column III. fc values are noted. Fluorophores with non-overlapping spectra were paired. Row 1, Venus expression (yellow-green) was paired with Dylight405-labelled SA (blue). Row 2, eGFP expression (green) was paired with Alexafluor594-labeled SA (red). Row 3, mCherry expression (red) was paired with Alexafluor488-labeled SA (green). Scale bar in lower left, 50μm.

That is, microscopy results related to the colocalization analysis are depicted for pairings of Venus and blue-streptavidin (SA), eGFP and red-SA, and mCherry and green-SA (Fig. 2c). Strong signals were observed in both filter sets (the fluorescent protein (Column I) and the labelled streptavidin (Column II)). Overlaying each image reveals colocalization, as indicated in Column III, where arrows point to examples of strong colocalization. In addition, Column IV plots fluorescence intensities across horizontal sections of the images, where cells that exhibit colocalized fluorescence are indicated by superimposed peaks. For +pSBP–Venus cells, those with both a blue and yellow signal are observed as pale blue–violet in the overlaid image. Cells with +pSBP–eGFP and +pSBP–mCherry and labelled streptavidin emit both green and red signals; their colocalization appears yellow. Controls shown in Supplementary Fig. 2, verify that fluorescent streptavidin (all colours) has specificity for only SBP-expressing cells over negative controls. Colocalization indicates that not only are both components of the fusion, SBP and the fluorescent protein, expressed, but that SBP is accessible to bind streptavidin on the cell’s surface. This is the first use of AIDAc for cell surface anchoring of fluorescent proteins, each having been functionalized with an affinity peptide.

Cell hybridization via mNPs

Given that expression of a fluorescent protein tagged with SBP enabled external binding of streptavidin, we employed this interaction for fastening streptavidin-functionalized materials directly to the cell surface. We chose streptavidin-conjugated mNPs, 100nm in diameter (an order of magnitude smaller than a cell), for binding to a cell surface (Fig. 3a) to impart the abiotic magnetic properties. Scanning electron microscopy (SEM) was used to observe surface interaction between cell surface-expressing SBP and streptavidin-functionalized mNPs. Supplementary Fig. 3a,bshows electron micrographs of E. coli cells (dimensions 1.5–2μm in length) and the mNPs (~100nm in diameter). The SEM image in Fig. 3b, shows a magnetically isolated SBP-expressing cell with streptavidin-mNPs. The sample was prepared by mixing SBP-expressing cells with streptavidin-mNPs, then collecting or ‘focusing’ into a magnetized pellet via magnetic field, then separating from unbound cells in the supernatant. The cells were then washed and resuspended. In Fig. 3b, clusters of surface-bound mNPs are observed. In addition, the elemental composition was analysed with energy-dispersive X-ray spectroscopy, shown in Fig. 3c by an element map superimposed with carbon (red) and iron (green). While the cell appears to be of a uniform carbon composition, the particles localized at the cell surface (highlighted with arrows) were found having a strong iron composition; thus, elemental analysis confirmed particle identity as iron oxide mNPs. Additional characterization of magnetic functionality, including detailed SEM and fluorescent microscopic analysis prior to and after application of magnetic fields, is described in theSupplementary Information (Supplementary Fig. 3).

Figure 3: Cells equipped with magnetic nanoparticles (mNPs) via streptavidin-mediated interaction with surface-expressed proteins.

Cells equipped with magnetic nanoparticles (mNPs) via streptavidin-mediated interaction with surface-expressed proteins.

(a) Cell surface binding of streptavidin-conjugated magnetic nanoparticles occurs via surface-anchored streptavidin-binding peptide (SBP). The fusion of T7-expressed SBP-fluorescent protein (FP)-AIDAc enables the cell surface accessibility. (b) Scanning electron micrograph of an E. coli cell with surface-bound particles. (c) Element map of carbon (red) and iron (green) through energy-dispersive spectroscopy.

In sum, the well-known affinity interaction between streptavidin and the peptide SBP is harnessed to endow cells with non-natural abiotic properties. Here coupling a functionalized nanomaterial to the surface-displayed peptide physically extends the fusion protein and also adds physical (magnetic) functionality to the cell.

Linking expression to AI-2 recognition

The expression system for pSBP–Venus was then put under AI-2 control so that the protein is expressed in the presence of AI-2 instead of IPTG. That is, we coupled the native QS signal transduction circuitry to the reporter cassette. To ensure ample expression (as the native operon is fairly weak), we placed expression of T7 RNA polymerase under control of the natural QS circuitry43. Phosphorylated AI-2 activates the system through derepression of the regulator LsrR, naturally upregulating AI-2 import and phosphorylation44, and, by design, the T7 RNA polymerase on a sensor plasmid43. When sbp–Venus is included downstream of a T7 promoter region on a second plasmid, expression is then triggered by AI-2 uptake (Supplementary Fig. 4a). Then, we used two host sensor strains engineered to provide varied AI-2 sensitivity (denoted responders ‘A’ and ‘B’). In ‘A’, lsrFG, genes required for internally phosphorylated AI-2 degradation45, 46 are deleted. Also, both strains lack the terminal AI-2 synthase, luxS, so they cannot produce AI-2 and, instead, must ‘receive’ AI-2 from an external source (Supplementary Fig. 4a). The phenotypic difference between A and B is the threshold level of AI-2 that activates the genetic response47, 48. Fully constructed, these cells are designed to take up and process AI-2 to generate fluorescence output (that co-functions with streptavidin binding).

We next evaluated the kinetics of surface-fusion protein expression and effects on cell growth. The AI-2-induced expression for AIDAc-linked and SBP-tagged fluorescent proteins did not alter growth kinetics for either cell type (Supplementary Fig. 4b,c). Expression efficacy was also evaluated via immunoassay of the outer membrane, probing for AI-2-induced surface display. After induction with 20μM AI-2, extracts from cell types A and B were size-separated and blotted using alkaline phosphatase-conjugated streptavidin to probe for the SBP-tagged protein fusion (Supplementary Fig. 5). The 88kDa AIDAc–Venus–SBP protein was only found in the membrane-containing pellet fraction (Fig. 4a). Analogously, protein orientation was assessed by immunolabeling the fluorescent protein. Cell type B transformed with pSBP–eGFP was induced with 20μM AI-2 overnight; cell surfaces were then probed for eGFP using a mouse anti-GFP primary antibody and red-labelled secondary anti-mouse IgG. Simultaneously, cells were observed using phase contrast and fluorescence confocal microscopy. We noted a punctate pattern for eGFP, which was in one-to-one correspondence with red immunostaining of the surface-expressed protein. The positive staining of eGFP-expressing cells for red fluorescence, contrasted by the absence of negative control immunostaining indicated surface exposure of the fusion (Supplementary Fig. 6). Confocal microscopy confirmed precise colocalization of the eGFP and red-labelled antibodies within the confines of individual cells (Fig. 4b). Therefore, efficient transport of this functionality to the membrane under AI-2 induction was demonstrated in each host.

Figure 4: Affinity-based probing for functional analysis of AI-2-induced protein expression.

Affinity-based probing for functional analysis of AI-2-induced protein expression.

(a) 64–82kDa region of western blot for pelleted (P) and supernatant (S) protein fractions isolated from Type A and B cells. Alkaline phosphatase-conjugated streptavidin was used to target AIDAc–Venus–SBP at expression timepoints. Arrows indicate the expected position of the full fusion protein. (b) Immunostaining for assessment of the fluorescent protein surface accessibility. The external surfaces of cells expressing AIDAc–eGFP–SBP were probed with an anti–eGFP and Alexafluor594-labelled antibody pair. A representative overlaid fluorescence and phase contrast image is shown along with fluorescence images of the green (G) and red (R) filters for the boxed-in region. Scale bar, 2μM.

Establishing molecular ranges for cell interrogation

Importantly, the engineered cells each provide a characteristic response to the level of AI-2. Recently, we showed that AI-2 level influences the quorum size of responding engineered populations but does not alter the expression level within each quorum47. Here we evaluated our engineered AI-2 responders, again for quorum size (or in other words, percentage of AI-2-responsive cells in the population), this time varying the compositions of molecular input and the configuration of responders (Fig. 5a). First, we added AI-2, synthesized in vitro, to each of the two responder populations (Fig. 5b). We also added conditioned medium (CM), the spent medium from an AI-2 producer culture containing metabolic byproducts, as well as AI-2 (refs 36, 49; Fig. 5c). We also mixed the responder populations and added AI-2 to gauge responses in complex cultures (Fig. 5d).

Figure 5: Single and multi-population cell responses to autoinducer-2.

Single and multi-population cell responses to autoinducer-2.

(a) Fluorescence output is linked to small molecule input, derived from purified or crude sources. Fluorescence from Responders A and B was analysed after exposure to autoinducer-2 (AI-2) in mono and mixed culture environments. (b) Venus expression from in vitro-synthesized AI-2 added to monocultures of A and B. (c) Venus expression from conditioned media (CM) added to monocultures of A and B. CM was isolated from WT W3110 E. coli cultures sampled at indicated OD. Data are averages from triplicate cultures with s.d. indicated. (d) Red and green fluorescence responses to AI-2 during co-incubation of Responders A (pSBP–mCherry+, red) and B (pSBP–eGFP+, green). Representative fluorescence images show colocalization of red and green cells. Scale bar, 10μm. The average cell count per responder cell is plotted against AI-2 concentration, as determined by image analysis in quadruplicate. All data are plotted as averages of at least triplicate samples with s.d.

Specifically, in Fig. 5b, A and B populations were incubated at mid-exponential phase with in vitro-synthesized AI-2 (refs 50, 51) at concentrations: 0, 2, 10, 28 and 75μM. After 12h, samples were observed for fluorescence by confocal microscopy and then quantified by fluorescence-activated cell sorting (FACS; Supplementary Fig. 4c). We found that SBP–Venus expression for responder A cells occurred at the lowest tested level (2μM AI-2), where 56% of the population expressed SBP–Venus and this fraction increased with AI-2 reaching a maximum of 90% at 28μM. For type B, a more gradual trend was found; only ~1% was fluorescent from 0-2μM, and this increased from 9 to 46% as AI-2 was increased to 28μM. Finally, the highest fraction of fluorescing cells was found at the highest concentration tested, 75μM.

We next isolated CM, which contains a dynamic composition of unfiltered metabolites and media components, from W3110 E. coli cultures at intervals during their exponential growth, throughout which AI-2 accumulates (AI-2 levels for the samples are indicated in Supplementary Fig. 7). CM aliquots were mixed with either A or B cells and cultured in triplicate for 12h. Through FACS analysis it was found, again, that a larger subpopulation of A expressed Venus compared with population B at any concentration (Fig. 5c). Statistically relevant expression from B was not apparent until incubated with CM from cultures at an optical density (OD) of 0.23. In all cases, population A recognized AI-2 presence, including from media isolated at a W3110 OD of 0.05, the minimum cell density tested in this study.

The sensitivities of both strains to AI-2-mediated induction corroborate previous literature10, 47. These trends demonstrate that strains engineered for altered sensitivity to molecular cues provide discrimination of concentration level. That is, the identical plasmid expression system was transformed into different hosts, providing robust and distinct levels of expression.

Having developed cell types A and B with differential ability to detect AI-2, we next altered the reporters so that each cell type expressed a unique SBP-fluorescence fusion for colour-coded designation. Cell type A was engineered with pSBP–mCherry and type B with pSBP–eGFP, resulting in red and green fluorescence, respectively. These populations were mixed together in equal proportion at mid-exponential phase, introduced to a range of AI-2 concentrations, and incubated overnight. Populations A and B exhibited equal growth rates when cultured alone and together (Supplementary Fig. 8c); it followed that the cocultures should comprise a 1:1 ratio of each constituent. Fluorescence output is shown by representative images in Fig. 5d. Also in Fig. 5d, the green and red cell count is plotted from a quadruplicate analysis for each input concentration.

Coculturing enables parallel processing as the molecule-rich environment is perceived by each cell, and is processed uniquely per cell type. Yet, since each sensing mechanism is a living and proliferating population, we tested whether the potentially altered dynamics of coculturing would permit the same sensitivities as isolated culturing. We evaluated the Monod-type saturation constant for each population independently and in cocultures. We found, in Fig. 5d, the general trends in response to an increasing AI-2 level were as predicted by modelled response curves (Supplementary Table 4), which were also well-correlated to Fig. 5b data (Supplementary Fig. 8a,b). That is, the saturation constants that describe dependence on AI-2 were unchanged when measured in cocultures. Phenomenologically, as expected, an initial accumulation of red type A responders was found. Then, at higher AI-2 levels, we found an emergence of a green subpopulation (type B). Above 28μM, there was no longer an apparent differential response that would otherwise enable discrimination of AI-2 concentration; based on the consistency with modelled behaviour, coculturing contributed to dampen the response as the maximum percentage of responding cells in cocultures is 50% instead of 100%. However, the overall fluorescence output is enriched by the combination of multiple populations since the ranges of sensitivity overlap and effectively expand that of the master population (Supplementary Fig. 8d). Specifically, because the fluorescence of B is described by a larger saturation constant, its fluorescence continually increases at higher AI-2 concentrations, while the fluorescence of A remains unchanged. Thus, coculturing between A and B enables resolvable output that is lower than the detection limit of B (due to A) yet surpasses the upper limit at which A saturates by the inclusion of B. The choice to fluorescently differentiate A and B was important because the output would otherwise be biased by extracellular components including the existence of non-sensing cells. Due to colour designation of A and B, a colour ‘pattern’ emerges as a feature of the parallel response, which we recognize is independent of the absolute fluorescence of the population.

Consensus feedback through multidimensional processing

We hypothesized that the value of cell-based sensing would be enhanced if the cell output could be collated in an unbiased manner that in turn were easily ‘read’ using optical means. We engaged magnetic processing, which represents an abiotic processing step that enhances the signal by focusing the collective response. Hence, cells were equipped with streptavidin-conjugated mNPs (Fig. 3). The ability of a magnetic field to refine fluorescence output through filtering and focusing is described in the Supplementary Information (Supplementary Fig. 11). Thus, in our combinatorial approach, fluorescence feedback about molecular information within a microbial community entails biotic processing through constituencies of two independent cell types in conjunction with magnetic post-processing that is enabled by guidance at the nanoscale (Fig. 6c). Moreover, since the fluorescence feedback data is provided through two constituencies, consensus from each independently provides an aggregate output; in our example, the output becomes relayed as a distinctive ‘binned’ category due to finite colour-combinations generated from constituencies A and B (Fig. 6c).

Figure 6: Binning molecular information through cell-based parallel processing and magnetically focusing fluorescence into collective consensus output.

Binning molecular information through cell-based parallel processing and magnetically focusing fluorescence into collective consensus output.

(a) A and B cell types were co-incubated with AI-2 levels ranging from 0 to 55μM AI-2 (left axis), then imaged after magnetic nanoparticle coupling and magnetic collation. Fluorescence results (centred directly over the magnet) are shown from high to low input (top left to bottom right). (b) Quantification of red and green fluorescence cell densities per AI-2 level. (c) The process of accessing molecular information begins by distributing Responders A and B within the environment of an AI-2 producer, P. A and B independently express fluorophore fusions and are linked with magnetic nanoparticles on processing autoinducer-2. Magnetic focusing translocates fluorescing responders. Image analysis of the magnetically collated cell aggregate reveals classified fluorescence output, representing the AI-2 composition of the interrogated environment. (d) Bright field (left) and fluorescence (right, red and green filters) images positioned over the edge of a magnet, as indicated by the inset. The sample in the bottom image pair was isolated from an environment of low AI-2 accumulation. The sample in the top image pair was isolated from a high AI-2 environment. (e) Quantification of visual space occupied by collated cells (eGFP and mCherry expressers) while distributed (- magnet) and magnetically focused (+). Scale bars, 50μm.

