Archive for the ‘Biochemical pathways’ Category

Live Notes, Real Time Conference Coverage AACR 2020 #AACR20: Tuesday June 23, 2020 Noon-2:45 Educational Sessions

Live Notes, Real Time Conference Coverage AACR 2020: Tuesday June 23, 2020 Noon-2:45 Educational Sessions

Reporter: Stephen J. Williams, PhD

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Presidential Address

Elaine R Mardis, William N Hait


Welcome and introduction

William N Hait


Improving diagnostic yield in pediatric cancer precision medicine

Elaine R Mardis
  • Advent of genomics have revolutionized how we diagnose and treat lung cancer
  • We are currently needing to understand the driver mutations and variants where we can personalize therapy
  • PD-L1 and other checkpoint therapy have not really been used in pediatric cancers even though CAR-T have been successful
  • The incidence rates and mortality rates of pediatric cancers are rising
  • Large scale study of over 700 pediatric cancers show cancers driven by epigenetic drivers or fusion proteins. Need for transcriptomics.  Also study demonstrated that we have underestimated germ line mutations and hereditary factors.
  • They put together a database to nominate patients on their IGM Cancer protocol. Involves genetic counseling and obtaining germ line samples to determine hereditary factors.  RNA and protein are evaluated as well as exome sequencing. RNASeq and Archer Dx test to identify driver fusions
  • PECAN curated database from St. Jude used to determine driver mutations. They use multiple databases and overlap within these databases and knowledge base to determine or weed out false positives
  • They have used these studies to understand the immune infiltrate into recurrent cancers (CytoCure)
  • They found 40 germline cancer predisposition genes, 47 driver somatic fusion proteins, 81 potential actionable targets, 106 CNV, 196 meaningful somatic driver mutations



Tuesday, June 23

12:00 PM – 12:30 PM EDT

Awards and Lectures

NCI Director’s Address

Norman E Sharpless, Elaine R Mardis


Introduction: Elaine Mardis


NCI Director Address: Norman E Sharpless
  • They are functioning well at NCI with respect to grant reviews, research, and general functions in spite of the COVID pandemic and the massive demonstrations on also focusing on the disparities which occur in cancer research field and cancer care
  • There are ongoing efforts at NCI to make a positive difference in racial injustice, diversity in the cancer workforce, and for patients as well
  • Need a diverse workforce across the cancer research and care spectrum
  • Data show that areas where the clinicians are successful in putting African Americans on clinical trials are areas (geographic and site specific) where health disparities are narrowing
  • Grants through NCI new SeroNet for COVID-19 serologic testing funded by two RFAs through NIAD (RFA-CA-30-038 and RFA-CA-20-039) and will close on July 22, 2020


Tuesday, June 23

12:45 PM – 1:46 PM EDT

Virtual Educational Session

Immunology, Tumor Biology, Experimental and Molecular Therapeutics, Molecular and Cellular Biology/Genetics

Tumor Immunology and Immunotherapy for Nonimmunologists: Innovation and Discovery in Immune-Oncology

This educational session will update cancer researchers and clinicians about the latest developments in the detailed understanding of the types and roles of immune cells in tumors. It will summarize current knowledge about the types of T cells, natural killer cells, B cells, and myeloid cells in tumors and discuss current knowledge about the roles these cells play in the antitumor immune response. The session will feature some of the most promising up-and-coming cancer immunologists who will inform about their latest strategies to harness the immune system to promote more effective therapies.

Judith A Varner, Yuliya Pylayeva-Gupta



Judith A Varner
New techniques reveal critical roles of myeloid cells in tumor development and progression
  • Different type of cells are becoming targets for immune checkpoint like myeloid cells
  • In T cell excluded or desert tumors T cells are held at periphery so myeloid cells can infiltrate though so macrophages might be effective in these immune t cell naïve tumors, macrophages are most abundant types of immune cells in tumors
  • CXCLs are potential targets
  • PI3K delta inhibitors,
  • Reduce the infiltrate of myeloid tumor suppressor cells like macrophages
  • When should we give myeloid or T cell therapy is the issue
Judith A Varner
Novel strategies to harness T-cell biology for cancer therapy
Positive and negative roles of B cells in cancer
Yuliya Pylayeva-Gupta
New approaches in cancer immunotherapy: Programming bacteria to induce systemic antitumor immunity



Tuesday, June 23

12:45 PM – 1:46 PM EDT

Virtual Educational Session

Cancer Chemistry

Chemistry to the Clinic: Part 2: Irreversible Inhibitors as Potential Anticancer Agents

There are numerous examples of highly successful covalent drugs such as aspirin and penicillin that have been in use for a long period of time. Despite historical success, there was a period of reluctance among many to purse covalent drugs based on concerns about toxicity. With advances in understanding features of a well-designed covalent drug, new techniques to discover and characterize covalent inhibitors, and clinical success of new covalent cancer drugs in recent years, there is renewed interest in covalent compounds. This session will provide a broad look at covalent probe compounds and drug development, including a historical perspective, examination of warheads and electrophilic amino acids, the role of chemoproteomics, and case studies.

Benjamin F Cravatt, Richard A. Ward, Sara J Buhrlage


Discovering and optimizing covalent small-molecule ligands by chemical proteomics

Benjamin F Cravatt
  • Multiple approaches are being investigated to find new covalent inhibitors such as: 1) cysteine reactivity mapping, 2) mapping cysteine ligandability, 3) and functional screening in phenotypic assays for electrophilic compounds
  • Using fluorescent activity probes in proteomic screens; have broad useability in the proteome but can be specific
  • They screened quiescent versus stimulated T cells to determine reactive cysteines in a phenotypic screen and analyzed by MS proteomics (cysteine reactivity profiling); can quantitate 15000 to 20,000 reactive cysteines
  • Isocitrate dehydrogenase 1 and adapter protein LCP-1 are two examples of changes in reactive cysteines they have seen using this method
  • They use scout molecules to target ligands or proteins with reactive cysteines
  • For phenotypic screens they first use a cytotoxic assay to screen out toxic compounds which just kill cells without causing T cell activation (like IL10 secretion)
  • INTERESTINGLY coupling these MS reactive cysteine screens with phenotypic screens you can find NONCANONICAL mechanisms of many of these target proteins (many of the compounds found targets which were not predicted or known)

