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Archive for the ‘Metabolism’ Category


Nano-guided cell networks as conveyors of molecular communication

Nature Communications
6,
Article number:
8500
doi:10.1038/ncomms9500
Received
07 March 2015
Accepted
28 August 2015
Published
12 October 2015

Abstract

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

Figures

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  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

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Introduction

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.

Results

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.

Discussion

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

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

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

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

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

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

 

When a baby is born through its mother’s birth canal, it is bathed in a soup of microbes. Those born by caesarean section (C-section) miss out on this bacterial baptism. The differences in microbe exposure at birth and later health could be caused by other factors, such as whether a mother takes antibiotics during her surgery, and whether a baby is breastfed or has a genetic predisposition to obesity. So, the researchers are sharply split on whether or not this missing of bacterial exposure increases the risk of chronic health problems such as obesity and asthma.

 

Researchers found that babies delivered surgically harboured different collections of bacteria than did those born vaginally. C-section babies, which comprise more than 30% of births in the United States, are also more prone to obesity and immune diseases such as diabetes. Experiments show that mice born by C-section are more prone to obesity and have impaired immune systems. There are fewer factors that could account for these differences in the rodents, which can be studied in controlled conditions, than in people.

 

A wave of clinical trials now under way could help to settle the question — and feed into the debate over whether seeding babies born by C-section with their mother’s vaginal bacteria is beneficial or potentially harmful. Several groups of researchers will be swabbing hundreds of C-section babies with their mother’s microbes, while comparing them to a control group. Each team plans to monitor its study participants over several years in the hope of learning more about how the collection of microbes in their bodies might influence weight, allergy risk and other factors.

 

But some scientists say that the trials could expose C-section babies to infection, or encourage mothers to try do-it-yourself swabbing, without much evidence that there is a benefit or risk. Moreover, there is no evidence that differing exposure to vaginal microbes at birth can help explain variation in people’s health over time. Presently the whole concept is in very much a state of uncertainty.

 

Researchers in near future will compare swabbed C-section babies with a placebo group and with infants delivered vaginally. They confirmed that their protocols will not increase the risk of infection for C-section babies. Scientists will also rigorously screen mothers participating in these trials for microbes such as HIV and group B streptococcus — a common vaginal bacterium that causes respiratory problems in newborns.

 

References:

 

https://www.nature.com/articles/d41586-019-02348-3?utm_source=Nature+Briefing

 

https://www.ncbi.nlm.nih.gov/pubmed/31431742

 

https://www.ncbi.nlm.nih.gov/pubmed/20566857

 

https://www.ncbi.nlm.nih.gov/pubmed/25452656

 

https://www.ncbi.nlm.nih.gov/pubmed/22939691

 

https://www.ncbi.nlm.nih.gov/pubmed/24030708

 

https://pharmaceuticalintelligence.com/2017/02/22/babys-microbiome-changing-due-to-caesarean-birth-and-formula-feeding/

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The Journey of Antibiotic Discovery

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

 

The term ‘antibiotic’ was introduced by Selman Waksman as any small molecule, produced by a microbe, with antagonistic properties on the growth of other microbes. An antibiotic interferes with bacterial survival via a specific mode of action but more importantly, at therapeutic concentrations, it is sufficiently potent to be effective against infection and simultaneously presents minimal toxicity. Infectious diseases have been a challenge throughout the ages. From 1347 to 1350, approximately one-third of Europe’s population perished to Bubonic plague. Advances in sanitary and hygienic conditions sufficed to control further plague outbreaks. However, these persisted as a recurrent public health issue. Likewise, infectious diseases in general remained the leading cause of death up to the early 1900s. The mortality rate shrunk after the commercialization of antibiotics, which given their impact on the fate of mankind, were regarded as a ‘medical miracle’. Moreover, the non-therapeutic application of antibiotics has also greatly affected humanity, for instance those used as livestock growth promoters to increase food production after World War II.

 

Currently, more than 2 million North Americans acquire infections associated with antibiotic resistance every year, resulting in 23,000 deaths. In Europe, nearly 700 thousand cases of antibiotic-resistant infections directly develop into over 33,000 deaths yearly, with an estimated cost over €1.5 billion. Despite a 36% increase in human use of antibiotics from 2000 to 2010, approximately 20% of deaths worldwide are related to infectious diseases today. Future perspectives are no brighter, for instance, a government commissioned study in the United Kingdom estimated 10 million deaths per year from antibiotic resistant infections by 2050.