Again, type A transmits red output (SBP–mCherry+) and type B transmits green (SBP–eGFP+). These were first co-incubated with titred concentrations of AI-2, to obtain results similar to those ofFig. 5d. By coupling mNPs to the responsive parallel populations, we tested for aggregate two-colour output to provide informative feedback within a set of outcomes ranging from no colour, red-only to red+green. After overnight co-incubation and a magnetic sweep with streptavidin-mNPs, fluorescence results are shown in Fig. 6a, where the recovered cells are displayed above a magnet’s center in order from highest to lowest AI-2 level (top left to bottom right). The processing output generated by the range of conditions was quantitatively assessed for contributions from A and B responders. The spatial density of each fluorophore, or the area occupied by fluorescent responders as a percentage of total visible area, was quantified and plotted in Fig. 6b. Here the trend of increasing fluorescence with AI-2 is followed by both A and B cell types; however, red A cells accumulate at a higher rate than green B cells. This relationship between A and B processing is not only consistent with their previous characterizations (Fig. 5) but indicates that the aggregate output is unbiased regardless of assembly with mNPs and magnetic-stimulated redistribution (Supplementary Information, Supplementary Fig. 14a).

Next, A and B cells were added together to probe the QS environment of Listeria innocua, an AI-2-producing cell type that is genetically and ecologically similar to the pathogenic strain L. monocytogenes52. The environment was biased towards low and high cell density conditions by altering nutrient levels to develop contrasting scenarios of AI-2 level. Preliminary characterization in the Supplementary Information indicated that L. innocua proliferation is unperturbed by the presence of E. coli responders (Supplementary Fig. 12) and that type A cells detect AI-2 at lowListeria densities limited by sparse nutrients; then with rich nutrient availability, cell proliferation permits a higher AI-2 level that can be detected by type B (Supplementary Fig. 13). Replicating these conditions, we expected red fluorescence to be observed at low culture density and for green fluorescence to be reported when high (Fig. 6c). Two conditions were tested: L. innocua was proportioned to responder cells at 20:1 in dilute media to establish a low culture density condition or, alternatively, a ratio of 200:1 in rich media for a high culture density condition. After overnight co-incubation and a magnetic sweep (applied directly to the triple strain cultures) with streptavidin-mNPs, the recovered cells are displayed above a magnet’s edge (shown in Fig. 6d). Acute focusing of the fluorescence signals, contributed by each subset population of the processor (A and B), is visually apparent. The magnetic field had a physical effect of repositioning the ‘on’ subsets to be tightly confined within the magnetic field.

The processing output generated by the contrasting culture conditions was again assessed for the respective contributions of A and B, and for changes in spatial signal density due to the magnetic sweep (Fig. 6e). The analysis was based on images provided in Supplementary Fig. 14b. Data inFig. 6e indicate that red type A cells are prevalent regardless of culture condition (except negative controls). However, compared with the low AI-2 condition, the abundance of green cells is 100-fold higher in the high AI-2 condition. In addition, the ratio of green to red was consistent prior to and after magnetic concentration, substantiating observations in the distributed system. Further, data show that magnetic refining increased per-area fluorescence 100-fold or 10-fold in low and high cell culture studies, respectively.

Based on the thresholds established for responder populations A and B, we found colour-coded binning corresponded to AI-2 level, where ‘red-only’ represented less AI-2 than ‘red+green’ (Fig. 5d). Thus, we found a binned output was established via this multidimensional molecular information-processing system and that this matched the expectations. Red feedback (from responder A) indicated dilute AI-2 accumulation occurred in the low density culture. In the dense cultures, high AI-2 accumulation turned on both A and B for combined red and green feedback.

System response patterns defined by parallel populations

Our example demonstrates the concept of an amorphous processing system that utilizes several biotic and abiotic components for multidimensional information processing. Interestingly, a binning effect was enabled: our system yields an index of colour-categorized feedback that characterizes the sampled environment. In Fig. 7, we present a means to extend our approach to multidimensional systems, those with more than one molecule-of-interest and at different concentrations. That is, by appropriate design of the cell responders, we can further enrich the methodology, its depth and breadth of applicability. We depict 10 hypothetical pairs of responses (with defining equations located in Supplementary Table 5)—those that can be driven by appropriately engineering cells to portend altered genetic responses. For example, rows 1 and 3 provide genetic outcomes as a function of analyte (AI-2) concentration. The hypothetical depictions are feasible as ‘designer’ signal transduction and marker expression processes enabled by synthetic biology21, 53, 54. Rows 2 and 4 demonstrate the corresponding visual planes, where red cell numbers (x-axis) are plotted against green (y-axis), illustrated by the first example. If one divides the two-dimensional space into quadrants (no colour, majority red, majority green, and equivalent ratios of red and green), it becomes apparent that the relationship between cell types influences the ‘visual’ or optical output. Thus, the 10 arbitrary response sets yield a variety of pairings that can provide unique visual patterns for categorizing molecular information. We have simplified the analysis by placing dot marker symbols at the various coincident datapoints, revealing visual patterns. In this way, the ability to incorporate unique responses to a multitude of molecular cues, all within a single pair of cells, or through further multiplexing with additional cell populations becomes apparent.

Figure 7: Extension of nano-guided cell networks for hypothetical regulatory structures.

Extension of nano-guided cell networks for hypothetical regulatory structures.

(a) Rows 1 and 3 depict 10 hypothetical genetic responses to molecular inputs for pairs of fluorescence-reporting cell populations (red, R and green, G). Rows 2 and 4 depict genetic responses as phase-plane plots yielding distinct patterns. This establishes a visual field, showing the extent of any population–population bias (illustrated in example case 1). (b) Left panel: a two-population pairing (shown in case 10) defines visual output that inherently bins into three quadrants: Q1, negligible colour; Q2, red bias due to majority red cell output; and Q4, combined red and green output. Right panel: data from Figs 5d and 6bare plotted analogously, where each data point represents an autoinducer-2 input (labelled, μM). As expected, red and green outputs were binned into Q1, Q2 and Q4 as indicated by coloured outlines.

Our AI-2-conveying cell network is similar to example 7 in Fig. 7a and the AI-2 response curves inFig. 5 (characterized by Supplementary Table 4 equations). Example 7 establishes output into three basic quadrants, including Q1 (negligible colour), Q2 (majority red) and Q4 (roughly equal red and green) (Fig. 7b). We recast the data from Figs 5d and 6b as a phase-plane portrait in Fig. 7c. This reveals the mechanisms by which the output is binned and how the originating cell response curves lead to this pattern, which in turn, was unchanged due to magnetic refinement. InSupplementary Fig. 15, we demonstrate a parameterization of the red and green response curves that suggest the methodology is robust, that when cells are appropriately engineered one could ‘tune’ system characteristics to enhance or diminish a binning effect. We suggest that the utility of subcellular genetic tuning extends well beyond per-cell performance. Rather, we suggest such a strategy may be used to guide the dynamics of population architecture for actuation of by-design response patterns at a systems level.


While cell-based sensors work well in well-defined assay conditions, extension to complex environments remains a challenge. They grow, they move, they perturb their environs, they report in a time and concentration-dependent manner, small numbers of sensor cells may require signal amplification and so on. Also, increasingly, bacterial cells are engineered for user specified ‘executive’ functions in complex environments55, 56, 57. Their performance depends on their ability to filter out extraneous noise while surveying the molecular landscape, and providing informed actuation.

Our system interrogates the molecular space by focusing on bacterial QS and a widely distributed signal molecule, AI-2. In addition to genetic attributes of the AI-2-responding sensor cells, AI-2 is a chemoattractant for E. coli, and hence E. coli engineered to sense and respond to AI-2 will naturally move towards its sources, enabling full sampling of the prevailing state10, 37. Each strain evaluates AI-2 with a distinct sensitivity. When ‘activated’ in response to a characteristic level, the cells simultaneously expressed a fluorescent marker and a SBP on the outer membrane via AIDAc translocation. SBP provides a means for cell hybridization through its strong affinity to streptavidin, and here, aids in binding mNPs. This enables the non-genetically coded property of cell translocation within a magnetic field through physically stimulated focusing and binning.

By making use of a diversity of biotic and abiotic features, our multidimensional system of ‘responder’ populations exemplifies several key metrics that promote executive performance in such environments: active molecule capture, post-capture refining of the detection output and finally the utilization of multiple feedback thresholds58, 59, 60. Here cells facilitate AI-2 recognition autonomously and actively because, as a distributed network they reside planktonically, chemotaxing to and continually processing signals over time. When AI-2 is detected, a processor cell’s cognate machinery responds by upregulation of the native QS operon, leading to rapid signal uptake and thereby creating an active-capture signal-processing mechanism. To maximize information acquisition and account for a potentially heterogeneous molecular landscape, cells serve as molecular sampling units among a distributed population, which leads to data fed back as a consensus of fluorescent ‘datapoints’. Then, distributed data collection can be selectively reversed via the incorporated abiotic feature: mNPs, fastened externally on the cell through affinity-guided self-assembly. As such, responding cells obtained this extendable feature, thereby becomes sensitized to repositioning within a magnetic field.

The layered nature of the processor here, from the subcellular to multicellular scale, permits a series of selective steps: it commences with the AI-2-triggered expression cascade which releases a tight repressor, surface localization of both the fluorescent protein and SBP tag, and finally nanoparticle binding for recovery. In addition, multiple layers of amplification result in orthogonal fluorescence feedback. The AI-2 detection event leads to whole-cell fluorescence through expression of many protein copies47. Then their physical collection further amplifies the signal, yielding a macroscopic composite of many individual cell units. When utilized as a network of multiple constituencies, responder cell types A and B contribute individual recognition results (off, red or green) to a single consensus output. Finally, due to their overlapping thresholds for recognition of the same molecule, in this case, AI-2, parallel processing by A and B responders can contribute to visual interpretation of information about the molecule. Outcomes are classified into a finite number of states: here output to no fluorescence, red, or red and green, with each addition of colour as a metric of a higher interval of AI-2. In many respects, the elucidation of layered information networks as demonstrated here is analogous to computer information processing via information theory61, 62, 63.

Here, however, interrogation of biological systems requires a reliable means for accessing molecular information—that which is communicated between biological species and that which can be relayed to the end user. The responder cells need not be present in high concentration, nor must they all be collected in the present format. We suggest that engineered biological mechanisms are well-poised to serve at this critical interface between information acquisition and user interaction. Thus, the functional design of components for autonomous self-assembly, decision-making and networking is requisite in the field of micro- and nano-scaled machines. Our combinatorial approach allows for cells to independently assess, yet collectively report, on molecular information. Its processing is enabled through appropriate integration of synthetic biology and nanomaterials design. We suggest this approach provides a rich opportunity to direct many formats of multi-population response through genetic tuning and systems-level engineering. Further development of cellular networks and incorporation of alternate abiotic attributes can expand the depth and breadth of molecular communication for user specified actuation.

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Reporter: Stephen J. Williams, Ph.D.


The three-day symposium aims to bring oncologists and statisticians together to share new research, discuss novel ideas, ask questions and provide solutions for cancer clinical trials. In the era of big data, precision medicine, and genomics and immune-based oncology, it is crucial to provide a platform for interdisciplinary dialogues among clinical and quantitative scientists. The Stat4Onc Annual Symposium serves as a venue for oncologists and statisticians to communicate their views on trial design and conduct, drug development, and translations to patient care. To be discussed includes big data and genomics for oncology clinical trials, novel dose-finding designs, drug combinations, immune oncology clinical trials, and umbrella/basket oncology trials. An important aspect of Stat4Onc is the participation of researchers across academia, industry, and regulatory agency.

Meeting Agenda will be announced coming soon. For Updated Agenda and Program Speakers please CLICK HERE

The registration of the symposium is via NESS Society PayPal. Click here to register.

Other  2019 Conference Announcement Posts on this Open Access Journal Include:

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Blast crisis in myeloid leukemia and the activation of a microRNA-editing enzyme called ADAR1

Curator: Larry H. Bernstein, MD, FCAP


Fix to RNA-Editing Glitch May Defuse Blast Crisis

GEN News Highlights Jun 10, 2016   http://www.genengnews.com/gen-news-highlights/fix-to-rna-editing-glitch-may-defuse-blast-crisis/81252818/

The self-renewal of leukemia stem cells depends on the activation of a microRNA-editing enzyme called ADAR1. According to a new study, ADAR1 activation represents a unique therapeutic vulnerability in leukemia stem cells, which can give rise to blast crisis in chronic myeloid leukemia.     http://www.genengnews.com/Media/images/GENHighlight/thumb_Jun10_2016_ChronicMyeloidLeukemiaBloodCells6222419925.jpg

Few cancer mechanisms are as devastating as the generation of cancer stem cells, which arise in leukemia from white blood cell precursors. The mechanisms of this transition have been obscure, but the consequences are all too clear. Leukemia stem cells promote an aggressive, therapy-resistant form of disease called blast crisis.

Delving into the mechanisms by which leukemia stem cells are primed, a team of scientists at the University of California, San Diego (UCSD), uncovered a misfiring RNA-editing system. The main problem the scientists found was an enzyme called ADAR1 (adenosine deaminase acting on RNA1), which mediates post-transcriptional adenosine-to-inosine (A-to-I) RNA editing.

ADAR1 can edit the sequence of microRNAs (miRNAs), small pieces of genetic material. By swapping out just one miRNA building block for another, ADAR1 alters the carefully orchestrated system cells use to control which genes are turned on or off at which times.

ADAR1 is known to promote cancer progression and resistance to therapy. To study ADAR1, the UCSD team used human blast crisis chronic myeloid leukemia (CML) cells in the lab, and mice transplanted with these cells, to determine the enzyme’s role in governing leukemia stem cells.

The scientists, led by Catriona Jamieson, M.D., Ph.D., published their work June 9 in Cell Stem Cell, in an article entitled, “ADAR1 Activation Drives Leukemia Stem Cell Self-Renewal by Impairing Let-7 Biogenesis.” The article presented the first mechanistic link between pro-cancer inflammatory signals and RNA editing–driven reprogramming of precursor cells into leukemia stem cells.

The article describes how ADAR1-mediated A-to-I RNA editing is activated by Janus kinase 2 (JAK2) signaling and BCR-ABL1 signaling. Also, it indicated, in a model of blast crisis (BC) CML, that combined JAK2 and BCR-ABL1 inhibition prevents leukemia stem cell self-renewal commensurate with ADAR1 downregulation.

Essentially, the scientists were able to trace a series of molecular events: First, white blood cells with a leukemia-promoting gene mutation become more sensitive to signs of inflammation. That inflammatory response activates ADAR1. Then, hyper-ADAR1 editing slows down the miRNAs known as let-7. Ultimately, this activity increases cellular regeneration, or self-renewal, turning white blood cell precursors into leukemia stem cells.

“Lentiviral ADAR1 wild-type, but not an editing-defective ADAR1E912A mutant, induces self-renewal gene expression and impairs biogenesis of stem cell regulatory let-7 microRNAs,” wrote the author of the Cell Stem Cell article. “Combined RNA sequencing, qRT-PCR, CLIP-ADAR1, and pri-let-7 mutagenesis data suggest that ADAR1 promotes LSC generation via let-7 pri-microRNA editing andLIN28B upregulation.”

After learning how the ADAR1 system works, Dr. Jamieson’s team looked for a way to stop it. By inhibiting sensitivity to inflammation or inhibiting ADAR1 with a small-molecule tool compound, the researchers were able to counter ADAR1’s effect on leukemia stem cell self-renewal and restore let-7. Self-renewal of blast crisis CML cells was reduced by approximately 40% when treated with the small molecule called 8-Aza as compared to untreated cells.