Electrophilic warheads and nucleophilic amino acids: A chemical and computational perspective on covalent modifier

The covalent targeting of cysteine residues in drug discovery and its application to the discovery of Osimertinib

Richard A. Ward
  • Cysteine activation: thiolate form of cysteine is a strong nucleophile
  • Thiolate form preferred in polar environment
  • Activation can be assisted by neighboring residues; pKA will have an effect on deprotonation
  • pKas of cysteine vary in EGFR
  • cysteine that are too reactive give toxicity while not reactive enough are ineffective


Accelerating drug discovery with lysine-targeted covalent probes


Tuesday, June 23

12:45 PM – 2:15 PM EDT

Virtual Educational Session

Molecular and Cellular Biology/Genetics

Virtual Educational Session

Tumor Biology, Immunology

Metabolism and Tumor Microenvironment

This Educational Session aims to guide discussion on the heterogeneous cells and metabolism in the tumor microenvironment. It is now clear that the diversity of cells in tumors each require distinct metabolic programs to survive and proliferate. Tumors, however, are genetically programmed for high rates of metabolism and can present a metabolically hostile environment in which nutrient competition and hypoxia can limit antitumor immunity.

Jeffrey C Rathmell, Lydia Lynch, Mara H Sherman, Greg M Delgoffe


T-cell metabolism and metabolic reprogramming antitumor immunity

Jeffrey C Rathmell


Jeffrey C Rathmell

Metabolic functions of cancer-associated fibroblasts

Mara H Sherman

Tumor microenvironment metabolism and its effects on antitumor immunity and immunotherapeutic response

Greg M Delgoffe
  • Multiple metabolites, reactive oxygen species within the tumor microenvironment; is there heterogeneity within the TME metabolome which can predict their ability to be immunosensitive
  • Took melanoma cells and looked at metabolism using Seahorse (glycolysis): and there was vast heterogeneity in melanoma tumor cells; some just do oxphos and no glycolytic metabolism (inverse Warburg)
  • As they profiled whole tumors they could separate out the metabolism of each cell type within the tumor and could look at T cells versus stromal CAFs or tumor cells and characterized cells as indolent or metabolic
  • T cells from hyerglycolytic tumors were fine but from high glycolysis the T cells were more indolent
  • When knock down glucose transporter the cells become more glycolytic
  • If patient had high oxidative metabolism had low PDL1 sensitivity
  • Showed this result in head and neck cancer as well
  • Metformin a complex 1 inhibitor which is not as toxic as most mito oxphos inhibitors the T cells have less hypoxia and can remodel the TME and stimulate the immune response
  • Metformin now in clinical trials
  • T cells though seem metabolically restricted; T cells that infiltrate tumors are low mitochondrial phosph cells
  • T cells from tumors have defective mitochondria or little respiratory capacity
  • They have some preliminary findings that metabolic inhibitors may help with CAR-T therapy

Obesity, lipids and suppression of anti-tumor immunity

Lydia Lynch
  • Hypothesis: obesity causes issues with anti tumor immunity
  • Less NK cells in obese people; also produce less IFN gamma
  • RNASeq on NOD mice; granzymes and perforins at top of list of obese downregulated
  • Upregulated genes that were upregulated involved in lipid metabolism
  • All were PPAR target genes
  • NK cells from obese patients takes up palmitate and this reduces their glycolysis but OXPHOS also reduced; they think increased FFA basically overloads mitochondria
  • PPAR alpha gamma activation mimics obesity



Tuesday, June 23

12:45 PM – 2:45 PM EDT

Virtual Educational Session

Clinical Research Excluding Trials

The Evolving Role of the Pathologist in Cancer Research

Long recognized for their role in cancer diagnosis and prognostication, pathologists are beginning to leverage a variety of digital imaging technologies and computational tools to improve both clinical practice and cancer research. Remarkably, the emergence of artificial intelligence (AI) and machine learning algorithms for analyzing pathology specimens is poised to not only augment the resolution and accuracy of clinical diagnosis, but also fundamentally transform the role of the pathologist in cancer science and precision oncology. This session will discuss what pathologists are currently able to achieve with these new technologies, present their challenges and barriers, and overview their future possibilities in cancer diagnosis and research. The session will also include discussions of what is practical and doable in the clinic for diagnostic and clinical oncology in comparison to technologies and approaches primarily utilized to accelerate cancer research.


Jorge S Reis-Filho, Thomas J Fuchs, David L Rimm, Jayanta Debnath


Tuesday, June 23

12:45 PM – 2:45 PM EDT


High-dimensional imaging technologies in cancer research

David L Rimm

  • Using old methods and new methods; so cell counting you use to find the cells then phenotype; with quantification like with Aqua use densitometry of positive signal to determine a threshold to determine presence of a cell for counting
  • Hiplex versus multiplex imaging where you have ten channels to measure by cycling of flour on antibody (can get up to 20plex)
  • Hiplex can be coupled with Mass spectrometry (Imaging Mass spectrometry, based on heavy metal tags on mAbs)
  • However it will still take a trained pathologist to define regions of interest or field of desired view



Jayanta Debnath

Challenges and barriers of implementing AI tools for cancer diagnostics

Jorge S Reis-Filho

Implementing robust digital pathology workflows into clinical practice and cancer research

Jayanta Debnath

Invited Speaker

Thomas J Fuchs
  • Founder of spinout of Memorial Sloan Kettering
  • Separates AI from computational algothimic
  • Dealing with not just machines but integrating human intelligence
  • Making decision for the patients must involve human decision making as well
  • How do we get experts to do these decisions faster
  • AI in pathology: what is difficult? =è sandbox scenarios where machines are great,; curated datasets; human decision support systems or maps; or try to predict nature
  • 1) learn rules made by humans; human to human scenario 2)constrained nature 3)unconstrained nature like images and or behavior 4) predict nature response to nature response to itself
  • In sandbox scenario the rules are set in stone and machines are great like chess playing
  • In second scenario can train computer to predict what a human would predict
  • So third scenario is like driving cars
  • System on constrained nature or constrained dataset will take a long time for commuter to get to decision
  • Fourth category is long term data collection project
  • He is finding it is still finding it is still is difficult to predict nature so going from clinical finding to prognosis still does not have good predictability with AI alone; need for human involvement
  • End to end partnering (EPL) is a new way where humans can get more involved with the algorithm and assist with the problem of constrained data
  • An example of a workflow for pathology would be as follows from Campanella et al 2019 Nature Medicine: obtain digital images (they digitized a million slides), train a massive data set with highthroughput computing (needed a lot of time and big software developing effort), and then train it using input be the best expert pathologists (nature to human and unconstrained because no data curation done)
  • Led to first clinically grade machine learning system (Camelyon16 was the challenge for detecting metastatic cells in lymph tissue; tested on 12,000 patients from 45 countries)
  • The first big hurdle was moving from manually annotated slides (which was a big bottleneck) to automatically extracted data from path reports).
  • Now problem is in prediction: How can we bridge the gap from predicting humans to predicting nature?
  • With an AI system pathologist drastically improved the ability to detect very small lesions


Virtual Educational Session


Cancer Increases in Younger Populations: Where Are They Coming from?