 

The increase in antibiotic-resistant bacteria, alongside the alarmingly low rate of newly approved antibiotics for clinical usage, we are on the verge of not having effective treatments for many common infectious diseases. Historically, antibiotic discovery has been crucial in outpacing resistance and success is closely related to systematic procedures – platforms – that have catalyzed the antibiotic golden age, namely the Waksman platform, followed by the platforms of semi-synthesis and fully synthetic antibiotics. Said platforms resulted in the major antibiotic classes: aminoglycosides, amphenicols, ansamycins, beta-lactams, lipopeptides, diaminopyrimidines, fosfomycins, imidazoles, macrolides, oxazolidinones, streptogramins, polymyxins, sulphonamides, glycopeptides, quinolones and tetracyclines.

 

The increase in drug-resistant pathogens is a consequence of multiple factors, including but not limited to high rates of antimicrobial prescriptions, antibiotic mismanagement in the form of self-medication or interruption of therapy, and large-scale antibiotic use as growth promotors in livestock farming. For example, 60% of the antibiotics sold to the USA food industry are also used as therapeutics in humans. To further complicate matters, it is estimated that $200 million is required for a molecule to reach commercialization, with the risk of antimicrobial resistance rapidly developing, crippling its clinical application, or on the opposing end, a new antibiotic might be so effective it is only used as a last resort therapeutic, thus not widely commercialized.

 

Besides a more efficient management of antibiotic use, there is a pressing need for new platforms capable of consistently and efficiently delivering new lead substances, which should attend their precursors impressively low rates of success, in today’s increasing drug resistance scenario. Antibiotic Discovery Platforms are aiming to screen large libraries, for instance the reservoir of untapped natural products, which is likely the next antibiotic ‘gold mine’. There is a void between phenotanypic screening (high-throughput) and omics-centered assays (high-information), where some mechanistic and molecular information complements antimicrobial activity, without the laborious and extensive application of various omics assays. The increasing need for antibiotics drives the relentless and continuous research on the foreground of antibiotic discovery. This is likely to expand our knowledge on the biological events underlying infectious diseases and, hopefully, result in better therapeutics that can swing the war on infectious diseases back in our favor.

 

During the genomics era came the target-based platform, mostly considered a failure due to limitations in translating drugs to the clinic. Therefore, cell-based platforms were re-instituted, and are still of the utmost importance in the fight against infectious diseases. Although the antibiotic pipeline is still lackluster, especially of new classes and novel mechanisms of action, in the post-genomic era, there is an increasingly large set of information available on microbial metabolism. The translation of such knowledge into novel platforms will hopefully result in the discovery of new and better therapeutics, which can sway the war on infectious diseases back in our favor.

 

References:

 

https://www.mdpi.com/2079-6382/8/2/45/htm

 

https://www.ncbi.nlm.nih.gov/pubmed/19515346

 

https://www.ajicjournal.org/article/S0196-6553(11)00184-2/fulltext

 

https://www.ncbi.nlm.nih.gov/pubmed/21700626

 

http://www.med.or.jp/english/journal/pdf/2009_02/103_108.pdf

 

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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.

https://hms.harvard.edu/news/major-shift?utm_source=Silverpop&utm_medium=email&utm_term=field_news_item_3&utm_content=HMNews05132019

 

 

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

Abstract

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.

SOURCE

https://www.massgeneral.org/about/pressrelease.aspx?id=2403

 

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

http://www.amazon.com/dp/B012BB0ZF0

Gluten-free Diets

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

https://pharmaceuticalintelligence.com/2015/03/01/gluten-free-diets/

 

Breakthrough Digestive Disorders Research: Conditions affecting the Gastrointestinal Tract.