“A small-molecule tool compound antagonizes ADAR1’s effect on LSC self-renewal in stromal co-cultures and restores let-7 biogenesis,” the study’s authors noted. “Thus, ADAR1 activation represents a unique therapeutic vulnerability in LSCs with active JAK2 signaling.”

“In this study, we showed that cancer stem cells co-opt a RNA editing system to clone themselves. What’s more, we found a method to dial it down,” said Dr. Catriona Jamieson. “Based on this research, we believe that detecting ADAR1 activity will be important for predicting cancer progression.

“In addition, inhibiting this enzyme represents a unique therapeutic vulnerability in cancer stem cells with active inflammatory signaling that may respond to pharmacologic inhibitors of inflammation sensitivity or selective ADAR1 inhibitors that are currently being developed.”


ADAR1 Activation Drives Leukemia Stem Cell Self-Renewal by Impairing Let-7 Biogenesis

Maria Anna Zipeto, Angela C. Court, Anil Sadarangani, Nathaniel P. Delos Santos, Larisa Balaian, Hye-Jung Chun, Gabriel Pineda, Sheldon R. Morris, Cayla N. Mason, Ifat Geron, Christian Barrett, Daniel J. Goff, Russell Wall, Maurizio Pellecchia, Mark Minden, Kelly A. Frazer, Marco A. Marra, Leslie A. Crews, Qingfei Jiang, Catriona H.M. Jamieson
Published online: June 9, 2016
  • JAK2 signaling activates ADAR1-mediated A-to-I RNA editing
  • JAK2 and BCR-ABL1 signaling converge on ADAR1 activation through STAT5a
  • ADAR1-mediated microRNA editing impairs let-7 biogenesis and enhances LSC self-renewal
  • JAK2 and BCR-ABL1 inhibition reduces ADAR1 expression and prevents LSC self-renewal

Post-transcriptional adenosine-to-inosine RNA editing mediated by adenosine deaminase acting on RNA1 (ADAR1) promotes cancer progression and therapeutic resistance. However, ADAR1 editase-dependent mechanisms governing leukemia stem cell (LSC) generation have not been elucidated. In blast crisis chronic myeloid leukemia (BC CML), we show that increased JAK2 signaling and BCR-ABL1 amplification activate ADAR1. In a humanized BC CML mouse model, combined JAK2 and BCR-ABL1 inhibition prevents LSC self-renewal commensurate with ADAR1 downregulation. Lentiviral ADAR1 wild-type, but not an editing-defective ADAR1E912A mutant, induces self-renewal gene expression and impairs biogenesis of stem cell regulatory let-7 microRNAs. Combined RNA sequencing, qRT-PCR, CLIP-ADAR1, and pri-let-7 mutagenesis data suggest that ADAR1 promotes LSC generation via let-7 pri-microRNA editing and LIN28Bupregulation. A small-molecule tool compound antagonizes ADAR1’s effect on LSC self-renewal in stromal co-cultures and restores let-7 biogenesis. Thus, ADAR1 activation represents a unique therapeutic vulnerability in LSCs with active JAK2 signaling.


Figure thumbnail fx1




4014 Inflammatory Cytokine-Responsive ADAR1 Impairs Let-7 Biogenesis and Promotes Leukemia Stem Cell Generation

Chronic Myeloid Leukemia: Biology and Pathophysiology, excluding Therapy
Program: Oral and Poster Abstracts
Session: 631. Chronic Myeloid Leukemia: Biology and Pathophysiology, excluding Therapy: Poster III
Monday, December 7, 2015, 6:00 PM-8:00 PM
Hall A, Level 2 (Orange County Convention Center)

Maria Anna Zipeto, Ph.D1*, Angela Court Recart2*, Nathaniel Delos Santos3*, Qingfei Jiang, PhD4*, Leslie A Crews, PhD3* and Catriona HM Jamieson, MD, PhD3

1Sanford Consortium for Regenerative Medicine, University of California San Diego, La Jolla, CA
2University of California San Diego, LA JOLLA, CA
3Division of Regenerative Medicine, University of California, San Diego, La Jolla, CA
4University of California San Diego, La Jolla, CA

BackgroundIn advanced human malignancies, RNA sequencing (RNA-seq) has uncovered deregulation of adenosine deaminase acting on RNA (ADAR) editases that promote therapeutic resistance and leukemia stem cell (LSC) generation. Chronic myeloid leukemia (CML), an important paradigm for understanding LSC evolution, is initiated by BCR-ABL1 oncogene expression in hematopoietic stem cells (HSCs) but undergoes blast crisis (BC) transformation following aberrant self-renewal acquisition by myeloid progenitors harboring cytokine-responsive ADAR1 p150 overexpression. Emerging evidence suggests that adenosine to inosine editing at the level of primary (pri) or precursor (pre)-microRNA (miRNA), alters miRNA biogenesis and impairs biogenesis. However, relatively little is known about the role of inflammatory niche-driven ADAR1 miRNA editing in malignant reprogramming of progenitors into self-renewing LSCs.


Primary normal and CML progenitors were FACS-purified and RNA-Seq analysis as well as qRT-PCR validation were performed according to published methods (Jiang, 2013). MiRNAs were extracted from purified CD34+ cells derived from CP, BC CML and cord blood by RNeasy microKit (QIAGEN) and let-7 expression was evaluated by qRT-PCR using miScript Primer assay (QIAGEN). CD34+ cord blood (n=3) were transduced with lentiviral human JAK2, let-7a, wt-ADAR1 and mutant ADAR1, which lacks a functional deaminase domain.  Because STAT signaling triggers ADAR1 transcriptional activation and both BCR-ABL1 and JAK2 activate STAT5a, nanoproteomics analysis of STAT5a levels was performed.  Engrafted immunocompromised RAG2-/-γc-/- mice were treated with a JAK2 inhibitor, SAR302503, alone or in combination with a potent BCR-ABL1 TKI Dasatinib, for two weeks followed by FACS analysis of human progenitor engraftment in hematopoietic tissues and serial transplantation.


RNA-seq and qRT-PCR analysis in FACS purified BC CML progenitors revealed an over-representation of inflammatory pathway activation and higher levels of JAK2-dependent inflammatory cytokine receptors, when compared to normal and chronic phase (CP) progenitors. Moreover, RNA-seq and qRT-PCR analysis showed decreased levels of mature let-7 family of stem cell regulatory miRNA in BC compared to normal and CP progenitors. Lentiviral human JAK2 transduction of CD34+ progenitors led to an increase of ADAR1 transcript levels and to a reduction in let-7 family members. Interestingly, lentiviral human JAK2 transduction of normal progenitors enhanced ADAR1 activity, as revealed by RNA editing-specific qRT-PCR and RNA-seq analysis. Moreover, qRT-PCR analysis of CD34+ progenitors transduced with wt-ADAR1, but not mutant ADAR1 lacking functional deaminase activity, reduced let-7 miRNA levels. These data suggested that ADAR1 impairs let-7 family biogenesis in a RNA editing dependent manner. Interestingly, RNA-seq analysis confirmed higher frequency of A-to-I editing events in pri- and pre-let-7 family members in CD34+ BC compared to CP progenitors, as well as normal progenitors transduced with human JAK2 and ADAR1-wt, but not mutant ADAR1. Lentiviral ADAR1 overexpression enhanced CP CML progenitor self-renewal and decreased levels of some members of the let-7 family. In contrast, lentiviral transduction of human let-7a significantly reduced self-renewal of progenitors. In vivo treatments with Dasatinib in combination with a JAK2 inhibitor, significantly reduced self-renewal of BCR-ABL1 expressing BC progenitors in the bone marrow thereby prolonging survival of serially transplanted mice. Finally, a reduction in ADAR1 p150 transcripts was also noted following combination treatment only suggesting a role for ADAR1 in CSC propagation.


This is the first demonstration that intrinsic BCR-ABL oncogenic signaling and extrinsic cytokines signaling through JAK2 converge on activation of ADAR1 that drives LSC generation by impairing let-7 miRNA biogenesis. Targeted reversal of ADAR1-mediated miRNA editing may enhance eradication of inflammatory niche resident cancer stem cells in a broad array of malignancies, including JAK2-driven myeloproliferative neoplasms.

Disclosures: Jamieson: J&J: Research Funding ; GSK: Research Funding .

Interferon Receptor Signaling in Malignancy: a Network of Cellular Pathways Defining Biological Outcomes

Interferons (IFNs) are cytokines with important anti-proliferative activity and exhibit key roles in immune surveillance against malignancies. Early work initiated over 3 decades ago led to the discovery of IFN receptor activated Jak-Stat pathways and provided important insights into mechanisms for transcriptional activation of interferon stimulated genes (ISGs) that mediate IFN-biological responses. Since then, additional evidence has established critical roles for other receptor activated signaling pathways in the induction of IFN-activities. These include MAPK pathways, mTOR cascades and PKC pathways. In addition, specific microRNAs (miRNAs) appear to play a significant role in the regulation of IFN-signaling responses. This review focuses on the emerging evidence for a model in which IFNs share signaling elements and pathways with growth factors and tumorigenic signals, but engage them in a distinctive manner to mediate anti-proliferative and antiviral responses.

Because of their antineoplastic, antiviral, and immunomodulatory properties, recombinant interferons (IFNs) have been used extensively in the treatment of various diseases in humans (1). IFNs have clinical activity against several malignancies and are actively used in the treatment of solid tumors such as malignant melanoma and renal cell carcinoma; and hematological malignancies, such as myeloproliferative neoplasms (MPNs) (1). In addition, IFNs play prominent roles in the treatment of viral syndromes, such as hepatitis B and C (2). In contrast to their beneficial therapeutic properties, IFNs have been also implicated in the pathophysiology of certain diseases in humans. In many cases this involvement reflects abnormal activation of the endogenous IFN system, which has important roles in various physiological processes. Diseases in which dysregulation of the Type I IFN system has been implicated as a pathogenetic mechanism include autoimmune disorders such as systemic lupus erythematosous (3), Sjogren’s syndrome (3,4), dermatomyositis (5) and systemic sclerosis (3, 4). In addition, Type II IFN (IFNγ) overproduction has been implicated in bone marrow failure syndromes, such as aplastic anemia (6). There is also recent evidence for opposing actions of distinct IFN subtypes in the pathophysiology of certain diseases. For instance, a recent study demonstrated that there is an inverse association between IFNβ and IFNγ gene expression in human leprosy, consistent with opposing functions between Type I and II IFNs in the pathophysiology of this disease (7). Thus, differential targeting of components of the IFN-system, to either promote or block induction of IFN-responses depending on the disease context, may be useful in the therapeutic management of various human illnesses. The emerging evidence for the complex regulation of the IFN-system underscores the need for a detailed understanding of the mechanisms of IFN-signaling in order to target IFN-responses effectively and selectively.

It took over 35 years from the original discovery of IFNs in 1957 to the discovery of Jak-Stat pathways (8). The identification of the functions of Jaks and Stats dramatically advanced our understanding of the mechanisms of IFN-signaling and had a broad impact on the cytokine research field as a whole, as it led to the identification of similar pathways from other cytokine receptors (8). Subsequently, several other IFN receptor (IFNR)-regulated pathways were identified (9). As discussed below, in recent years there has been accumulating evidence that beyond Stats, non-Stat pathways play important and essential roles in IFN-signaling. This has led to an evolution of our understanding of the complexity associated with IFN receptor activation and how interacting signaling networks determine the relevant IFN response.

Interferons and their functions

The interferons are classified in 3 major categories, Type I (α, β, ω, ε, τ, κ, ν); Type II (γ) and Type III IFNs (λ1, λ2, λ3) (1, 9, 10). The largest IFN-gene family is the group of Type I IFNs. This family includes 14 IFNα genes, one of which is a pseudogene, resulting in the expression of 13 IFNα protein subtypes (1, 9). There are 3 distinct IFNRs that are specific for the 3 different IFN types. All Type I IFN subtypes bind to and activate the Type I IFNR, while Type II and III IFNs bind to and activate the Type II and III IFNRs, respectively (911). It should be noted that although all the different Type I IFNs bind to and activate the Type I IFNR, differences in binding to the receptor may account for specific responses and biological effects (9). For instance, a recent study provided evidence that direct binding of mouse IFNβ to the Ifnar1 subunit, in the absence of Ifnar2, regulates engagement of signals that control expression of genes specifically induced by IFNβ, but not IFNα (12). This recent discovery followed original observations from the 90s that revealed differential interactions between the different subunits of the Type I IFN receptor in response to IFNβ binding as compared to IFNα binding and partially explained observed differences in functional responses between different Type I IFNs (9).

A common property of all IFNs, independently of type and subtype, is the induction of antiviral effects in vitro and in vivo (1). Because of their potent antiviral properties, IFNs constitute an important element of the immune defense against viral infections. There is emerging information indicating that specificity of the antiviral response is cell type dependent and/or reflects specific tissue expression of certain IFNs. As an example, a recent comparative analysis of the involvement of the Type I IFN system as compared to the Type III IFN system in antiviral protection against rotavirus infection of intestinal epithelial cells demonstrated an almost exclusive requirement for IFNλ (Type III IFN) (13). The antiviral effects of IFNα have led to the introduction of this cytokine in the treatment of hepatitis C and B in humans (2) and different viral genotypes have been associated with response or failure to IFN-therapy (14).

Most importantly, IFNs exhibit important antineoplastic effects, reflecting both direct antiproliferative responses mediated by IFNRs expressed on malignant cells, as well as indirect immunomodulatory effects (15). IFNα and its pegylated form (peg IFNα) have been widely used in the treatment of several neoplastic diseases, such as hairy cell leukemia (HCL), chronic myeloid leukemia (CML), cutaneous T cell lymphoma (CTCL), renal cell carcinoma (RCC), malignant melanoma, and myeloproliferative neoplasms (MPNs) (1, 16). Although the emergence of new targeted therapies and more effective agents have minimized the use of IFNs in the treatment of diseases like HCL and CML, IFNs are still used extensively in the treatment of melanoma, CTCL and MPNs (1, 16, 17). Notably, recent studies have provided evidence for long lasting molecular responses in patients with polycythemia vera (PV), essential thrombocytosis (ET) and myelofibrosis (MF) who were treated with IFNα (16). Beyond their inhibitory properties on malignant hematopoietic progenitors, IFNs are potent regulators of normal hematopoiesis (9) and contribute to the regulation of normal homeostasis in the human bone marrow (18). Related to its effects in the central nervous system, IFNβ has clinical activity in multiple sclerosis (MS) and has been used extensively for the treatment of patients with MS (19). The immunoregulatory properties of Type I IFNs include key roles in the control of innate and adaptive immune responses, as well as positive and negative effects on the activation of the inflammasome (15). Dysregulation of the Type I IFN response is seen in certain autoimmune diseases, such as Aicardi-Goutières syndrome (20). In fact, self-amplifying Type I IFN-production is a key pathophysiological mechanism in autoimmune syndromes (21). There is also emerging evidence that IFNλ may contribute to the IFN signature in autoimmune diseases (3).