Incidence rates of several cancers (e.g., colorectal, pancreatic, and breast cancers) are rising in younger populations, which contrasts with either declining or more slowly rising incidence in older populations. Early-onset cancers are also more aggressive and have different tumor characteristics than those in older populations. Evidence on risk factors and contributors to early-onset cancers is emerging. In this Educational Session, the trends and burden, potential causes, risk factors, and tumor characteristics of early-onset cancers will be covered. Presenters will focus on colorectal and breast cancer, which are among the most common causes of cancer deaths in younger people. Potential mechanisms of early-onset cancers and racial/ethnic differences will also be discussed.

Stacey A. Fedewa, Xavier Llor, Pepper Jo Schedin, Yin Cao

Cancers that are and are not increasing in younger populations

Stacey A. Fedewa


  • Early onset cancers, pediatric cancers and colon cancers are increasing in younger adults
  • Younger people are more likely to be uninsured and these are there most productive years so it is a horrible life event for a young adult to be diagnosed with cancer. They will have more financial hardship and most (70%) of the young adults with cancer have had financial difficulties.  It is very hard for women as they are on their childbearing years so additional stress
  • Types of early onset cancer varies by age as well as geographic locations. For example in 20s thyroid cancer is more common but in 30s it is breast cancer.  Colorectal and testicular most common in US.
  • SCC is decreasing by adenocarcinoma of the cervix is increasing in women’s 40s, potentially due to changing sexual behaviors
  • Breast cancer is increasing in younger women: maybe etiologic distinct like triple negative and larger racial disparities in younger African American women
  • Increased obesity among younger people is becoming a factor in this increasing incidence of early onset cancers



Other Articles on this Open Access  Online Journal on Cancer Conferences and Conference Coverage in Real Time Include

Press Coverage

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Symposium: New Drugs on the Horizon Part 3 12:30-1:25 PM

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on NCI Activities: COVID-19 and Cancer Research 5:20 PM

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Evaluating Cancer Genomics from Normal Tissues Through Metastatic Disease 3:50 PM

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Novel Targets and Therapies 2:35 PM


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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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Celiac Disease Breakthrough: (1) 472 genes regulated differently in organoids reflecting celiac disease than in non-celiac control organoids (2) bio-products derived from gut microorganisms can be employed to modify the epithelial response to gluten, a finding that could lead to future treatment strategies.


Reporter: Aviva Lev-Ari, PhD, RN

“These results confirm our hypothesis that genes and exposure to gluten are necessary but not sufficient, since changes in both the composition and function of the gut microbiome are also needed to switch from genetic predisposition to clinical outcome, as shown by our data,” said Alessio Fasano, HMS professor of pediatrics at Mass General, director of MIBRC and co-senior author of the paper.




Image Source: iStock/wildpixel

Article OPEN Published: 

Human gut derived-organoids provide model to study gluten response and effects of microbiota-derived molecules in celiac disease

Scientific Reports volume 9, Article number: 7029 (2019Download Citation


Celiac disease (CD) is an immune-mediated disorder triggered by gluten exposure. The contribution of the adaptive immune response to CD pathogenesis has been extensively studied, but the absence of valid experimental models has hampered our understanding of the early steps leading to loss of gluten tolerance. Using intestinal organoids developed from duodenal biopsies from both non-celiac (NC) and celiac (CD) patients, we explored the contribution of gut epithelium to CD pathogenesis and the role of microbiota-derived molecules in modulating the epithelium’s response to gluten. When compared to NC, RNA sequencing of CD organoids revealed significantly altered expression of genes associated with gut barrier, innate immune response, and stem cell functions. Monolayers derived from CD organoids exposed to gliadin showed increased intestinal permeability and enhanced secretion of pro-inflammatory cytokines compared to NC controls. Microbiota-derived bioproducts butyrate, lactate, and polysaccharide A improved barrier function and reduced gliadin-induced cytokine secretion. We concluded that: (1) patient-derived organoids faithfully express established and newly identified molecular signatures characteristic of CD. (2) microbiota-derived bioproducts can be used to modulate the epithelial response to gluten. Finally, we validated the use of patient-derived organoids monolayers as a novel tool for the study of CD.

Mass. General researchers develop 3D “mini-gut” model to study autoimmune response to gluten in celiac and non-celiac patient tissue

Gene expression of intestinal organoids reflects functional differences found in celiac disease

In pursuit of a novel tool for the research and treatment of celiac disease, scientists at the Mucosal Immunology and Biology Research Center (MIBRC) at Massachusetts General Hospital (MGH) have validated the use of intestinal organoids. These three-dimensional tissue cultures are miniature, simplified versions of the intestine produced in vitro. Taking tissue from duodenal biopsies of celiac and non-celiac patients, researchers created the “mini-guts” to explore how the gut epithelium and microbiota-derived molecules respond to gluten, a complex class of proteins found in wheat and other grains.

“We currently have no animal model that can recapitulate the response to gluten that we see in humans,” says Stefania Senger, PhD, co-senior author of the study published in Scientific Reports this week. “Using this human tissue model, we observed that intestinal organoids express the same molecular markers as actual epithelium in the celiac tissue, and the signature gene expression reflects the functional differences that occur when epithelia of celiac disease patients are exposed to gliadin.” Gliadin and glutenin proteins are main components of gluten.