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2012/12/12/breakthrough-digestive-disorders-research-conditions-affecting-the-gastrointestinal-tract/

 

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

https://pharmaceuticalintelligence.com/2016/01/25/collagen-binding-molecular-chaperone-hsp47-role-in-intestinal-fibrosis-colonic-epithelial-cells-and-subepithelial-myofibroblasts/

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

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2017/01/17/expanding-area-of-tolerance-inducing-autoimmune-disease-therapeutics-key-players/

 

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

Author and Curator: Larry H Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2014/03/21/what-is-the-key-method-to-harness-inflammation-to-close-the-doors-for-many-complex-diseases/

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PEER-REVIEWED MEDICAL JOURNAL PUBLISHES LANDMARK STUDY ON EFFICACY AND SAFETY OF FDgard® (COLM-SST), DEMONSTRATING RAPID REDUCTION OF FUNCTIONAL DYSPEPSIA (FD OR RECURRING, MEAL-TRIGGERED INDIGESTION) SYMPTOMS WITHIN 24 HOURS

  • FDgard® (COLM-SST), a solid-state microsphere formulation of caraway oil and l-Menthol, taken daily and proactively 30-60 minutes before meals, showed statistically significant, rapid reduction of Functional Dyspepsia (FD) symptoms within 24 hours and, additionally, relief of severe FD symptoms.
  • FDREST clinical trial with FDgard represents an important medical advance, as no previous trials have shown rapid relief of FD symptoms. There are no approved products for this highly prevalent condition.
  • In FDREST, patients received greater and more durable benefits with the addition of FDgard taken daily and proactively to their typical medical regimen.
  • FDREST is the first clinical trial in FD to use patented, Site Specific Targeting (SST®) technology to deliver the FDgard formulation to the upper belly (duodenum), the primary site of disturbance in FD.
  • FDgard represents an effective, safe and well-tolerated option to address the unmet medical needs of millions of adults with FD.

Reporter: Gail S. Thornton

Boca Raton Fl., – (April 30, 2019) – IM HealthScience today announced that Clinical and Translational Gastroenterology (CTG), a peer-reviewed medical journal, has published the U.S. results of a landmark, double-blind, placebo-controlled study, FDREST™ (Functional Dyspepsia Reduction Evaluation and Safety Trial), which showed statistically significant, rapid reduction of Functional Dyspepsia (FD or recurring, meal-triggered indigestion) symptoms within 24 hours and, additionally, relief of severe FD symptoms.

The study, entitled “A Novel, Duodenal-Release Formulation of a Combination of Caraway Oil and L-Menthol for the Treatment of Functional Dyspepsia: A Randomized Controlled Trial,” is now available to the public via open access on the Clinical and Translational Gastroenterology website. Clinical and Translational Gastroenterology, published on behalf of the American College of Gastroenterology (ACG), is dedicated to innovative clinical work in the field of gastroenterology and hepatology.

The FDREST study demonstrated that patients who took COLM-SST (FDgard®) on a daily and proactive basis, 30 to 60 minutes before meals, along with commonly used off-label FD medications versus patients who took placebo along with commonly used off-label FD medications, experienced a statistically significant, rapid reduction of FD symptoms within 24 hours across the FD study population.

This study had a higher hurdle than previous studies on a similar combination of ingredients. Firstly, concomitant medications for FD symptoms were allowed in order to assess FDgard in a real-world setting. Second, only a subgroup of patients in FDREST was categorized into the high-symptom burden, while they constituted the entire groups in previous studies. Among this subgroup of patients with the high-symptom burden, FDgard showed efficacy at 24 hours. In spite of the polypharmacy and use of rescue medications for FD, after 48 hours of first dose, FDgard helped further improve symptoms at 4 weeks, especially in those high-symptom burden patients. In all cases, FDgard was safe and well-tolerated.  

The study results of FDREST were first presented at Digestive Disease Week (DDW), the largest gathering of gastroenterologists, in May 2017.

Study Commentary

Commenting on the study, lead author William Chey, M.D., FACG, Director in the Division of Gastroenterology, Michigan Medicine Gastroenterology Clinic, Ann Arbor, said, “This landmark study was designed to answer a very important scientific question about the effectiveness, safety, and tolerability of a novel and innovative formulation of caraway oil and l-Menthol designed as solid state, enteric coated microspheres for targeted duodenal release for FD. In patients taking their usual medications for FD, FDgard was found to be effective, safe and well tolerated in rapidly reducing symptoms and in relieving severe symptoms.” Chey continued, “The positive finding at 24 hours is clinically important as symptoms are often triggered by a meal and patients are looking for rapid relief of those symptoms.”

The study authors also cited the importance of utilizing the microsphere-based site-specific targeting of FDgard (caraway oil and l-Menthol, the active ingredient in peppermint oil) to the duodenum. They wrote, “This site (duodenum) was targeted primarily due to mounting evidence that gastroduodenal mucosal integrity and low-grade inflammation play a role in FD. Furthermore, studies have shown that caraway oil and peppermint oil act on the duodenum to induce smooth muscle relaxation, and that l-Menthol has anti-inflammatory effects.” This may help normalize motility effects.