Jak-Stat pathways

Jak kinases and DNA binding Stat-complexes

Tyrosine kinases of the Janus family (Jaks) are associated in unique combinations with different IFNRs and their functions are essential for IFN-inducible biological responses. Stats are transcriptional activators whose activation depends on tyrosine phosphorylation by Jaks (8, 9). In the case of the Type I IFN receptor, Tyk2 and Jak1 are constitutively associated with the IFNAR1 and IFNAR2 subunits, respectively (8, 9) (Fig. 1). For the Type II IFN receptor, Jak1 and Jak2 are associated with the IFNGR1 and IFNGR2 receptor subunits, respectively (8, 9) (Fig. 1). Finally, in the case of the Type III IFNR, Jak1 and Tyk2 are constitutively associated with the IFN-λR1 and IL-10R2 receptor chains, respectively (10) (Fig. 1). Upon engagement of the different IFNRs by the corresponding ligands, the kinase domains of the associated Jaks are activated and phosphorylate tyrosine residues in the intracellular domains of the receptor subunits that serve as recruitmenst sites for specific Stat proteins. Subsequently, the Jaks phosphorylate Stat proteins that form unique complexes and translocate to the nucleus where they bind to specific sequences in the promoters of ISGs to initiate transcription. A major Stat complex in IFN-signaling is the interferon stimulated gene factor 3 (ISGF3) complex. This IFN-inducible complex is composed or Stat1, Stat2 and IRF9 and regulates transcription by binding to IFN stimulated response elements (ISRE) in the promoters of a large group of IFN stimulated genes (ISGs) (8, 9). ISGF3 complexes are induced during engagement of the Type I and III IFN receptors, but not in response to activation of Type II IFN receptors (810) (Table 1). Beyond ISGF3, several other Stat-complexes involving different Stat homodimers or heterodimers are activated by IFNs and bind to IFNγ-activated (GAS) sequences in the promoters of groups of ISGs (8, 9). Such GAS binding complexes are induced by all different IFNs (I, II and III), although there is variability in the engagement and utilization of different Stats by the different IFN-receptors (Table 1). It should also be noted that engagement of certain Stats, such as Stat4 and Stat6, is cell type-specific and may be relevant for tissue specific functions (9). The significance of different Stat binding complexes in the induction of Type I and II IFN responses was in part addressed in a study in which Stat1 cooperative DNA binding was disrupted by generating knock-in mice expressing cooperativity-deficient STAT1 (22). As expected, Type II IFN-induced gene transcription and antibacterial responses were essentially lost in these mice, but Type I IFN-dependent recruitment of Stat1 to ISRE elements and antiviral responses were not affected (22), demonstrating the existence of important differences in Stat1 cooperative DNA binding between Type I and II IFN signaling.

Type I, II, III interferon receptors subunits, associated kinases of the Janus family, and effector Stat-pathways. Note: Stat:Stat reflects multiple potential Stat:Stat compexes, as outlined in Table 2.

Table 1

Different Stat-DNA binding complexes induced by Type I, II and III IFNs.

Serine phosphorylation of Stats

The nuclear translocation of Stat-proteins occurs after their activation, following phosphorylation on specific sites by Jak kinases (8, 9). It is well established that phosphorylation on tyrosine 701 is required for activation of Stat1 and phosphorylation on tyrosine 705 is required for activation of Stat3 (8, 9). Beyond tyrosine phosphorylation, phosphorylation on serine 727 in the Stat1 and Stat3 transactivation domains is required for full and optimal transcriptional activation of ISGs (8, 9). There is evidence that serine phosphorylation occurs after the phosphorylation of Stat1 on tyrosine 701 and that translocation to the nucleus and recruitment to the chromatin are essential in order for Stat1 to undergo serine 727 phosphorylation (23). Several IFN-dependent serine kinases for Stat1 have been described, raising the possibility that this phosphorylation occurs in a cell type specific manner. After the original demonstration that protein kinase C (PKC) delta (PKCδ) is a serine kinase for Stat1 and is required for optimal transcriptional activation in response to IFNα (24), extensive work has confirmed the role of this PKC isoform in the regulation of serine 727 phosphorylation in Stat1 and has been extended to different cellular systems (2529) (Table 2). In the Type II IFN system five different serine kinases for the transactivation domain (TAD) of Stat1/phosphorylation on serine 727 have been demonstrated in different cell systems.  …..

Serine phosphorylation of Stats

The nuclear translocation of Stat-proteins occurs after their activation, following phosphorylation on specific sites by Jak kinases (8, 9). It is well established that phosphorylation on tyrosine 701 is required for activation of Stat1 and phosphorylation on tyrosine 705 is required for activation of Stat3 (8, 9). Beyond tyrosine phosphorylation, phosphorylation on serine 727 in the Stat1 and Stat3 transactivation domains is required for full and optimal transcriptional activation of ISGs (8, 9). There is evidence that serine phosphorylation occurs after the phosphorylation of Stat1 on tyrosine 701 and that translocation to the nucleus and recruitment to the chromatin are essential in order for Stat1 to undergo serine 727 phosphorylation (23). Several IFN-dependent serine kinases for Stat1 have been described, raising the possibility that this phosphorylation occurs in a cell type specific manner. After the original demonstration that protein kinase C (PKC) delta (PKCδ) is a serine kinase for Stat1 and is required for optimal transcriptional activation in response to IFNα (24), extensive work has confirmed the role of this PKC isoform in the regulation of serine 727 phosphorylation in Stat1 and has been extended to different cellular systems (2529) (Table 2). In the Type II IFN system five different serine kinases for the transactivation domain (TAD) of Stat1/phosphorylation on serine 727 have been demonstrated in different cell systems. ….

Protein tyrosine phosphatases with regulatory effects on Jak-Stat pathways in IFN-signaling.

MicroRNAs (miRs) and the IFN response

IFN-inducible JAK-STAT, MAPK and mTOR signaling cascades are also regulated potentially by microRNAs (miRs). miRs are important regulators of post-transcriptional events, leading to inhibition of mRNA translation or mRNA degradation (105). In recent years it has become apparent that the direct regulation of STAT activity by mIRs has profound effects on consequent gene expression, specifically in the context of cytokine-inducible events (106). Pertinent for this review of IFN-inducible STAT activation, miR-145, miR-146A and miR-221/222 target STAT1 and miR-221/222 target STAT2 (106). Numerous studies describe different miRs that target STAT3: mIR-17, miR-17-5p, mIR-17-3p, mIR-18a, miR-19b, mIR-92-1, miR-20b, Let-7a, miR-106a, miR-106-25, miR-106a-362 and miR-125b (106) (Fig. 4). mIR-132, miR-212 and miR-200a have been implicated in negatively regulating STAT4 expression in human NK cells (107) and miR-222 has been shown to regulate STAT5 expression (108). In addition, JAK-STAT signaling is affected by miR targeting of suppressors of cytokine signaling (SOCS) proteins. miR-122 and miR-155 targeting of SOCS1 releases the inhibition of STAT1 (and STAT5a/b) (109111), and mIR-19a regulation of SOCS1 and SOCS3 effectively prolongs activation of both STAT1 and STAT3 (112). There is also evidence that miR-155 targets the inositol phosphatase SHIP1, effectively prolonging/inducing IFN-γ expression (113). Much of the evidence associated with miRs prolonging JAK-STAT activation relates to cancer studies, where tumor-secreted miRs promote cell migration and angiogenesis by prolonging JAK-STAT activation (114). miR-145 targeting of SOCS7 affects nuclear translocation of STAT3 and has been associated with enhanced IFNβ production (115). Beyond inhibition of SOCS proteins, miRs may influence the expression of other inhibitory factors associated with JAK-STAT signaling, and miR-301a and miR-18a have been shown to inhibit PIAS3, a negative regulator of STAT3 activation (116). There is also the potential for STATS to directly regulate miR gene expression. STAT5 suppresses expression of miR15/16 (117) and there is evidence that there are potential STAT3 binding sites in the promoters of about 200 miRs (118). Viewed altogether, there is compelling evidence for miR-STAT interactions, yet few studies have considered the contributions of miRs to IFN-inducible JAK-STAT signaling.

Targeting and regulation of various proteins known to be involved in IFN-signaling by different miRNAs.  ….

Evolution of our understanding of IFN-signals and future perspectives

A substantial amount of knowledge has accumulated since the original discovery of the Jak-Stat pathway in the early 90s. It is now clear that several key signaling cascades are essential for the induction of Type I, II and III IFN-responses. The original view that IFN-signals can be transmitted from the cell surface to the nucleus in two simple steps involving tyrosine phosphorylation of Stat proteins (8) now appears somewhat simplistic, as it has been established that modifications of Jak-Stat signals by other pathways and/or simultaneous engagement of other essential complementary cellular cascades is essential for induction of ISG transcriptional activation, mRNA translation, protein expression and subsequent induction of IFN-responses. Such pathways include PKC and MAP kinase pathways and mTORC1 and mTORC2-dpendent signaling cascades.

Over the next decade our understanding of the mechanisms by which IFN-signals are induced will likely continue to evolve, with the anticipated outcome that it will be possible exploit this new knowledge for translational-therapeutic purposes. For instance, selective targeting of kinase-elements of the IFN-pathway with kinase inhibitors may be useful in the treatment of autoimmune diseases where dysregulated/excessive Type I IFN production contributes to the pathophysiology of disease. On the other hand, efforts to promote the induction of specific IFN-signals, may lead to novel, less toxic, therapeutic interventions for a variety of viral infectious diseases and neoplastic disorders.

Exploring the RNA World in Hematopoietic Cells Through the Lens of RNA-Binding Proteins

The discovery of microRNAs has renewed interest in post-transcriptional modes of regulation, fueling an emerging view of a rich RNA world within our cells that deserves further exploration. Much work has gone into elucidating genetic regulatory networks that orchestrate gene expression programs and direct cell fate decisions in the hematopoietic system. However, the focus has been to elucidate signaling pathways and transcriptional programs. To bring us one step closer to reverse engineering the molecular logic of cellular differentiation, it will be necessary to map post-transcriptional circuits as well and integrate them in the context of existing network models. In this regard, RNA-binding proteins (RBPs) may rival transcription factors as important regulators of cell fates and represent a tractable opportunity to connect the RNA world to the proteome. ChIP-seq has greatly facilitated genome-wide localization of DNA-binding proteins, helping us to understand genomic regulation at a systems level. Similarly, technological advances such as CLIP-seq allow transcriptome-wide mapping of RBP binding sites, aiding us to unravel post-transcriptional networks. Here, we review RBP-mediated post-transcriptional regulation, paying special attention to findings relevant to the immune system. As a prime example, we highlight the RBP Lin28B, which acts as a heterochronic switch between fetal and adult lymphopoiesis.

The basis of cellular differentiation and function can be represented as integrated circuits that are genetically programmed. Identification of the master regulators within these complex circuits that can switch on or off a genetic program will enable us to reprogram cells to suit biomedical needs. A remarkable example was the discovery by Takahashi and Yamanaka (1) that somatic cells could be reprogrammed into induced pluripotent stem (iPS) cells via the ectopic expression of four key transcription factors. Interestingly, a specific set of microRNAs (miRNAs) could also mediate this reprogramming (2, 3), revealing a powerful layer of post-transcriptional regulation that is able to override a pre-existing transcriptional program (4). Similarly, miR-9 and miR-124 were sufficient to mediate transdifferentiation of human fibroblasts into neurons (5). Accordingly, we are enamored by the RNA world and pay special attention in our investigations to regulatory non-coding RNAs (ncRNAs), particularly miRNAs and long non-coding RNAs (lncRNAs) and how they integrate with known genetic regulatory networks (Fig. 1). With the exception of certain ribozymes, regulatory RNAs generally do not work alone. Instead, they are physically organized as RNA-protein (RNP) complexes. Operationally, RNA-binding proteins (RBPs) and their interactome work in concert as post-transcriptional networks, or RNA regulons, in response to developmental and environmental cues (6). Inspired by this concept and other pioneering studies in the worm, we recently demonstrated that a single RBP Lin28 was sufficient to reprogram adult hematopoietic progenitors to adopt fetal-like properties (7). We discuss these and related findings, which begin to disentangle the complex functions of RBPs in the context of recent advances in post-transcriptional regulation, starting with the discovery of miRNAs.

Fig. 1

Updated model of gene regulation that integrates RBPs and ncRNAs

The Lin28/let-7 circuit: from worm development to lymphopoiesis

Inspiration from the worm

Working in C. elegans, Ambros and Horvitz (8) identified a set of genes that control developmental timing, a category that they termed heterochronic genes. Heterochrony is a term coined by evolutionary biologists and popularized by the worm community to denote events that either positively or negatively regulate developmental timing in multicellular organisms. The discovery of two heterochronic genes, lin-4 and lin-28, which encode a miRNA and RBP respectively, is particularly relevant to this review. The lineage (lin) mutants were previously identified and named because they displayed abnormalities in cell lineage differentiation. Furthermore, some of them were considered heterochronic, as adult mutants harbored immature characteristics (retarded phenotype) or, conversely, larval mutants displayed adult characteristics (precocious phenotype). It was not until 1993 that lin-4 was characterized molecularly, because contrary to popular expectations, the gene did not encode a protein but instead a small RNA now appreciated as the first miRNA to be discovered (9). The lin-4 miRNA acts in part by inhibiting the expression of the LIN-14 transcription factor through imperfect basepairing to sites in the 3′ untranslated region (UTR) of lin-14 mRNA (9, 10). However, it was not apparent initially whether lin-4 or lin-14 is evolutionarily conserved, potentially relegating these findings to be relevant only to the worm. Interestingly, Lin28, a gene conserved in mammals, was later identified to be a direct target of the lin-4 miRNA (11). Lin28 loss-of-function resulted in a precocious phenotype, whereas gain-of-function resulted in a retarded phenotype; thus, Lin28 acts as a heterochronic switch during C. elegans larval development (11).

The possibility that lin-4 may be an oddity of the worm was dissolved with the discovery of the second miRNA, again in C. elegans, let-7 (12). Unlike lin-4, the evolutionary conservation of let-7 from sea urchin to human was quickly appreciated (13). Importantly, expression analysis showed that let-7 expression is temporally regulated from molluscs to vertebrates in all three major clades of bilaterian animals, implying that its role as a developmental timekeeper is conserved (14). This established miRNAs as a field unto its own that has progressed rapidly with the identification of Drosha, Dgcr8, Dicer, and Argonaute (Ago) RBPs as core components of the miRNA pathway (15). Orthologs of lin-4were eventually found in mammals (mir-125a, -b-1, and -b-2) (16) along with hundreds of novel miRNAs from numerous organisms (17). We now recognize that miRNAs, in complex with the RBP Ago, frequently bind their cognate targets via imperfect complementarity to evolutionarily conserved sequences in 3′ UTRs (1820) and mediate post-transcriptional repression (21).


One diverse group of RBPs appreciated to be important in the immune system, even before the discovery of miRNAs, is distinguished by their ability to bind to AU-rich elements (AREs) often found in 3′ UTRs of genes involved in inflammation, growth, and survival. Such RBPs are known as ARE-BPs and have been implicated in mRNA decay, alternative splicing, translation, as well as both alleviating and enhancing miRNA-mediated mRNA repression (104107). Genetic inactivation of several ARE-BPs have been linked to aberrant cytokine expression due to impaired ARE-mediated decay (5, 108111) (Table 1). In addition, deficiency of HuR and AUF1 has uncovered a pro-survival role for both in lymphocytes (112, 113), while ectopic expression of Tis11b (ZFP36L1) negatively regulates erythropoiesis by down-regulating Stat5b mRNA stability (114). The KH-type splicing regulatory protein (KSRP) originally identified as an alternative-splicing factor is a multi-functional RBP. It has been shown to associate with both Drosha and Dicer complexes to positively regulate the biogenesis of a subset of miRNAs including mir-155 and let-7 (73, 108, 115120). In addition, KSRP, like many other ARE-BPs, mediate selective decay of mRNAs by recruitment of exosome complexes to mRNA targets (121) and constitutes a prime example of a multi-functional RBP.