Celiac disease is triggered when genetically predisposed individuals consume gluten. The condition affects approximately 1 percent of the U.S. population. Based on current data, the onset of celiac disease is thought to be preceded by the release of the protein zonulin, which is triggered by the activation of undigested gliadin to induce an autoimmune response. This leads to increased intestinal permeability and a disrupted barrier function. Novel evidence suggests that the microorganisms in the gastrointestinal tract may play a role in the onset of celiac disease.

Earlier studies from the MIBRC group and others have shown that human organoids “retain a gene expression that recapitulates the expression of the tissue of origin, including a diseased state,” the authors write. Through RNA sequencing, the new findings validate the organoid model as a “faithful in vitro model for celiac disease,” Senger says.
Using whole-transcriptome analysis, the researchers identified 472 genes regulated differently in organoids reflecting celiac disease than in non-celiac control organoids. These included novel genes associated with epithelial functions related to the pathogenesis of celiac disease – including gut barrier maintenance, stem cell regeneration and innate immune response. A second finding of the study shows that bioproducts derived from gut microorganisms can be employed to modify the epithelial response to gluten, a finding that could lead to future treatment strategies.

“These results confirm our hypothesis that genes and exposure to gluten are necessary but not sufficient, since changes in both the composition and function of the gut microbiome are also needed to switch from genetic predisposition to clinical outcome, as shown by our data,” says Alessio Fasano, MD, director of the Mucosal Immunology and Biology Research Center and co-senior author.

Senger adds, “We believe our observations represent a major shift in the study of celiac disease. We are confident that with adequate funding we could achieve major goals that include the development and implementation of high-throughput drug screenings to quickly identify new treatments for patients and expand the organoid repository to develop more complex models and pursue personalized treatment.”
Additional co-authors of the paper are first author Rachel Freire, PhD, along with Laura Ingano and Gloria Serena, PhD, of the MGH MIBRC; Murat Cetinbas, PhD, and Ruslan Sadreyev, PhD, MGH Department of Molecular Biology; Anthony Anselmo, PhD, formerly of MGH Molecular Biology and now with PatientsLikeMe, Cambridge, Mass.; and Anna Sapone, MD, PhD, Takeda Pharmaceuticals International. Support for the study includes National Institutes of Health grants RO1 DK104344-01A1 and 1U19 AI082655-02 and the Egan Family Foundation.




Other related articles and e-Books by LPBI Group’s Authors published on this Open Access Online Scientific Journal include the following:


Series D: e-Books on BioMedicine – Metabolomics, Immunology, Infectious Diseases

  • Metabolomics 

VOLUME 1: Metabolic Genomics and Pharmaceutics. On Amazon.com since 7/21/2015


Gluten-free Diets

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



Breakthrough Digestive Disorders Research: Conditions affecting the Gastrointestinal Tract.

Reporter: Aviva Lev-Ari, PhD, RN



Collagen-binding Molecular Chaperone HSP47: Role in Intestinal Fibrosis – colonic epithelial cells and subepithelial myofibroblasts

Curators: Larry H Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN


Expanding area of Tolerance-inducing Autoimmune Disease Therapeutics: Key Players

Reporter: Aviva Lev-Ari, PhD, RN



What is the key method to harness Inflammation to close the doors for many complex diseases?

Author and Curator: Larry H Bernstein, MD, FCAP


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Reporter and Curator: Dr. Sudipta Saha, Ph.D.


Protein kinase C (PKC) isozymes function as tumor suppressors in increasing contexts. These enzymes are crucial for a number of cellular activities, including cell survival, proliferation and migration — functions that must be carefully controlled if cells get out of control and form a tumor. In contrast to oncogenic kinases, whose function is acutely regulated by transient phosphorylation, PKC is constitutively phosphorylated following biosynthesis to yield a stable, autoinhibited enzyme that is reversibly activated by second messengers. Researchers at University of California San Diego School of Medicine found that another enzyme, called PHLPP1, acts as a “proofreader” to keep careful tabs on PKC.


The researchers discovered that in pancreatic cancer high PHLPP1 levels lead to low PKC levels, which is associated with poor patient survival. They reported that the phosphatase PHLPP1 opposes PKC phosphorylation during maturation, leading to the degradation of aberrantly active species that do not become autoinhibited. They discovered that any time an over-active PKC is inadvertently produced, the PHLPP1 “proofreader” tags it for destruction. That means the amount of PHLPP1 in patient’s cells determines his amount of PKC and it turns out those enzyme levels are especially important in pancreatic cancer.


This team of researchers reversed a 30-year paradigm when they reported evidence that PKC actually suppresses, rather than promotes, tumors. For decades before this revelation, many researchers had attempted to develop drugs that inhibit PKC as a means to treat cancer. Their study implied that anti-cancer drugs would actually need to do the opposite — boost PKC activity. This study sets the stage for clinicians to one day use a pancreatic cancer patient’s PHLPP1/PKC levels as a predictor for prognosis, and for researchers to develop new therapeutic drugs that inhibit PHLPP1 and boost PKC as a means to treat the disease.


The ratio — high PHLPP1/low PKC — correlated with poor prognoses: no pancreatic patient with low PKC in the database survived longer than five-and-a-half years. On the flip side, 50 percent of the patients with low PHLPP1/high PKC survived longer than that. While still in the earliest stages, the researchers hope that this information might one day aid pancreatic diagnostics and treatment. The researchers are next planning to screen chemical compounds to find those that inhibit PHLPP1 and restore PKC levels in low-PKC-pancreatic cancer cells in the lab. These might form the basis of a new therapeutic drug for pancreatic cancer.




















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Lesson 4 Cell Signaling And Motility: G Proteins, Signal Transduction: Curations and Articles of reference as supplemental information: #TUBiol3373

Curator: Stephen J. Williams, Ph.D.

Updated 7/15/2019

Below please find the link to the Powerpoint presentation for lesson #4 for #TUBiol3373.  The lesson first competes the discussion on G Protein Coupled Receptors, including how cells terminate cell signals.  Included are mechanisms of receptor desensitization.  Please NOTE that desensitization mechanisms like B arrestin decoupling of G proteins and receptor endocytosis occur after REPEATED and HIGH exposures to agonist.  Hydrolysis of GTP of the alpha subunit of G proteins, removal of agonist, and the action of phosphodiesterase on the second messenger (cAMP or cGMP) is what results in the downslope of the effect curve, the termination of the signal after agonist-receptor interaction.