Biological processes involved in the development and function of the immune system require programmed changes in protein production and constitute prime candidates for post-transcriptional regulation. While the ENCODE project initially aimed to identify all functional elements in the human DNA sequence, recent discoveries centered around miRNAs and multi-tasking RBPs, such as Lin28, have highlighted the need for a similar systematic effort in mapping post-transcriptional functional elements within the transcriptome. Integration of genomic, transcriptomic, and proteomic data remains a daunting but necessary task to achieve understanding of the full impact of genetic programs and the enigmatic roles of regulatory RNAs. Mastering the science of (re)programming cell fates promises to unleash the potential of stem cells for Regenerative Medicine.

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SomaticSeq: An Ensemble Approach with Machine Learning to Detect Cancer Variants

June 16 at 1pm EDT Register for this Webinar |  View All Webinars

Accurate detection of somatic mutations has proven to be challenging in cancer NGS analysis, due to tumor heterogeneity and cross-contamination between tumor and matched normal samples. Oftentimes, a somatic caller that performs well for one tumor may not for another.

In this webinar we will introduce SomaticSeq, a tool within the Bina Genomic Management Solution (Bina GMS) designed to boost the accuracy of somatic mutation detection with a machine learning approach. You will learn:

  • Benchmarking of leading somatic callers, namely MuTect, SomaticSniper, VarScan2, JointSNVMix2, and VarDict
  • Integration of such tools and how accuracy is achieved using a machine learning classifier that incorporates over 70 features with SomaticSeq
  • Accuracy validation including results from the ICGC-TCGA DREAM Somatic Mutation Calling Challenge, in which Bina placed 1st in indel calling and 2nd in SNV calling in stage 5
  • Creation of a new SomaticSeq classifier utilizing your own dataset
  • Review of the somatic workflow within the Bina Genomic Management Solution


Li Tai Fang

Li Tai Fang
Sr. Bioinformatics Scientist
Bina Technologies, Part of
Roche Sequencing

Anoop Grewal

Anoop Grewal
Product Marketing Manager
Bina Technologies, Part of
Roche Sequencing

<Read full speaker bios here>

Cost: No cost!

Schedule conflict? Register now and you’ll receive a copy of the recording.

This webinar is compliments of: 


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Cancer initiatives

Larry H. Bernstein, MD, FCAP, Curator



Updated 4/12/2019

AACR 2016: Biden Calls for Overhauling Cancer Research Incentives



The first priority cited by the vice president was data sharing. Biden defended the concept as essential to advancing the process of cancer research and countered a January 21 New England Journal of Medicine editorial in which editor-in-chief Jeffrey Drazen, M.D., contended that data sharing could breed data “parasites.”

Four days later, Dr. Drazen clarified NEJM’s position by adding that with “appropriate systems” in place, “we will require a commitment from authors to make available the data that underlie the reported results of their work within 6 months after we publish them.”

Other priorities Biden said should serve as the basis of new incentives:

  • Involve patients in clinical trial design—Raising awareness of trials, and allowing patients to participate in how they are designed and conducted, could help address the difficulty of recruiting patients for studies. Only 4% of cancer patients are involved in a trial, he said.
  • “Let scientists do science”—Biden contrasted unfavorably NIH’s roughly 1-year process for decisions on grants to that of the Prostate Cancer Foundation, which limits grant applications to 10 pages and decides on those funding requests within 30 days: “Why is it that it takes multiple submissions and more than a year to get an answer from us?” Biden said.
  • Encourage grants from younger researchers—Biden decried the current professional system under which younger researchers are sidetracked for years doing administrative work in labs before they can pursue their own research grants: “It’s like asking Derek Jeter to take several years off to sell bonds to build Yankee Stadium,” the VP quipped.
  • Measure progress by outcomes—Rather than the quantity of research papers generated by grants, Biden said, “what you propose and how it affects patients, it seems to me, should be the basis of whether you continue to get the grant.”
  • Promote open-access publication of results—Biden criticized academic publishing’s reliance on paid-subscription journals that block content behind paywalls and which own data for up to a year. He contrasted that system with the Bill and Melinda Gates Foundation’s stipulation that the research it funds be published in an open-access journal and be freely available once published.
  • Reward verification—Research that verifies results through replication should be encouraged, Biden said, which acknowledging that few people now get such funding.

Biden recalled how following Beau’s diagnosis with cancer, he and his wife Jill Biden, Ed.D., who introduced the VP at the AACR event, “had access to the best doctors in the world.”

“The more we talked to them, the more we understood that we are on the cusp of a real inflection point in the fight against cancer.”

Updated 4/12/2019

Pediatric Cancer Initiatives

Data Sharing for Pediatric Cancers: President Trump Announces Pledge to Fight Childhood Cancer Will Involve Genomic Data Sharing Effort

In the journal Science, Drs. Olena Morozova Vaske ( and David Haussler University of California, Santa Cruz) recently wrote an editorial entitled “Data Sharing for Pediatric Cancers“, in which they discuss the implications of President Trump’s intentions to increase funding for pediatric cancers with a corresponding effort for genomic data sharing.  Also discussed is the current efforts on pediatric genomic data sharing as well as some opinions on coordinating these efforts on a world-wide scale to benefit the patients, researchers, and clinicians.

The article is found below as it is a very good read on the state of data sharing in the pediatric cancer field and offers some very good insights in designing such a worldwide system to handle this data sharing, including allowing patients governance over their own data.

Last month, in a conference call held by the U.S. Department of Health and Human Services and National Institutes of Health (NIH), it was revealed that a large focus of President Trump’s pledge to fund childhood cancer research will be genomic data sharing. Although the United States has only 5% of the world’s pediatric cancer cases, it has disproportionately more resources and access to genomic information compared to low-income countries. We hope that the spotlight on genomic data sharing in the United States will galvanize the world’s pediatric cancer community to elevate genomic data sharing to a level where its full potential can finally be realized.

Pediatric cancers are rare, affecting 50 to 200 children per million a year worldwide. Thus, with 16 different major types and many subtypes, no cancer center encounters large cohorts of patients with the same diagnosis. To advance their understanding of particular cancer subtypes, pediatric oncologists must have access to data from similar cases at other centers. Because subtypes of pediatric cancer are rare, assembling large cohorts is a limiting factor in clinical trials as well. Here, too, data sharing is the first critical step.

Typically, pediatric cancers don’t have the number of mutations that make immunotherapies effective, and only a few subtypes have recurrent mutations that can be used to develop gene-targeted therapies. However, the abnormal expression level of genes gives a vivid picture of genetic misregulation, and just sharing this information would be a huge step forward. Using gene expression and mutation data, analysis of genetic misregulation in different pediatric cancer subtypes could point the way to new treatments.

A major challenge in genomic data sharing is the patient’s young age, which frequently precludes an opportunity for informed consent. Compounding this, the rarity of subtypes requires the aggregation of patients from multiple jurisdictions, raising barriers to assembling large representative data sets. A greater percentage of children than adults with cancer participate in research studies, and children often participate in multiple studies. However, this means that data collected on individual children may be found at multiple institutions, creating difficulties if there are no standards for data sharing.

To enable effective sharing of genomic and clinical data, the Global Alliance for Genomics and Health has developed the Key Implications for Data Sharing (KIDS) framework for pediatric genomics. The recommendations include involving children in the data-sharing decision-making process and imposing an ethical obligation on data generators to provide children and parents with the opportunity to share genomic and clinical information with researchers. Although KIDS guidelines are not legally binding, they could inform policy development worldwide.

To advance the sharing culture, along with the NIH, pediatric cancer foundations such as the St. Baldrick’s Foundation and Alex’s Lemonade Stand Foundation have incorporated genomic data-sharing requirements into their grants processes. Researchers and clinicians around the world have created dozens of pediatric cancer genomic databases and portals, but pulling these together into a larger network is problematic, especially for patients with data at more than one institution, as patient identifiers are stripped from shared data. However, initiatives like the Children’s Oncology Group’s Project Every Child and the European Network for Cancer Research in Children and Adolescents’ Unified Patient Identity may resolve this issue.

We urge the creators of pediatric cancer genomic resources to collaborate and build a real-time federated data-sharing system, and hope that the new U.S. initiative will inspire other countries to link databases rather than just create new siloed regional resources. The great advances in information technology and life sciences in the last decades have given us a new opportunity to save our children from the scourge of cancer. We must resolve to use them.

Source: Olena Morozova Vaske and David Haussler.  Science; 363(6432): 1125 (2019). Data sharing for pediatric cancers. 

NIH-NCI Initiative: International collaboration to create new cancer models to accelerate research

LIVE 1:45 pm – 3:10 pm 4/25/2016 Forum Opening, A War or Moonshot: Where Do We Stand? Creating a Disruptive Cancer Pipeline @2016 World Medical Innovation Forum: CANCER, April 25-27, 2016, Westin Hotel, Boston

Will President Obama’ s Cancer Immunotherapy Colloquium (dubbed Moonshot) mean Government is Fully Behind the War on Cancer or have we heard this before?

Exome Aggregation Consortium (ExAC), generated the largest catalogue so far of variation in human protein-coding regions: Sequence data of 60,000 people, NOW is a publicly accessible database

Healthcare conglomeration to access Big Data and lower costs


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CRISPR/Cas9, Familial Amyloid Polyneuropathy ( FAP) and Neurodegenerative Disease

CRISPR/Cas9, Familial Amyloid Polyneuropathy ( FAP) and Neurodegenerative Disease

Curator: Larry H. Bernstein, MD, FCAP


CRISPR/Cas9 and Targeted Genome Editing: A New Era in Molecular Biology


The development of efficient and reliable ways to make precise, targeted changes to the genome of living cells is a long-standing goal for biomedical researchers. Recently, a new tool based on a bacterial CRISPR-associated protein-9 nuclease (Cas9) from Streptococcus pyogenes has generated considerable excitement (1). This follows several attempts over the years to manipulate gene function, including homologous recombination (2) and RNA interference (RNAi) (3). RNAi, in particular, became a laboratory staple enabling inexpensive and high-throughput interrogation of gene function (4, 5), but it is hampered by providing only temporary inhibition of gene function and unpredictable off-target effects (6). Other recent approaches to targeted genome modification – zinc-finger nucleases [ZFNs, (7)] and transcription-activator like effector nucleases [TALENs (8)]– enable researchers to generate permanent mutations by introducing doublestranded breaks to activate repair pathways. These approaches are costly and time-consuming to engineer, limiting their widespread use, particularly for large scale, high-throughput studies.

The Biology of Cas9

The functions of CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) and CRISPR-associated (Cas) genes are essential in adaptive immunity in select bacteria and archaea, enabling the organisms to respond to and eliminate invading genetic material. These repeats were initially discovered in the 1980s in E. coli (9), but their function wasn’t confirmed until 2007 by Barrangou and colleagues, who demonstrated that S. thermophilus can acquire resistance against a bacteriophage by integrating a genome fragment of an infectious virus into its CRISPR locus (10).

Three types of CRISPR mechanisms have been identified, of which type II is the most studied. In this case, invading DNA from viruses or plasmids is cut into small fragments and incorporated into a CRISPR locus amidst a series of short repeats (around 20 bps). The loci are transcribed, and transcripts are then processed to generate small RNAs (crRNA – CRISPR RNA), which are used to guide effector endonucleases that target invading DNA based on sequence complementarity (Figure 1) (11).

Figure 1. Cas9 in vivo: Bacterial Adaptive Immunity


In the acquisition phase, foreign DNA is incorporated into the bacterial genome at the CRISPR loci. CRISPR loci is then transcribed and processed into crRNA during crRNA biogenesis. During interference, Cas9 endonuclease complexed with a crRNA and separate tracrRNA cleaves foreign DNA containing a 20-nucleotide crRNA complementary sequence adjacent to the PAM sequence. (Figure not drawn to scale.)


One Cas protein, Cas9 (also known as Csn1), has been shown, through knockdown and rescue experiments to be a key player in certain CRISPR mechanisms (specifically type II CRISPR systems). The type II CRISPR mechanism is unique compared to other CRISPR systems, as only one Cas protein (Cas9) is required for gene silencing (12). In type II systems, Cas9 participates in the processing of crRNAs (12), and is responsible for the destruction of the target DNA (11). Cas9’s function in both of these steps relies on the presence of two nuclease domains, a RuvC-like nuclease domain located at the amino terminus and a HNH-like nuclease domain that resides in the mid-region of the protein (13).

To achieve site-specific DNA recognition and cleavage, Cas9 must be complexed with both a crRNA and a separate trans-activating crRNA (tracrRNA or trRNA), that is partially complementary to the crRNA (11). The tracrRNA is required for crRNA maturation from a primary transcript encoding multiple pre-crRNAs. This occurs in the presence of RNase III and Cas9 (12).

During the destruction of target DNA, the HNH and RuvC-like nuclease domains cut both DNA strands, generating double-stranded breaks (DSBs) at sites defined by a 20-nucleotide target sequence within an associated crRNA transcript (11, 14). The HNH domain cleaves the complementary strand, while the RuvC domain cleaves the noncomplementary strand.

The double-stranded endonuclease activity of Cas9 also requires that a short conserved sequence, (2–5 nts) known as protospacer-associated motif (PAM), follows immediately 3´- of the crRNA complementary sequence (15). In fact, even fully complementary sequences are ignored by Cas9-RNA in the absence of a PAM sequence (16).

Cas9 and CRISPR as a New Tool in Molecular Biology

The simplicity of the type II CRISPR nuclease, with only three required components (Cas9 along with the crRNA and trRNA) makes this system amenable to adaptation for genome editing. This potential was realized in 2012 by the Doudna and Charpentier labs (11). Based on the type II CRISPR system described previously, the authors developed a simplified two-component system by combining trRNA and crRNA into a single synthetic single guide RNA (sgRNA). sgRNAprogrammed Cas9 was shown to be as effective as Cas9 programmed with separate trRNA and crRNA in guiding targeted gene alterations (Figure 2A).

To date, three different variants of the Cas9 nuclease have been adopted in genome-editing protocols. The first is wild-type Cas9, which can site-specifically cleave double-stranded DNA, resulting in the activation of the doublestrand break (DSB) repair machinery. DSBs can be repaired by the cellular Non-Homologous End Joining (NHEJ) pathway (17), resulting in insertions and/or deletions (indels) which disrupt the targeted locus. Alternatively, if a donor template with homology to the targeted locus is supplied, the DSB may be repaired by the homology-directed repair (HDR) pathway allowing for precise replacement mutations to be made (Figure 2A) (17, 18).

Cong and colleagues (1) took the Cas9 system a step further towards increased precision by developing a mutant form, known as Cas9D10A, with only nickase activity. This means it cleaves only one DNA strand, and does not activate NHEJ. Instead, when provided with a homologous repair template, DNA repairs are conducted via the high-fidelity HDR pathway only, resulting in reduced indel mutations (1, 11, 19). Cas9D10A is even more appealing in terms of target specificity when loci are targeted by paired Cas9 complexes designed to generate adjacent DNA nicks (20) (see further details about “paired nickases” in Figure 2B).

The third variant is a nuclease-deficient Cas9 (dCas9, Figure 2C) (21). Mutations H840A in the HNH domain and D10A in the RuvC domain inactivate cleavage activity, but do not prevent DNA binding (11, 22). Therefore, this variant can be used to sequence-specifically target any region of the genome without cleavage. Instead, by fusing with various effector domains, dCas9 can be used either as a gene silencing or activation tool (21, 23–26). Furthermore, it can be used as a visualization tool. For instance, Chen and colleagues used dCas9 fused to Enhanced Green Fluorescent Protein (EGFP) to visualize repetitive DNA sequences with a single sgRNA or nonrepetitive loci using multiple sgRNAs (27).

Figure 2. CRISPR/Cas9 System Applications


  1. Wild-type Cas9 nuclease site specifically cleaves double-stranded DNA activating double-strand break repair machinery. In the absence of a homologous repair template non-homologous end joining can result in indels disrupting the target sequence. Alternatively, precise mutations and knock-ins can be made by providing a homologous repair template and exploiting the homology directed repair pathway.
    B. Mutated Cas9 makes a site specific single-strand nick. Two sgRNA can be used to introduce a staggered double-stranded break which can then undergo homology directed repair.
    C. Nuclease-deficient Cas9 can be fused with various effector domains allowing specific localization. For example, transcriptional activators, repressors, and fluorescent proteins.

Targeting Efficiency and Off-target Mutations

Targeting efficiency, or the percentage of desired mutation achieved, is one of the most important parameters by which to assess a genome-editing tool. The targeting efficiency of Cas9 compares favorably with more established methods, such as TALENs or ZFNs (8). For example, in human cells, custom-designed ZFNs and TALENs could only achieve efficiencies ranging from 1% to 50% (29–31). In contrast, the Cas9 system has been reported to have efficiencies up to >70% in zebrafish (32) and plants (33), and ranging from 2–5% in induced pluripotent stem cells (34). In addition, Zhou and colleagues were able to improve genome targeting up to 78% in one-cell mouse embryos, and achieved effective germline transmission through the use of dual sgRNAs to simultaneously target an individual gene (35).

A widely used method to identify mutations is the T7 Endonuclease I mutation detection assay (36, 37) (Figure 3). This assay detects heteroduplex DNA that results from the annealing of a DNA strand, including desired mutations, with a wildtype DNA strand (37).

Figure 3. T7 Endonuclease I Targeting Efficiency Assay


Genomic DNA is amplified with primers bracketing the modified locus. PCR products are then denatured and re-annealed yielding 3 possible structures. Duplexes containing a mismatch are digested by T7 Endonuclease I. The DNA is then electrophoretically separated and fragment analysis is used to calculate targeting efficiency.

Another important parameter is the incidence of off-target mutations. Such mutations are likely to appear in sites that have differences of only a few nucleotides compared to the original sequence, as long as they are adjacent to a PAM sequence. This occurs as Cas9 can tolerate up to 5 base mismatches within the protospacer region (36) or a single base difference in the PAM sequence (38). Off-target mutations are generally more difficult to detect, requiring whole-genome sequencing to rule them out completely.

Recent improvements to the CRISPR system for reducing off-target mutations have been made through the use of truncated gRNA (truncated within the crRNA-derived sequence) or by adding two extra guanine (G) nucleotides to the 5´ end (28, 37). Another way researchers have attempted to minimize off-target effects is with the use of “paired nickases” (20). This strategy uses D10A Cas9 and two sgRNAs complementary to the adjacent area on opposite strands of the target site (Figure 2B). While this induces DSBs in the target DNA, it is expected to create only single nicks in off-target locations and, therefore, result in minimal off-target mutations.

By leveraging computation to reduce off-target mutations, several groups have developed webbased tools to facilitate the identification of potential CRISPR target sites and assess their potential for off-target cleavage. Examples include the CRISPR Design Tool (38) and the ZiFiT Targeter, Version 4.2 (39, 40).

Applications as a Genome-editing and Genome Targeting Tool

Following its initial demonstration in 2012 (9), the CRISPR/Cas9 system has been widely adopted. This has already been successfully used to target important genes in many cell lines and organisms, including human (34), bacteria (41), zebrafish (32), C. elegans (42), plants (34), Xenopus tropicalis (43), yeast (44), Drosophila (45), monkeys (46), rabbits (47), pigs (42), rats (48) and mice (49). Several groups have now taken advantage of this method to introduce single point mutations (deletions or insertions) in a particular target gene, via a single gRNA (14, 21, 29). Using a pair of gRNA-directed Cas9 nucleases instead, it is also possible to induce large deletions or genomic rearrangements, such as inversions or translocations (50). A recent exciting development is the use of the dCas9 version of the CRISPR/Cas9 system to target protein domains for transcriptional regulation (26, 51, 52), epigenetic modification (25), and microscopic visualization of specific genome loci (27).

The CRISPR/Cas9 system requires only the redesign of the crRNA to change target specificity. This contrasts with other genome editing tools, including zinc finger and TALENs, where redesign of the protein-DNA interface is required. Furthermore, CRISPR/Cas9 enables rapid genome-wide interrogation of gene function by generating large gRNA libraries (51, 53) for genomic screening.

The Future of CRISPR/Cas9

The rapid progress in developing Cas9 into a set of tools for cell and molecular biology research has been remarkable, likely due to the simplicity, high efficiency and versatility of the system. Of the designer nuclease systems currently available for precision genome engineering, the CRISPR/Cas system is by far the most user friendly. It is now also clear that Cas9’s potential reaches beyond DNA cleavage, and its usefulness for genome locus-specific recruitment of proteins will likely only be limited by our imagination.


Scientists urge caution in using new CRISPR technology to treat human genetic disease

By Robert Sanders, Media relations | MARCH 19, 2015


The bacterial enzyme Cas9 is the engine of RNA-programmed genome engineering in human cells. (Graphic by Jennifer Doudna/UC Berkeley)

A group of 18 scientists and ethicists today warned that a revolutionary new tool to cut and splice DNA should be used cautiously when attempting to fix human genetic disease, and strongly discouraged any attempts at making changes to the human genome that could be passed on to offspring.

Among the authors of this warning is Jennifer Doudna, the co-inventor of the technology, called CRISPR-Cas9, which is driving a new interest in gene therapy, or “genome engineering.” She and colleagues co-authored a perspective piece that appears in the March 20 issue of Science, based on discussions at a meeting that took place in Napa on Jan. 24. The same issue of Science features a collection of recent research papers, commentary and news articles on CRISPR and its implications.    …..

A prudent path forward for genomic engineering and germline gene modification

David Baltimore1,  Paul Berg2, …., Jennifer A. Doudna4,10,*, et al.
Science  19 Mar 2015.  http://dx.doi.org:/10.1126/science.aab1028


Correcting genetic defects

Scientists today are changing DNA sequences to correct genetic defects in animals as well as cultured tissues generated from stem cells, strategies that could eventually be used to treat human disease. The technology can also be used to engineer animals with genetic diseases mimicking human disease, which could lead to new insights into previously enigmatic disorders.

The CRISPR-Cas9 tool is still being refined to ensure that genetic changes are precisely targeted, Doudna said. Nevertheless, the authors met “… to initiate an informed discussion of the uses of genome engineering technology, and to identify proactively those areas where current action is essential to prepare for future developments. We recommend taking immediate steps toward ensuring that the application of genome engineering technology is performed safely and ethically.”


Amyloid CRISPR Plasmids and si/shRNA Gene Silencers


Santa Cruz Biotechnology, Inc. offers a broad range of gene silencers in the form of siRNAs, shRNA Plasmids and shRNA Lentiviral Particles as well as CRISPR/Cas9 Knockout and CRISPR Double Nickase plasmids. Amyloid gene silencers are available as Amyloid siRNA, Amyloid shRNA Plasmid, Amyloid shRNA Lentiviral Particles and Amyloid CRISPR/Cas9 Knockout plasmids. Amyloid CRISPR/dCas9 Activation Plasmids and CRISPR Lenti Activation Systems for gene activation are also available. Gene silencers and activators are useful for gene studies in combination with antibodies used for protein detection.    Amyloid CRISPR Knockout, HDR and Nickase Knockout Plasmids


CRISPR-Cas9-Based Knockout of the Prion Protein and Its Effect on the Proteome

Mehrabian M, Brethour D, MacIsaac S, Kim JK, Gunawardana C.G, Wang H, et al.
PLoS ONE 2014; 9(12): e114594. http://dx.doi.org/10.1371/journal.pone.0114594

The molecular function of the cellular prion protein (PrPC) and the mechanism by which it may contribute to neurotoxicity in prion diseases and Alzheimer’s disease are only partially understood. Mouse neuroblastoma Neuro2a cells and, more recently, C2C12 myocytes and myotubes have emerged as popular models for investigating the cellular biology of PrP. Mouse epithelial NMuMG cells might become attractive models for studying the possible involvement of PrP in a morphogenetic program underlying epithelial-to-mesenchymal transitions. Here we describe the generation of PrP knockout clones from these cell lines using CRISPR-Cas9 knockout technology. More specifically, knockout clones were generated with two separate guide RNAs targeting recognition sites on opposite strands within the first hundred nucleotides of the Prnp coding sequence. Several PrP knockout clones were isolated and genomic insertions and deletions near the CRISPR-target sites were characterized. Subsequently, deep quantitative global proteome analyses that recorded the relative abundance of>3000 proteins (data deposited to ProteomeXchange Consortium) were undertaken to begin to characterize the molecular consequences of PrP deficiency. The levels of ∼120 proteins were shown to reproducibly correlate with the presence or absence of PrP, with most of these proteins belonging to extracellular components, cell junctions or the cytoskeleton.




Development and Applications of CRISPR-Cas9 for Genome Engineering

Patrick D. Hsu,1,2,3 Eric S. Lander,1 and Feng Zhang1,2,*
Cell. 2014 Jun 5; 157(6): 1262–1278.   doi:  10.1016/j.cell.2014.05.010

Recent advances in genome engineering technologies based on the CRISPR-associated RNA-guided endonuclease Cas9 are enabling the systematic interrogation of mammalian genome function. Analogous to the search function in modern word processors, Cas9 can be guided to specific locations within complex genomes by a short RNA search string. Using this system, DNA sequences within the endogenous genome and their functional outputs are now easily edited or modulated in virtually any organism of choice. Cas9-mediated genetic perturbation is simple and scalable, empowering researchers to elucidate the functional organization of the genome at the systems level and establish causal linkages between genetic variations and biological phenotypes. In this Review, we describe the development and applications of Cas9 for a variety of research or translational applications while highlighting challenges as well as future directions. Derived from a remarkable microbial defense system, Cas9 is driving innovative applications from basic biology to biotechnology and medicine.

The development of recombinant DNA technology in the 1970s marked the beginning of a new era for biology. For the first time, molecular biologists gained the ability to manipulate DNA molecules, making it possible to study genes and harness them to develop novel medicine and biotechnology. Recent advances in genome engineering technologies are sparking a new revolution in biological research. Rather than studying DNA taken out of the context of the genome, researchers can now directly edit or modulate the function of DNA sequences in their endogenous context in virtually any organism of choice, enabling them to elucidate the functional organization of the genome at the systems level, as well as identify causal genetic variations.

Broadly speaking, genome engineering refers to the process of making targeted modifications to the genome, its contexts (e.g., epigenetic marks), or its outputs (e.g., transcripts). The ability to do so easily and efficiently in eukaryotic and especially mammalian cells holds immense promise to transform basic science, biotechnology, and medicine (Figure 1).


For life sciences research, technologies that can delete, insert, and modify the DNA sequences of cells or organisms enable dissecting the function of specific genes and regulatory elements. Multiplexed editing could further allow the interrogation of gene or protein networks at a larger scale. Similarly, manipulating transcriptional regulation or chromatin states at particular loci can reveal how genetic material is organized and utilized within a cell, illuminating relationships between the architecture of the genome and its functions. In biotechnology, precise manipulation of genetic building blocks and regulatory machinery also facilitates the reverse engineering or reconstruction of useful biological systems, for example, by enhancing biofuel production pathways in industrially relevant organisms or by creating infection-resistant crops. Additionally, genome engineering is stimulating a new generation of drug development processes and medical therapeutics. Perturbation of multiple genes simultaneously could model the additive effects that underlie complex polygenic disorders, leading to new drug targets, while genome editing could directly correct harmful mutations in the context of human gene therapy (Tebas et al., 2014).

Eukaryotic genomes contain billions of DNA bases and are difficult to manipulate. One of the breakthroughs in genome manipulation has been the development of gene targeting by homologous recombination (HR), which integrates exogenous repair templates that contain sequence homology to the donor site (Figure 2A) (Capecchi, 1989). HR-mediated targeting has facilitated the generation of knockin and knockout animal models via manipulation of germline competent stem cells, dramatically advancing many areas of biological research. However, although HR-mediated gene targeting produces highly precise alterations, the desired recombination events occur extremely infrequently (1 in 106–109 cells) (Capecchi, 1989), presenting enormous challenges for large-scale applications of gene-targeting experiments.

Genome Editing Technologies Exploit Endogenous DNA Repair Machinery


To overcome these challenges, a series of programmable nuclease-based genome editing technologies have been developed in recent years, enabling targeted and efficient modification of a variety of eukaryotic and particularly mammalian species. Of the current generation of genome editing technologies, the most rapidly developing is the class of RNA-guided endonucleases known as Cas9 from the microbial adaptive immune system CRISPR (clustered regularly interspaced short palindromic repeats), which can be easily targeted to virtually any genomic location of choice by a short RNA guide. Here, we review the development and applications of the CRISPR-associated endonuclease Cas9 as a platform technology for achieving targeted perturbation of endogenous genomic elements and also discuss challenges and future avenues for innovation.   ……

Figure 4   Natural Mechanisms of Microbial CRISPR Systems in Adaptive Immunity


……  A key turning point came in 2005, when systematic analysis of the spacer sequences separating the individual direct repeats suggested their extrachromosomal and phage-associated origins (Mojica et al., 2005Pourcel et al., 2005Bolotin et al., 2005). This insight was tremendously exciting, especially given previous studies showing that CRISPR loci are transcribed (Tang et al., 2002) and that viruses are unable to infect archaeal cells carrying spacers corresponding to their own genomes (Mojica et al., 2005). Together, these findings led to the speculation that CRISPR arrays serve as an immune memory and defense mechanism, and individual spacers facilitate defense against bacteriophage infection by exploiting Watson-Crick base-pairing between nucleic acids (Mojica et al., 2005Pourcel et al., 2005). Despite these compelling realizations that CRISPR loci might be involved in microbial immunity, the specific mechanism of how the spacers act to mediate viral defense remained a challenging puzzle. Several hypotheses were raised, including thoughts that CRISPR spacers act as small RNA guides to degrade viral transcripts in a RNAi-like mechanism (Makarova et al., 2006) or that CRISPR spacers direct Cas enzymes to cleave viral DNA at spacer-matching regions (Bolotin et al., 2005).   …..

As the pace of CRISPR research accelerated, researchers quickly unraveled many details of each type of CRISPR system (Figure 4). Building on an earlier speculation that protospacer adjacent motifs (PAMs) may direct the type II Cas9 nuclease to cleave DNA (Bolotin et al., 2005), Moineau and colleagues highlighted the importance of PAM sequences by demonstrating that PAM mutations in phage genomes circumvented CRISPR interference (Deveau et al., 2008). Additionally, for types I and II, the lack of PAM within the direct repeat sequence within the CRISPR array prevents self-targeting by the CRISPR system. In type III systems, however, mismatches between the 5′ end of the crRNA and the DNA target are required for plasmid interference (Marraffini and Sontheimer, 2010).  …..

In 2013, a pair of studies simultaneously showed how to successfully engineer type II CRISPR systems from Streptococcus thermophilus (Cong et al., 2013) andStreptococcus pyogenes (Cong et al., 2013Mali et al., 2013a) to accomplish genome editing in mammalian cells. Heterologous expression of mature crRNA-tracrRNA hybrids (Cong et al., 2013) as well as sgRNAs (Cong et al., 2013Mali et al., 2013a) directs Cas9 cleavage within the mammalian cellular genome to stimulate NHEJ or HDR-mediated genome editing. Multiple guide RNAs can also be used to target several genes at once. Since these initial studies, Cas9 has been used by thousands of laboratories for genome editing applications in a variety of experimental model systems (Sander and Joung, 2014). ……

The majority of CRISPR-based technology development has focused on the signature Cas9 nuclease from type II CRISPR systems. However, there remains a wide diversity of CRISPR types and functions. Cas RAMP module (Cmr) proteins identified in Pyrococcus furiosus and Sulfolobus solfataricus (Hale et al., 2012) constitute an RNA-targeting CRISPR immune system, forming a complex guided by small CRISPR RNAs that target and cleave complementary RNA instead of DNA. Cmr protein homologs can be found throughout bacteria and archaea, typically relying on a 5 site tag sequence on the target-matching crRNA for Cmr-directed cleavage.