Click below for PowerPoint of lesson 4

Powerpoint for lesson 4


Please Click below for the papers for your Group presentations

paper 1: Membrane interactions of G proteins and other related proteins

paper 2: Macaluso_et_al-2002-Journal_of_Cellular_Physiology

paper 3: Interactions of Ras proteins with the plasma membrane

paper 4: Futosi_et_al-2016-Immunological_Reviews


Please find related article on G proteins and Receptor Tyrosine Kinases on this Open Access Online Journal

G Protein–Coupled Receptor and S-Nitrosylation in Cardiac Ischemia and Acute Coronary Syndrome

Action of Hormones on the Circulation

Newer Treatments for Depression: Monoamine, Neurotrophic Factor & Pharmacokinetic Hypotheses

VEGF activation and signaling, lysine methylation, and activation of receptor tyrosine kinase


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Reporter and Curator: Dr. Sudipta Saha, Ph.D.


The relationship between gut microbial metabolism and mental health is one of the most intriguing and controversial topics in microbiome research. Bidirectional microbiota–gut–brain communication has mostly been explored in animal models, with human research lagging behind. Large-scale metagenomics studies could facilitate the translational process, but their interpretation is hampered by a lack of dedicated reference databases and tools to study the microbial neuroactive potential.


Out of all the many ways, the teeming ecosystem of microbes in a person’s gut and other tissues might affect health. But, its potential influences on the brain may be the most provocative for research. Several studies in mice had indicated that gut microbes can affect behavior, and small scale studies on human beings suggested this microbial repertoire is altered in depression. Studies by two large European groups have found that several species of gut bacteria are missing in people with depression. The researchers can’t say whether the absence is a cause or an effect of the illness, but they showed that many gut bacteria could make substances that affect the nerve cell function—and maybe the mood.


Butyrate-producing Faecalibacterium and Coprococcus bacteria were consistently associated with higher quality of life indicators. Together with DialisterCoprococcus spp. was also depleted in depression, even after correcting for the confounding effects of antidepressants. Two kinds of microbes, Coprococcus and Dialister, were missing from the microbiomes of the depressed subjects, but not from those with a high quality of life. The researchers also found the depressed people had an increase in bacteria implicated in Crohn disease, suggesting inflammation may be at fault.


Looking for something that could link microbes to mood, researchers compiled a list of 56 substances important for proper functioning of nervous system that gut microbes either produce or break down. They found, for example, that Coprococcus seems to have a pathway related to dopamine, a key brain signal involved in depression, although they have no evidence how this might protect against depression. The same microbe also makes an anti-inflammatory substance called butyrate, and increased inflammation is implicated in depression.


Still, it is very much unclear that how microbial compounds made in the gut might influence the brain. One possible channel is the vagus nerve, which links the gut and brain. Resolving the microbiome-brain connection might lead to novel therapies. Some physicians and companies are already exploring typical probiotics, oral bacterial supplements, for depression, although they don’t normally include the missing gut microbes identified in the new study.














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The second annual PureTech Health BIG (Brain-Immune-Gut) Summit 2019 – By invitation only –

Selected Tweets from  #BIGAxisSummit

by @pharma_BI @AVIVA1950

for @pharmaceuticalintelligence.com

Reporter: Aviva Lev-Ari, PhD, RN


January 30 – February 1, 2019

The second annual PureTech Health BIG Summit brings together an elite ensemble of leading scientific researchers, investors, and CEOs and R&D leaders from major pharmaceutical, technology, and biotech companies.

The BIG Summit is designed to stimulate ideas that will have an impact on existing pipelines and catalyze future interactions among a group of delegates that represent leaders and innovators in their fields.

Please follow the discussion on Twitter using #BIGAxisSummit

By invitation only; registration is non-transferable.

For more information, please contact PureTechHealthSummit@PureTechHealth.com






(Subject to Change)

PureTech Health BIG Summit 2019 Agenda_FINALv2_WEBSITE.jpg

“Almost starting to understand immunology at this thought-provoking @PureTechh #BIGAxisSummit. Great Speakers.”

-tweet by Simone Fishburn, BioCentury @SimoneFishburn




Selected Tweets from  #BIGAxisSummit

by @pharma_BI @AVIVA1950

for @pharmaceuticalintelligence.com

Gail S. Thornton Selections

Luke Timmerman‏ @ldtimmerman 7h7 hours ago

Back for final sessions at #BIGAxisSummit. @PureTechH Jim Harper of Sonde Health talking about how voice data — pacing, fine motor articulation, oscillation — can point the way to objective, quantitative measures for detecting and monitoring depression.


Eddie Martucci

 @EddieMartucci 5h5 hours ago

Paul Biondi at #BIGAxisSummit : What makes big deals happen is financial, and *deep conviction* of a big future fit. Disproportionate valuation from bidders is expected.

Love this. We often reduce everything to mathematical analyses to champion or ridicule deals. Not that simple


PureTech Health Plc‏ @PureTechH Jan 31

Bob Langer (@MIT) asks how #lymphatics affected by #aging. Santambrogio: typically blame aging #immune cells for increased disease, but aging affects lymphatics too (less efficient trafficking shown). Rejuvenating these could affect several aging-related diseases #BigAxisSummit


PureTech Health Plc‏ @PureTechH Jan 31

Viviane Labrie (@VAInstitute) discusses why the appendix has been identified as a potential starting point for #parkinsons #BIGAxisSummit


PureTech Health Plc‏ @PureTechH Jan 31

Chris Porter (@MIPS_Australia) notes #lymphatics is major route for trafficking #immune cells that surveil gut and respond to immune & #autoimmune stimuli. This is key in #BIGAxis interactions and why lymphatics-targeted therapies could enhance #immunomodulation #BIGAxisSummit


Dr. Stephen J. Williams Selections






Dr. Irina Robu Selection






Dr. Sudipta Saha Selection








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Ability of gut microbiota to influence the bioavailability of in Parkinson’s disease – The presence of more bacteria producing the tyrosine decarboxylase (TDC) enzyme means less levodopa in the bloodstream


Reporter: Aviva Lev-Ari, PhD, RN


Decarboxylase enzymes can convert levodopa into dopamine. In contrast to levodopa, dopamine cannot cross the , so patients are also given a decarboxylase inhibitor. “But the levels of levodopa that will reach the brain vary strongly among Parkinson’s disease patients.