Unlike RNAi, which is targeted largely by a 6 nt seed region and to a lesser extent 13 other bases, Cmr crRNAs contain 30–40 nt of target complementarity. Cmr-CRISPR technologies for RNA targeting are thus a promising target for orthogonal engineering and minimal off-target modification. Although the modularity of Cmr systems for RNA-targeting in mammalian cells remains to be investigated, Cmr complexes native to P. furiosus have already been engineered to target novel RNA substrates (Hale et al., 20092012).   ……

Although Cas9 has already been widely used as a research tool, a particularly exciting future direction is the development of Cas9 as a therapeutic technology for treating genetic disorders. For a monogenic recessive disorder due to loss-of-function mutations (such as cystic fibrosis, sickle-cell anemia, or Duchenne muscular dystrophy), Cas9 may be used to correct the causative mutation. This has many advantages over traditional methods of gene augmentation that deliver functional genetic copies via viral vector-mediated overexpression—particularly that the newly functional gene is expressed in its natural context. For dominant-negative disorders in which the affected gene is haplosufficient (such as transthyretin-related hereditary amyloidosis or dominant forms of retinitis pigmentosum), it may also be possible to use NHEJ to inactivate the mutated allele to achieve therapeutic benefit. For allele-specific targeting, one could design guide RNAs capable of distinguishing between single-nucleotide polymorphism (SNP) variations in the target gene, such as when the SNP falls within the PAM sequence.



CRISPR/Cas9: a powerful genetic engineering tool for establishing large animal models of neurodegenerative diseases

Zhuchi Tu, Weili Yang, Sen Yan, Xiangyu Guo and Xiao-Jiang Li

Molecular Neurodegeneration 2015; 10:35  http://dx.doi.org:/10.1186/s13024-015-0031-x

Animal models are extremely valuable to help us understand the pathogenesis of neurodegenerative disorders and to find treatments for them. Since large animals are more like humans than rodents, they make good models to identify the important pathological events that may be seen in humans but not in small animals; large animals are also very important for validating effective treatments or confirming therapeutic targets. Due to the lack of embryonic stem cell lines from large animals, it has been difficult to use traditional gene targeting technology to establish large animal models of neurodegenerative diseases. Recently, CRISPR/Cas9 was used successfully to genetically modify genomes in various species. Here we discuss the use of CRISPR/Cas9 technology to establish large animal models that can more faithfully mimic human neurodegenerative diseases.

Neurodegenerative diseases — Alzheimer’s disease(AD),Parkinson’s disease(PD), amyotrophic lateral sclerosis (ALS), Huntington’s disease (HD), and frontotemporal dementia (FTD) — are characterized by age-dependent and selective neurodegeneration. As the life expectancy of humans lengthens, there is a greater prevalence of these neurodegenerative diseases; however, the pathogenesis of most of these neurodegenerative diseases remain unclear, and we lack effective treatments for these important brain disorders.

CRISPR/Cas9,  Non-human primates,  Neurodegenerative diseases,  Animal model

There are a number of excellent reviews covering different types of neurodegenerative diseases and their genetic mouse models [812]. Investigations of different mouse models of neurodegenerative diseases have revealed a common pathology shared by these diseases. First, the development of neuropathology and neurological symptoms in genetic mouse models of neurodegenerative diseases is age dependent and progressive. Second, all the mouse models show an accumulation of misfolded or aggregated proteins resulting from the expression of mutant genes. Third, despite the widespread expression of mutant proteins throughout the body and brain, neuronal function appears to be selectively or preferentially affected. All these facts indicate that mouse models of neurodegenerative diseases recapitulate important pathologic features also seen in patients with neurodegenerative diseases.

However, it seems that mouse models can not recapitulate the full range of neuropathology seen in patients with neurodegenerative diseases. Overt neurodegeneration, which is the most important pathological feature in patient brains, is absent in genetic rodent models of AD, PD, and HD. Many rodent models that express transgenic mutant proteins under the control of different promoters do not replicate overt neurodegeneration, which is likely due to their short life spans and the different aging processes of small animals. Also important are the remarkable differences in brain development between rodents and primates. For example, the mouse brain takes 21 days to fully develop, whereas the formation of primate brains requires more than 150 days [13]. The rapid development of the brain in rodents may render neuronal cells resistant to misfolded protein-mediated neurodegeneration. Another difficulty in using rodent models is how to analyze cognitive and emotional abnormalities, which are the early symptoms of most neurodegenerative diseases in humans. Differences in neuronal circuitry, anatomy, and physiology between rodent and primate brains may also account for the behavioral differences between rodent and primate models.


Mitochondrial dynamics–fusion, fission, movement, and mitophagy–in neurodegenerative diseases

Hsiuchen Chen and David C. Chan
Human Molec Gen 2009; 18, Review Issue 2 R169–R176

Neurons are metabolically active cells with high energy demands at locations distant from the cell body. As a result, these cells are particularly dependent on mitochondrial function, as reflected by the observation that diseases of mitochondrial dysfunction often have a neurodegenerative component. Recent discoveries have highlighted that neurons are reliant particularly on the dynamic properties of mitochondria. Mitochondria are dynamic organelles by several criteria. They engage in repeated cycles of fusion and fission, which serve to intermix the lipids and contents of a population of mitochondria. In addition, mitochondria are actively recruited to subcellular sites, such as the axonal and dendritic processes of neurons. Finally, the quality of a mitochondrial population is maintained through mitophagy, a form of autophagy in which defective mitochondria are selectively degraded. We review the general features of mitochondrial dynamics, incorporating recent findings on mitochondrial fusion, fission, transport and mitophagy. Defects in these key features are associated with neurodegenerative disease. Charcot-Marie-Tooth type 2A, a peripheral neuropathy, and dominant optic atrophy, an inherited optic neuropathy, result from a primary deficiency of mitochondrial fusion. Moreover, several major neurodegenerative diseases—including Parkinson’s, Alzheimer’s and Huntington’s disease—involve disruption of mitochondrial dynamics. Remarkably, in several disease models, the manipulation of mitochondrial fusion or fission can partially rescue disease phenotypes. We review how mitochondrial dynamics is altered in these neurodegenerative diseases and discuss the reciprocal interactions between mitochondrial fusion, fission, transport and mitophagy.


Applications of CRISPR–Cas systems in Neuroscience

Matthias Heidenreich  & Feng Zhang
Nature Rev Neurosci 2016; 17:36–44   http://dx.doi.org:/10.1038/nrn.2015.2

Genome-editing tools, and in particular those based on CRISPR–Cas (clustered regularly interspaced short palindromic repeat (CRISPR)–CRISPR-associated protein) systems, are accelerating the pace of biological research and enabling targeted genetic interrogation in almost any organism and cell type. These tools have opened the door to the development of new model systems for studying the complexity of the nervous system, including animal models and stem cell-derived in vitro models. Precise and efficient gene editing using CRISPR–Cas systems has the potential to advance both basic and translational neuroscience research.
Cellular neuroscience
, DNA recombination, Genetic engineering, Molecular neuroscience

Figure 3: In vitro applications of Cas9 in human iPSCs.close


a | Evaluation of disease candidate genes from large-population genome-wide association studies (GWASs). Human primary cells, such as neurons, are not easily available and are difficult to expand in culture. By contrast, induced pluripo…

  1. Genome-editing Technologies for Gene and Cell Therapy

Molecular Therapy 12 Jan 2016

  1. Systematic quantification of HDR and NHEJ reveals effects of locus, nuclease, and cell type on genome-editing

Scientific Reports 31 Mar 2016

  1. Controlled delivery of β-globin-targeting TALENs and CRISPR/Cas9 into mammalian cells for genome editing using microinjection

Scientific Reports 12 Nov 2015


Alzheimer’s Disease: Medicine’s Greatest Challenge in the 21st Century


The development of the CRISPR/Cas9 system has made gene editing a relatively simple task.  While CRISPR and other gene editing technologies stand to revolutionize biomedical research and offers many promising therapeutic avenues (such as in the treatment of HIV), a great deal of debate exists over whether CRISPR should be used to modify human embryos. As I discussed in my previous Insight article, we lack enough fundamental biological knowledge to enhance many traits like height or intelligence, so we are not near a future with genetically-enhanced super babies. However, scientists have identified a few rare genetic variants that protect against disease.  One such protective variant is a mutation in the APP gene that protects against Alzheimer’s disease and cognitive decline in old age. If we can perfect gene editing technologies, is this mutation one that we should be regularly introducing into embryos? In this article, I explore the potential for using gene editing as a way to prevent Alzheimer’s disease in future generations. Alzheimer’s Disease: Medicine’s Greatest Challenge in the 21st Century Can gene editing be the missing piece in the battle against Alzheimer’s? (Source: bostonbiotech.org) I chose to assess the benefit of germline gene editing in the context of Alzheimer’s disease because this disease is one of the biggest challenges medicine faces in the 21st century. Alzheimer’s disease is a chronic neurodegenerative disease responsible for the majority of the cases of dementia in the elderly. The disease symptoms begins with short term memory loss and causes more severe symptoms – problems with language, disorientation, mood swings, behavioral issues – as it progresses, eventually leading to the loss of bodily functions and death. Because of the dementia the disease causes, Alzheimer’s patients require a great deal of care, and the world spends ~1% of its total GDP on caring for those with Alzheimer’s and related disorders. Because the prevalence of the disease increases with age, the situation will worsen as life expectancies around the globe increase: worldwide cases of Alzheimer’s are expected to grow from 35 million today to over 115 million by 2050.

Despite much research, the exact causes of Alzheimer’s disease remains poorly understood. The disease seems to be related to the accumulation of plaques made of amyloid-β peptides that form on the outside of neurons, as well as the formation of tangles of the protein tau inside of neurons. Although many efforts have been made to target amyloid-β or the enzymes involved in its formation, we have so far been unsuccessful at finding any treatment that stops the disease or reverses its progress. Some researchers believe that most attempts at treating Alzheimer’s have failed because, by the time a patient shows symptoms, the disease has already progressed past the point of no return.

While research towards a cure continues, researchers have sought effective ways to prevent Alzheimer’s disease. Although some studies show that mental and physical exercise may lower ones risk of Alzheimer’s disease, approximately 60-80% of the risk for Alzheimer’s disease appears to be genetic. Thus, if we’re serious about prevention, we may have to act at the genetic level. And because the brain is difficult to access surgically for gene therapy in adults, this means using gene editing on embryos.

Reference https://www.physicsforums.com/insights/can-gene-editing-eliminate-alzheimers-disease/


Utilising CRISPR to Generate Predictive Disease Models: a Case Study in Neurodegenerative Disorders

Dr. Bhuvaneish.T. Selvaraj  – Scottish Centre for Regenerative Medicine


  • Introducing the latest developments in predictive model generation
  • Discover how CRISPR is being used to develop disease models to study and treat neurodegenerative disorders
  • In depth Q&A session to answer your most pressing questions


Turning On Genes, Systematically, with CRISPR/Cas9



Scientists based at MIT assert that they can reliably turn on any gene of their choosing in living cells. [Feng Zhang and Steve Dixon]  http://www.genengnews.com/media/images/GENHighlight/Dec12_2014_CRISPRCas9GeneActivationSystem7838101231.jpg

With the latest CRISPR/Cas9 advance, the exhortation “turn on, tune in, drop out” comes to mind. The CRISPR/Cas9 gene-editing system was already a well-known means of “tuning in” (inserting new genes) and “dropping out” (knocking out genes). But when it came to “turning on” genes, CRISPR/Cas9 had little potency. That is, it had demonstrated only limited success as a way to activate specific genes.

A new CRISPR/Cas9 approach, however, appears capable of activating genes more effectively than older approaches. The new approach may allow scientists to more easily determine the function of individual genes, according to Feng Zhang, Ph.D., a researcher at MIT and the Broad Institute. Dr. Zhang and colleagues report that the new approach permits multiplexed gene activation and rapid, large-scale studies of gene function.

The new technique was introduced in the December 10 online edition of Nature, in an article entitled, “Genome-scale transcriptional activation by an engineered CRISPR-Cas9 complex.” The article describes how Dr. Zhang, along with the University of Tokyo’s Osamu Nureki, Ph.D., and Hiroshi Nishimasu, Ph.D., overhauled the CRISPR/Cas9 system. The research team based their work on their analysis (published earlier this year) of the structure formed when Cas9 binds to the guide RNA and its target DNA. Specifically, the team used the structure’s 3D shape to rationally improve the system.

In previous efforts to revamp CRISPR/Cas9 for gene activation purposes, scientists had tried to attach the activation domains to either end of the Cas9 protein, with limited success. From their structural studies, the MIT team realized that two small loops of the RNA guide poke out from the Cas9 complex and could be better points of attachment because they allow the activation domains to have more flexibility in recruiting transcription machinery.

Using their revamped system, the researchers activated about a dozen genes that had proven difficult or impossible to turn on using the previous generation of Cas9 activators. Each gene showed at least a twofold boost in transcription, and for many genes, the researchers found multiple orders of magnitude increase in activation.

After investigating single-guide RNA targeting rules for effective transcriptional activation, demonstrating multiplexed activation of 10 genes simultaneously, and upregulating long intergenic noncoding RNA transcripts, the research team decided to undertake a large-scale screen. This screen was designed to identify genes that confer resistance to a melanoma drug called PLX-4720.

“We … synthesized a library consisting of 70,290 guides targeting all human RefSeq coding isoforms to screen for genes that, upon activation, confer resistance to a BRAF inhibitor,” wrote the authors of the Nature paper. “The top hits included genes previously shown to be able to confer resistance, and novel candidates were validated using individual [single-guide RNA] and complementary DNA overexpression.”

A gene signature based on the top screening hits, the authors added, correlated with a gene expression signature of BRAF inhibitor resistance in cell lines and patient-derived samples. It was also suggested that large-scale screens such as the one demonstrated in the current study could help researchers discover new cancer drugs that prevent tumors from becoming resistant.

More at –  http://www.genengnews.com/gen-news-highlights/turning-on-genes-systematically-with-crispr-cas9/81250697/


Susceptibility and modifier genes in Portuguese transthyretin V30M amyloid polyneuropathy: complexity in a single-gene disease
Miguel L. Soares1,2, Teresa Coelho3,6, Alda Sousa4,5, …, Maria Joa˜o Saraiva2,5 and Joel N. Buxbaum1
Human Molec Gen 2005; 14(4): 543–553   http://dx.doi.org:/10.1093/hmg/ddi051

Familial amyloid polyneuropathy type I is an autosomal dominant disorder caused by mutations in the transthyretin (TTR ) gene; however, carriers of the same mutation exhibit variability in penetrance and clinical expression. We analyzed alleles of candidate genes encoding non-fibrillar components of TTR amyloid deposits and a molecule metabolically interacting with TTR [retinol-binding protein (RBP)], for possible associations with age of disease onset and/or susceptibility in a Portuguese population sample with the TTR V30M mutation and unrelated controls. We show that the V30M carriers represent a distinct subset of the Portuguese population. Estimates of genetic distance indicated that the controls and the classical onset group were furthest apart, whereas the late-onset group appeared to differ from both. Importantly, the data also indicate that genetic interactions among the multiple loci evaluated, rather than single-locus effects, are more likely to determine differences in the age of disease onset. Multifactor dimensionality reduction indicated that the best genetic model for classical onset group versus controls involved the APCS gene, whereas for late-onset cases, one APCS variant (APCSv1) and two RBP variants (RBPv1 and RBPv2) are involved. Thus, although the TTR V30M mutation is required for the disease in Portuguese patients, different genetic factors may govern the age of onset, as well as the occurrence of anticipation.