The bacterial  decarboxylase enzyme, which normally converts tyrosine into tyramine, but was found to also convert levodopa into . “We then determined that the source of this decarboxylase was Enterococcus bacteria.” The researchers also showed that the conversion of levodopa was not inhibited by a high concentration of the amino acid tyrosine, the main substrate of the bacterial tyrosine decarboxylase enzyme.

  • Carbidopa is over 10,000 times more potent in inhibiting the human decarboxylase,
  • the higher abundance of bacterial enzyme in the small intestines of rats reduced levels of levodopa in the bloodstream,
  • positive correlation between disease duration and levels of bacterial tyrosine decarboxylase.
  • Some Parkinson’s disease patients develop an overgrowth of small intestinal bacteria including Enterococci due to frequent uptake of proton pump inhibitors, which they use to treat gastrointestinal symptoms associated with the disease.
  • Altogether, these factors result in a vicious circle leading to an increased levodopa/decarboxylase inhibitor dosage requirement in a subset of patients.El Aidy concludes that
  • the presence of the bacterial tyrosine decarboxylase enzyme can explain why some patients need more frequent dosages of levodopa to treat their motor fluctuations. “This is considered to be a problem for Parkinson’s disease patients, because a higher dose will result in dyskinesia, one of the major side effects of levodopa treatment.



Article OPEN Published: 

Gut bacterial tyrosine decarboxylases restrict levels of levodopa in the treatment of Parkinson’s disease

Nature Communications volume 10, Article number: 310 (2019) Download Citation


Human gut microbiota senses its environment and responds by releasing metabolites, some of which are key regulators of human health and disease. In this study, we characterize gut-associated bacteria in their ability to decarboxylate levodopa to dopamine via tyrosine decarboxylases. Bacterial tyrosine decarboxylases efficiently convert levodopa to dopamine, even in the presence of tyrosine, a competitive substrate, or inhibitors of human decarboxylase. In situ levels of levodopa are compromised by high abundance of gut bacterial tyrosine decarboxylase in patients with Parkinson’s disease. Finally, the higher relative abundance of bacterial tyrosine decarboxylases at the site of levodopa absorption, proximal small intestine, had a significant impact on levels of levodopa in the plasma of rats. Our results highlight the role of microbial metabolism in drug availability, and specifically, that abundance of bacterial tyrosine decarboxylase in the proximal small intestine can explain the increased dosage regimen of levodopa treatment in Parkinson’s disease patients.




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Reporter and Curator: Dr. Sudipta Saha, Ph.D.


Over the past 20 years, studies have shown that girls and possibly boys have been experiencing puberty at progressively younger ages. This is troubling news, as earlier age at puberty has been linked with increased risk of mental illness, breast and ovarian cancer in girls and testicular cancer in boys. Researchers found that daughters of mothers who had higher levels of diethyl phthalate and triclosan in their bodies during pregnancy experienced puberty at younger ages. The same trend was not observed in boys. So, researchers suspected that girls exposed to chemicals commonly found in toothpaste, makeup, soap and other personal care products before birth may hit puberty earlier.


Diethyl phthalate is often used as a stabilizer in fragrances and cosmetics. The antimicrobial agent triclosan — which the FDA banned from use in hand soap in 2017 because it was shown to be ineffective — is still used in some toothpastes. Researchers suspected that many chemicals in personal care products can interfere with natural hormones in human bodies, and studies have shown that exposure to these chemicals can alter reproductive development in rats. Chemicals that have been implicated include phthalates, which are often found in scented products like perfumes, soaps and shampoos; parabens, which are used as preservatives in cosmetics; and phenols, which include triclosan.


However, few studies have looked at how these chemicals might affect the growth of human children. This present study at UC Berkeley, USA recruited pregnant women living in the farm-working, primarily Latino communities of Central California’s Salinas Valley between 1999 and 2000. While the primary aim of the study was to examine the impact of pesticide exposure on childhood development, the researchers used the opportunity to examine the effects of other chemicals as well. The scientists measured concentrations of phthalates, parabens and phenols in urine samples taken from mothers twice during pregnancy, and from children at the age of 9. They then followed the growth of the children — 159 boys and 179 girls — between the ages of 9 and 13 to track the timing of developmental milestones marking different stages of puberty.


The vast majority — more than 90 percent — of urine samples of both mothers and children showed detectable concentrations of all three classes of chemicals, with the exception of triclosan which was present in approximately 70 percent of samples. The researchers found that every time the concentrations of diethyl phthalate and triclosan in the mother’s urine doubled, the timing of developmental milestones in girls shifted approximately one month earlier. Girls who had higher concentrations of parabens in their urine at age 9 also experienced puberty at younger ages. However, it is unclear if the chemicals were causing the shift, or if girls who reached puberty earlier were more likely to start using personal care products at younger ages.


The limitations are that these chemicals are quickly metabolized and one to two urinary measurements per developmental point may not accurately reflect usual exposure. The study population was limited to Latino children of low socioeconomic status living in a farmworker community and may not be widely generalizable. But, this study contributes to a growing literature that suggests that exposure to certain endocrine disrupting chemicals may impact timing of puberty in children.














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Lesson 9 Cell Signaling:  Curations and Articles of reference as supplemental information for lecture section on WNTs: #TUBiol3373

Stephen J. Wiilliams, Ph.D: Curator

UPDATED 4/23/2019

This has an updated lesson on WNT signaling.  Please click on the following and look at the slides labeled under lesson 10

cell motility 9b lesson_2018_sjw

Remember our lessons on the importance of signal termination.  The CANONICAL WNT signaling (that is the β-catenin dependent signaling)

is terminated by the APC-driven degradation complex.  This leads to the signal messenger  β-catenin being degraded by the proteosome.  Other examples of growth factor signaling that is terminated by a proteosome-directed include the Hedgehog signaling system, which is involved in growth and differentiation as well as WNTs and is implicated in various cancers.

A good article on the Hedgehog signaling pathway is found here:

The Voice of a Pathologist, Cancer Expert: Scientific Interpretation of Images: Cancer Signaling Pathways and Tumor Progression

All images in use for this article are under copyrights with Shutterstock.com

Cancer is expressed through a series of transformations equally involving metabolic enzymes and glucose, fat, and protein metabolism, and gene transcription, as a result of altered gene regulatory and transcription pathways, and also as a result of changes in cell-cell interactions.  These are embodied in the following series of graphics.