Autosomal dominant disorders may vary in expression even within a given kindred. The basis of this variability is uncertain and can be attributed to epigenetic factors, environment or epistasis. We have studied familial amyloid polyneuropathy (FAP), an autosomal dominant disorder characterized by peripheral sensorimotor and autonomic neuropathy. It exhibits variation in cardiac, renal, gastrointestinal and ocular involvement, as well as age of onset. Over 80 missense mutations in the transthyretin gene (TTR ) result in autosomal dominant disease http://www.ibmc.up.pt/~mjsaraiv/ttrmut.html). The presence of deposits consisting entirely of wild-type TTR molecules in the hearts of 10– 25% of individuals over age 80 reveals its inherent in vivo amyloidogenic potential (1).

FAP was initially described in Portuguese (2) where, until recently, the TTR V30M has been the only pathogenic mutation associated with the disease (3,4). Later reports identified the same mutation in Swedish and Japanese families (5,6). The disorder has since been recognized in other European countries and in North American kindreds in association with V30M, as well as other mutations (7).

TTR V30M produces disease in only 5–10% of Swedish carriers of the allele (8), a much lower degree of penetrance than that seen in Portuguese (80%) (9) or in Japanese with the same mutation. The actual penetrance in Japanese carriers has not been formally established, but appears to resemble that seen in Portuguese. Portuguese and Japanese carriers show considerable variation in the age of clinical onset (10,11). In both populations, the first symptoms had originally been described as typically occurring before age 40 (so-called ‘classical’ or early-onset); however, in recent years, more individuals developing symptoms late in life have been identified (11,12). Hence, present data indicate that the distribution of the age of onset in Portuguese is continuous, but asymmetric with a mean around age 35 and a long tail into the older age group (Fig. 1) (9,13). Further, DNA testing in Portugal has identified asymptomatic carriers over age 70 belonging to a subset of very late-onset kindreds in whose descendants genetic anticipation is frequent. The molecular basis of anticipation in FAP, which is not mediated by trinucleotide repeat expansions in the TTR or any other gene (14), remains elusive.

Variation in penetrance, age of onset and clinical features are hallmarks of many autosomal dominant disorders including the human TTR amyloidoses (7). Some of these clearly reflect specific biological effects of a particular mutation or a class of mutants. However, when such phenotypic variability is seen with a single mutation in the gene encoding the same protein, it suggests an effect of modifying genetic loci and/or environmental factors contributing differentially to the course of disease. We have chosen to examine age of onset as an example of a discrete phenotypic variation in the presence of the particular autosomal dominant disease-associated mutation TTR V30M. Although the role of environmental factors cannot be excluded, the existence of modifier genes involved in TTR amyloidogenesis is an attractive hypothesis to explain the phenotypic variability in FAP. ….

ATTR (TTR amyloid), like all amyloid deposits, contains several molecular components, in addition to the quantitatively dominant fibril-forming amyloid protein, including heparan sulfate proteoglycan 2 (HSPG2 or perlecan), SAP, a plasma glycoprotein of the pentraxin family (encoded by the APCS gene) that undergoes specific calcium-dependent binding to all types of amyloid fibrils, and apolipoprotein E (ApoE), also found in all amyloid deposits (15). The ApoE4 isoform is associated with an increased frequency and earlier onset of Alzheimer’s disease (Ab), the most common form of brain amyloid, whereas the ApoE2 isoform appears to be protective (16). ApoE variants could exert a similar modulatory effect in the onset of FAP, although early studies on a limited number of patients suggested this was not the case (17).

In at least one instance of senile systemic amyloidosis, small amounts of AA-related material were found in TTR deposits (18). These could reflect either a passive co-aggregation or a contributory involvement of protein AA, encoded by the serum amyloid A (SAA ) genes and the main component of secondary (reactive) amyloid fibrils, in the formation of ATTR.

Retinol-binding protein (RBP), the serum carrier of vitamin A, circulates in plasma bound to TTR. Vitamin A-loaded RBP and L-thyroxine, the two natural ligands of TTR, can act alone or synergistically to inhibit the rate and extent of TTR fibrillogenesis in vitro, suggesting that RBP may influence the course of FAP pathology in vivo (19). We have analyzed coding and non-coding sequence polymorphisms in the RBP4 (serum RBP, 10q24), HSPG2 (1p36.1), APCS (1q22), APOE (19q13.2), SAA1 and SAA2 (11p15.1) genes with the goal of identifying chromosomes carrying common and functionally significant variants. At the time these studies were performed, the full human genome sequence was not completed and systematic singlenucleotide polymorphism (SNP) analyses were not available for any of the suspected candidate genes. We identified new SNPs in APCS and RBP4 and utilized polymorphisms in SAA, HSPG2 and APOE that had already been characterized and shown to have potential pathophysiologic significance in other disorders (16,20–22). The genotyping data were analyzed for association with the presence of the V30M amyloidogenic allele (FAP patients versus controls) and with the age of onset (classical- versus late-onset patients). Multilocus analyses were also performed to examine the effects of simultaneous contributions of the six loci for determining the onset of the first symptoms.  …..

The potential for different underlying models for classical and late onset is supported by the MDR analysis, which produces two distinct models when comparing each class with the controls. One could view the two onset classes as unique diseases. If this is the case, then the failure to detect a single predictive genetic model is consistent with two related, but different, diseases. This is exactly what would be expected in such a case of genetic heterogeneity (28). Using this approach, a major gene effect can be viewed as a necessary, but not sufficient, condition to explain the course of the disease. Analyzing the cases but omitting from the analysis of phenotype the necessary allele, in this case TTR V30M, can then reveal a variety of important modifiers that are distinct between the phenotypes.

The significant comparisons obtained in our study cohort indicate that the combined effects mainly result from two and three-locus interactions involving all loci except SAA1 and SAA2 for susceptibility to disease. A considerable number of four-site combinations modulate the age of onset with SAA1 appearing in a majority of significant combinations in late-onset disease, perhaps indicating a greater role of the SAA variants in the age of onset of FAP.

The correlation between genotype and phenotype in socalled simple Mendelian disorders is often incomplete, as only a subset of all mutations can reliably predict specific phenotypes (34). This is because non-allelic genetic variations and/or environmental influences underlie these disorders whose phenotypes behave as complex traits. A few examples include the identification of the role of homozygozity for the SAA1.1 allele in conferring the genetic susceptibility to renal amyloidosis in FMF (20) and the association of an insertion/deletion polymorphism in the ACE gene with disease severity in familial hypertrophic cardiomyopathy (35). In these disorders, the phenotypes arise from mutations in MEFV and b-MHC, but are modulated by independently inherited genetic variation. In this report, we show that interactions among multiple genes, whose products are confirmed or putative constituents of ATTR deposits, or metabolically interact with TTR, modulate the onset of the first symptoms and predispose individuals to disease in the presence of the V30M mutation in TTR. The exact nature of the effects identified here requires further study with potential application in the development of genetic screening with prognostic value pertaining to the onset of disease in the TTR V30M carriers.

If the effects of additional single or interacting genes dictate the heterogeneity of phenotype, as reflected in variability of onset and clinical expression (with the same TTR mutation), the products encoded by alleles at such loci could contribute to the process of wild-type TTR deposition in elderly individuals without a mutation (senile systemic amyloidosis), a phenomenon not readily recognized as having a genetic basis because of the insensitivity of family history in the elderly.


Safety and Efficacy of RNAi Therapy for Transthyretin Amyloidosis

Coelho T, Adams D, Silva A, et al.
N Engl J Med 2013;369:819-29.    http://dx.doi.org:/10.1056/NEJMoa1208760

Transthyretin amyloidosis is caused by the deposition of hepatocyte-derived transthyretin amyloid in peripheral nerves and the heart. A therapeutic approach mediated by RNA interference (RNAi) could reduce the production of transthyretin.

Methods We identified a potent antitransthyretin small interfering RNA, which was encapsulated in two distinct first- and second-generation formulations of lipid nanoparticles, generating ALN-TTR01 and ALN-TTR02, respectively. Each formulation was studied in a single-dose, placebo-controlled phase 1 trial to assess safety and effect on transthyretin levels. We first evaluated ALN-TTR01 (at doses of 0.01 to 1.0 mg per kilogram of body weight) in 32 patients with transthyretin amyloidosis and then evaluated ALN-TTR02 (at doses of 0.01 to 0.5 mg per kilogram) in 17 healthy volunteers.

Results Rapid, dose-dependent, and durable lowering of transthyretin levels was observed in the two trials. At a dose of 1.0 mg per kilogram, ALN-TTR01 suppressed transthyretin, with a mean reduction at day 7 of 38%, as compared with placebo (P=0.01); levels of mutant and nonmutant forms of transthyretin were lowered to a similar extent. For ALN-TTR02, the mean reductions in transthyretin levels at doses of 0.15 to 0.3 mg per kilogram ranged from 82.3 to 86.8%, with reductions of 56.6 to 67.1% at 28 days (P<0.001 for all comparisons). These reductions were shown to be RNAi mediated. Mild-to-moderate infusion-related reactions occurred in 20.8% and 7.7% of participants receiving ALN-TTR01 and ALN-TTR02, respectively.

ALN-TTR01 and ALN-TTR02 suppressed the production of both mutant and nonmutant forms of transthyretin, establishing proof of concept for RNAi therapy targeting messenger RNA transcribed from a disease-causing gene.


Alnylam May Seek Approval for TTR Amyloidosis Rx in 2017 as Other Programs Advance


Officials from Alnylam Pharmaceuticals last week provided updates on the two drug candidates from the company’s flagship transthyretin-mediated amyloidosis program, stating that the intravenously delivered agent patisiran is proceeding toward a possible market approval in three years, while a subcutaneously administered version called ALN-TTRsc is poised to enter Phase III testing before the end of the year.

Meanwhile, Alnylam is set to advance a handful of preclinical therapies into human studies in short order, including ones for complement-mediated diseases, hypercholesterolemia, and porphyria.

The officials made their comments during a conference call held to discuss Alnylam’s second-quarter financial results.

ATTR is caused by a mutation in the TTR gene, which normally produces a protein that acts as a carrier for retinol binding protein and is characterized by the accumulation of amyloid deposits in various tissues. Alnylam’s drugs are designed to silence both the mutant and wild-type forms of TTR.

Patisiran, which is delivered using lipid nanoparticles developed by Tekmira Pharmaceuticals, is currently in a Phase III study in patients with a form of ATTR called familial amyloid polyneuropathy (FAP) affecting the peripheral nervous system. Running at over 20 sites in nine countries, that study is set to enroll up to 200 patients and compare treatment to placebo based on improvements in neuropathy symptoms.

According to Alnylam Chief Medical Officer Akshay Vaishnaw, Alnylam expects to have final data from the study in two to three years, which would put patisiran on track for a new drug application filing in 2017.

Meanwhile, ALN-TTRsc, which is under development for a version of ATTR that affects cardiac tissue called familial amyloidotic cardiomyopathy (FAC) and uses Alnylam’s proprietary GalNAc conjugate delivery technology, is set to enter Phase III by year-end as Alnylam holds “active discussions” with US and European regulators on the design of that study, CEO John Maraganore noted during the call.

In the interim, Alnylam continues to enroll patients in a pilot Phase II study of ALN-TTRsc, which is designed to test the drug’s efficacy for FAC or senile systemic amyloidosis (SSA), a condition caused by the idiopathic accumulation of wild-type TTR protein in the heart.

Based on “encouraging” data thus far, Vaishnaw said that Alnylam has upped the expected enrollment in this study to 25 patients from 15. Available data from the trial is slated for release in November, he noted, stressing that “any clinical endpoint result needs to be considered exploratory given the small sample size and the very limited duration of treatment of only six weeks” in the trial.

Vaishnaw added that an open-label extension (OLE) study for patients in the ALN-TTRsc study will kick off in the coming weeks, allowing the company to gather long-term dosing tolerability and clinical activity data on the drug.

Enrollment in an OLE study of patisiran has been completed with 27 patients, he said, and, “as of today, with up to nine months of therapy … there have been no study drug discontinuations.” Clinical endpoint data from approximately 20 patients in this study will be presented at the American Neurological Association meeting in October.

As part of its ATTR efforts, Alnylam has also been conducting natural history of disease studies in both FAP and FAC patients. Data from the 283-patient FAP study was presented earlier this year and showed a rapid progression in neuropathy impairment scores and a high correlation of this measurement with disease severity.

During last week’s conference call, Vaishnaw said that clinical endpoint and biomarker data on about 400 patients with either FAC or SSA have already been collected in a nature history study on cardiac ATTR. Maraganore said that these findings would likely be released sometime next year.

Alnylam Presents New Phase II, Preclinical Data from TTR Amyloidosis Programs


Amyloid disease drug approved

Nature Biotechnology 2012; (3http://dx.doi.org:/10.1038/nbt0212-121b

The first medication for a rare and often fatal protein misfolding disorder has been approved in Europe. On November 16, the E gave a green light to Pfizer’s Vyndaqel (tafamidis) for treating transthyretin amyloidosis in adult patients with stage 1 polyneuropathy symptoms. [Jeffery Kelly, La Jolla]


Safety and Efficacy of RNAi Therapy for Transthyretin …


The New England Journal of Medicine

Aug 29, 2013 – Transthyretin amyloidosis is caused by the deposition of hepatocyte-derived transthyretin amyloid in peripheral nerves and the heart.


Alnylam’s RNAi therapy targets amyloid disease

Ken Garber
Nature Biotechnology 2015; 33(577)    http://dx.doi.org:/10.1038/nbt0615-577a

RNA interference’s silencing of target genes could result in potent therapeutics.


The most clinically advanced RNA interference (RNAi) therapeutic achieved a milestone in April when Alnylam Pharmaceuticals in Cambridge, Massachusetts, reported positive results for patisiran, a small interfering RNA (siRNA) oligonucleotide targeting transthyretin for treating familial amyloidotic polyneuropathy (FAP).  …

  1. Analysis of 589,306 genomes identifies individuals resilient to severe Mendelian childhood diseases

Nature Biotechnology 11 April 2016

  1. CRISPR-Cas systems for editing, regulating and targeting genomes

Nature Biotechnology 02 March 2014

  1. Near-optimal probabilistic RNA-seq quantification

Nature Biotechnology 04 April 2016


Translational Neuroscience: Toward New Therapies


Karoly Nikolich, ‎Steven E. Hyman – 2015 – ‎Medical

Tafamidis for Transthyretin Familial Amyloid Polyneuropathy: A Randomized, Controlled Trial. … Multiplex Genome Engineering Using CRISPR/Cas Systems.


Is CRISPR a Solution to Familial Amyloid Polyneuropathy?

Author and Curator: Larry H. Bernstein, MD, FCAP

Originally published as




FAP is characterized by the systemic deposition of amyloidogenic variants of the transthyretin protein, especially in the peripheral nervous system, causing a progressive sensory and motor polyneuropathy.

FAP is caused by a mutation of the TTR gene, located on human chromosome 18q12.1-11.2.[5] A replacement of valine by methionine at position 30 (TTR V30M) is the mutation most commonly found in FAP.[1] The variant TTR is mostly produced by the liver.[citation needed] The transthyretin protein is a tetramer.    ….



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