Figure 1: Sonic_hedgehog_pathwaySonic_hedgehog_pathway

The Voice of Dr. Larry

The figure shows a modification of nuclear translocation by Sonic hedgehog pathway. The hedgehog proteins have since been implicated in the development of internal organs, midline neurological structures, and the hematopoietic system in humans. The Hh signaling pathway consists of three main components: the receptor patched 1 (PTCH1), the seven transmembrane G-protein coupled receptor smoothened (SMO), and the intracellular glioma-associated oncogene homolog (GLI) family of transcription factors.5The GLI family is composed of three members, including GLI1 (gene activating), GLI2 (gene activating and repressive), and GLI3 (gene repressive).6 In the absence of an activating signal from either Shh, Ihh or Dhh, PTCH1 exerts an inhibitory effect on the signal transducer SMO, preventing any downstream signaling from occurring.7 When Hh ligands bind and activate PTCH1, the inhibition on SMO is released, allowing the translocation of SMO into the cytoplasm and its subsequent activation of the GLI family of transcription factors.


And from the review of  Elaine Y. C. HsiaYirui Gui, and Xiaoyan Zheng   Regulation of Hedgehog Signaling by Ubiquitination  Front Biol (Beijing). 2015 Jun; 10(3): 203–220.

the authors state:

Finally, termination of Hh signaling is also important for controlling the duration of pathway activity. Hh induced ubiquitination and degradation of Ci/Gli is the most well-established mechanism for limiting signal duration, and inhibiting this process can lead to cell patterning disruption and excessive cell proliferation (). In addition to Ci/Gli, a growing body of evidence suggests that ubiquitination also plays critical roles in regulating other Hh signaling components including Ptc, Smo, and Sufu. Thus, ubiquitination serves as a general mechanism in the dynamic regulation of the Hh pathway.

Overview of Hedgehog signaling showing the signal termination by ubiquitnation and subsequent degradation of the Gli transcriptional factors. obtained from Oncotarget 5(10):2881-911 · May 2014. GSK-3B as a Therapeutic Intervention in Cancer


















Note that in absence of Hedgehog ligands Ptch inhibits Smo accumulation and activation but upon binding of Hedgehog ligands (by an autocrine or paracrine fashion) Ptch is now unable to inhibit Smo (evidence exists that Ptch is now targeted for degradation) and Smo can now inhibit Sufu-dependent and GSK-3B dependent induced degradation of Gli factors Gli1 and Gli2.  Also note the Gli1 and Gli2 are transcriptional activators while Gli3 is a transcriptional repressor.

UPDATED 4/16/2019

Please click on the following links for the Powerpoint presentation for lesson 9.  In addition click on the mp4 links to download the movies so you can view them in Powerpoint slide 22:

cell motility 9 lesson_SJW 2019

movie file 1:

Tumorigenic but noninvasive MCF-7 cells motility on an extracellular matrix derived from normal (3DCntrol) or tumor associated (TA) fibroblasts.  Note that TA ECM is “soft” and not organized and tumor cells appear to move randomly if  much at all.

Movie 2:


Note that these tumorigenic and invasive MDA-MB-231 breast cancer cells move in organized patterns on organized ECM derived from Tumor Associated (TA) fibroblasts than from the ‘soft’ or unorganized ECM derived from normal  (3DCntrl) fibroblasts


The following contain curations of scientific articles from the site https://pharmaceuticalintelligence.com  intended as additional reference material  to supplement material presented in the lecture.

Wnts are a family of lipid-modified secreted glycoproteins which are involved in:

Normal physiological processes including

A. Development:

– Osteogenesis and adipogenesis (Loss of wnt/β‐catenin signaling causes cell fate shift of preosteoblasts from osteoblasts to adipocytes)

  – embryogenesis including body axis patterning, cell fate specification, cell proliferation and cell migration

B. tissue regeneration in adult tissue

read: Wnt signaling in the intestinal epithelium: from endoderm to cancer

And in pathologic processes such as oncogenesis (refer to Wnt/β-catenin Signaling [7.10]) and to your Powerpoint presentation


The curation Wnt/β-catenin Signaling is a comprehensive review of canonical and noncanonical Wnt signaling pathways


To review:












Activating the canonical Wnt pathway frees B-catenin from the degradation complex, resulting in B-catenin translocating to the nucleus and resultant transcription of B-catenin/TCF/LEF target genes.

Fig. 1 Canonical Wnt/FZD signaling pathway. (A) In the absence of Wnt signaling, soluble β-catenin is phosphorylated by a degradation complex consisting of the kinases GSK3β and CK1α and the scaffolding proteins APC and Axin1. Phosphorylated β-catenin is targeted for proteasomal degradation after ubiquitination by the SCF protein complex. In the nucleus and in the absence of β-catenin, TCF/LEF transcription factor activity is repressed by TLE-1; (B) activation of the canonical Wnt/FZD signaling leads to phosphorylation of Dvl/Dsh, which in turn recruits Axin1 and GSK3β adjacent to the plasma membrane, thus preventing the formation of the degradation complex. As a result, β-catenin accumulates in the cytoplasm and translocates into the nucleus, where it promotes the expression of target genes via interaction with TCF/LEF transcription factors and other proteins such as CBP, Bcl9, and Pygo.

NOTE: In the canonical signaling, the Wnt signal is transmitted via the Frizzled/LRP5/6 activated receptor to INACTIVATE the degradation complex thus allowing free B-catenin to act as the ultimate transducer of the signal.

Remember, as we discussed, the most frequent cancer-related mutations of WNT pathway constituents is in APC.

This shows how important the degradation complex is in controlling canonical WNT signaling.

Other cell signaling systems are controlled by protein degradation:

A.  The Forkhead family of transcription factors

Read: Regulation of FoxO protein stability via ubiquitination and proteasome degradation

B. Tumor necrosis factor α/NF κB signaling

Read: NF-κB, the first quarter-century: remarkable progress and outstanding questions

1.            Question: In cell involving G-proteins, the signal can be terminated by desensitization mechanisms.  How is both the canonical and noncanonical Wnt signal eventually terminated/desensitized?

We also discussed the noncanonical Wnt signaling pathway (independent of B-catenin induced transcriptional activity).  Note that the canonical and noncanonical involve different transducers of the signal.

Noncanonical WNT Signaling

Note: In noncanonical signaling the transducer is a G-protein and second messenger system is IP3/DAG/Ca++ and/or kinases such as MAPK, JNK.

Depending on the different combinations of WNT ligands and the receptors, WNT signaling activates several different intracellular pathways  (i.e. canonical versus noncanonical)


In addition different Wnt ligands are expressed at different times (temporally) and different cell types in development and in the process of oncogenesis. 

The following paper on Wnt signaling in ovarian oncogenesis shows how certain Wnt ligands are expressed in normal epithelial cells but the Wnt expression pattern changes upon transformation and ovarian oncogenesis. In addition, differential expression of canonical versus noncanonical WNT ligands occur during the process of oncogenesis (for example below the authors describe the noncanonical WNT5a is expressed in normal ovarian  epithelia yet WNT5a expression in ovarian cancer is lower than the underlying normal epithelium. However the canonical WNT10a, overexpressed in ovarian cancer cells, serves as an oncogene, promoting oncogenesis and tumor growth.

Wnt5a Suppresses Epithelial Ovarian Cancer by Promoting Cellular Senescence

Benjamin G. Bitler,1 Jasmine P. Nicodemus,1 Hua Li,1 Qi Cai,2 Hong Wu,3 Xiang Hua,4 Tianyu Li,5 Michael J. Birrer,6Andrew K. Godwin,7 Paul Cairns,8 and Rugang Zhang1,*

A.           Abstract

Epithelial ovarian cancer (EOC) remains the most lethal gynecological malignancy in the US. Thus, there is an urgent need to develop novel therapeutics for this disease. Cellular senescence is an important tumor suppression mechanism that has recently been suggested as a novel mechanism to target for developing cancer therapeutics. Wnt5a is a non-canonical Wnt ligand that plays a context-dependent role in human cancers. Here, we investigate the role of Wnt5a in regulating senescence of EOC cells. We demonstrate that Wnt5a is expressed at significantly lower levels in human EOC cell lines and in primary human EOCs (n = 130) compared with either normal ovarian surface epithelium (n = 31; p = 0.039) or fallopian tube epithelium (n = 28; p < 0.001). Notably, a lower level of Wnt5a expression correlates with tumor stage (p = 0.003) and predicts shorter overall survival in EOC patients (p = 0.003). Significantly, restoration of Wnt5a expression inhibits the proliferation of human EOC cells both in vitro and in vivo in an orthotopic EOC mouse model. Mechanistically, Wnt5a antagonizes canonical Wnt/β-catenin signaling and induces cellular senescence by activating the histone repressor A (HIRA)/promyelocytic leukemia (PML) senescence pathway. In summary, we show that loss of Wnt5a predicts poor outcome in EOC patients and Wnt5a suppresses the growth of EOC cells by triggering cellular senescence. We suggest that strategies to drive senescence in EOC cells by reconstituting Wnt5a signaling may offer an effective new strategy for EOC therapy.

Oncol Lett. 2017 Dec;14(6):6611-6617. doi: 10.3892/ol.2017.7062. Epub 2017 Sep 26.

Clinical significance and biological role of Wnt10a in ovarian cancer. 

Li P1Liu W1Xu Q1Wang C1.

Ovarian cancer is one of the five most malignant types of cancer in females, and the only currently effective therapy is surgical resection combined with chemotherapy. Wnt family member 10A (Wnt10a) has previously been identified to serve an oncogenic function in several tumor types, and was revealed to have clinical significance in renal cell carcinoma; however, there is still only limited information regarding the function of Wnt10a in the carcinogenesis of ovarian cancer. The present study identified increased expression levels of Wnt10a in two cell lines, SKOV3 and A2780, using reverse transcription-polymerase chain reaction. Functional analysis indicated that the viability rate and migratory ability of SKOV3 cells was significantly inhibited following Wnt10a knockdown using short interfering RNA (siRNA) technology. The viability rate of SKOV3 cells decreased by ~60% compared with the control and the migratory ability was only ~30% of that in the control. Furthermore, the expression levels of β-catenin, transcription factor 4, lymphoid enhancer binding factor 1 and cyclin D1 were significantly downregulated in SKOV3 cells treated with Wnt10a-siRNA3 or LGK-974, a specific inhibitor of the canonical Wnt signaling pathway. However, there were no synergistic effects observed between Wnt10a siRNA3 and LGK-974, which indicated that Wnt10a activated the Wnt/β-catenin signaling pathway in SKOV3 cells. In addition, using quantitative PCR, Wnt10a was overexpressed in the tumor tissue samples obtained from 86 patients with ovarian cancer when compared with matching paratumoral tissues. Clinicopathological association analysis revealed that Wnt10a was significantly associated with high-grade (grade III, P=0.031) and late-stage (T4, P=0.008) ovarian cancer. Furthermore, the estimated 5-year survival rate was 18.4% for patients with low Wnt10a expression levels (n=38), whereas for patients with high Wnt10a expression (n=48) the rate was 6.3%. The results of the present study suggested that Wnt10a serves an oncogenic role during the carcinogenesis and progression of ovarian cancer via the Wnt/β-catenin signaling pathway.

Targeting the Wnt Pathway includes curations of articles related to the clinical development of Wnt signaling inhibitors as a therapeutic target in various cancers including hepatocellular carcinoma, colon, breast and potentially ovarian cancer.


2.         Question: Given that different Wnt ligands and receptors activate different signaling pathways, AND  WNT ligands  can be deferentially and temporally expressed  in various tumor types and the process of oncogenesis, how would you approach a personalized therapy targeting the WNT signaling pathway?

3.         Question: What are the potential mechanisms of either intrinsic or acquired resistance to Wnt ligand antagonists being developed?


Other related articles published in this Open Access Online Scientific Journal include the following:

Targeting the Wnt Pathway [7.11]

Wnt/β-catenin Signaling [7.10]

Cancer Signaling Pathways and Tumor Progression: Images of Biological Processes in the Voice of a Pathologist Cancer Expert

e-Scientific Publishing: The Competitive Advantage of a Powerhouse for Curation of Scientific Findings and Methodology Development for e-Scientific Publishing – LPBI Group, A Case in Point 

Electronic Scientific AGORA: Comment Exchanges by Global Scientists on Articles published in the Open Access Journal @pharmaceuticalintelligence.com – Four Case Studies


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