About FDREST™

FDREST™ (Functional Dyspepsia Reduction and Evaluation Safety Trial) was a multi-centered, post-marketing, parallel group, U.S-based study conducted at seven university-based or gastroenterology research-based centers (study period July 1, 2015, to September 14, 2016). The study was designed to compare the efficacy, safety and tolerability of FDgard plus commonly used, off-label medications for FD vs. a control group of placebo plus commonly used, off-label medications prescribed for FD.

Ninety-five patients were enrolled (mean age = 43.4 years; 75.8 percent women). At 24 hours, the active arm reported a statistically significant reduction in Postprandial Distress Syndrome (PDS) symptoms (P = 0.039), and a nonsignificant trend toward benefit of Epigastric Pain Syndrome (EPS) symptoms (P = 0.074). In patients with more severe symptoms, approximately three-quarters showed substantial global improvement (i.e., clinical global impressions) after 4 weeks of treatment vs. half in the control arm. These differences were statistically significant for patients with EPS symptoms (epigastric pain or discomfort and burning) (P = 0.046), and trending toward significance for patients with PDS symptoms (early satiety, abdominal heaviness, pressure and fullness) (P = 0.091). There were no statistically significant differences between groups for Global Overall Symptom scores for the overall population at 2 and 4 weeks.

Dr. Chey said, “The results of this high-quality study highlight an advance in the management of FD, as current off-label medications such as PPIs, H2RAs and antidepressants offer only a modest level of therapeutic gain over placebo and may be associated with adverse events, especially with continued use. FDgard addresses a significant unmet medical need for a product to help manage symptoms in the 1 in 6 adults suffering from this common disorder.”

About Functional Dyspepsia (FD)

Functional dyspepsia is a very common disorder affecting 11 percent – 29.2 percent of the world’s population1, making it comparable in prevalence to IBS. However, unlike IBS, there is no FDA approved product to treat FD. Sufferers are often treated off-label with prescribed proton pump inhibitors (PPIs), histamine type-2 receptor antagonists (H2RAs), antidepressants, and prokinetics. While offering relief to a portion of FD patients, some of these have been associated with adverse events. Functional dyspepsia can have a negative effect on workplace attendance and productivity, with associated costs estimated in excess of $18 billion annually.2

In FD, which is typically recurring, meal-triggered indigestion with no known organic cause, the normal digestive processes are disrupted along with digestion and absorption of food nutrients. FD is accompanied by symptoms such as epigastric pain or discomfort, epigastric burning, postprandial fullness, inability to finish a normal sized meal, heaviness, pressure, bloating in the upper abdomen, nausea, and belching. When doctors diagnose FD, they often identify patients as those who have these symptoms for at least three months, with symptom onset six months previously.

About FDgard®

FDgard® is a nonprescription medical food designed to address the unmet medical need for products to help manage Functional Dyspepsia (FD or recurring, meal-triggered indigestion) and its accompanying symptoms.  FDgard capsules contain caraway oil and l-Menthol, the primary component in peppermint oil, for the dietary management of FD. These two main ingredients are specially formulated to be available in a solid state.  With patented Site Specific Targeting (SST®) technology pioneered by IM HealthScience, FDgard capsules release individually triple-coated, solid-state microspheres of caraway oil and l-Menthol quickly and reliably where they are needed most in FD — the duodenum or upper belly. The l-Menthol helps with smooth muscle relaxation and provides analgesic and anti-inflammatory activities.3–5 Caraway oil helps mitigate the effect of gastric acid on the stomach wall and also helps to normalize gallbladder function and may help to normalize motility in the small intestine (primarily the duodenum) and in the stomach.6,7 In addition to caraway oil and l-Menthol, FDgard also provides fiber and amino acids (from gelatin protein). These ingredients have additional positive effects on the gut wall and thus help toward normalizing digestion and absorption.            

Caraway oil and peppermint oil have a history of working in FD. In multiple clinical studies, the combination of caraway oil and peppermint oil has been shown to manage FD and its accompanying symptoms, such as reducing the intensity of epigastric pain, pain frequency, dyspeptic discomfort, and the intensity of sensations of pressure, abdominal heaviness and fullness significantly better than control.8,9 Cisapride, no longer an FDA-approved pro-motility drug after its removal from the market in 2000 due to cardiovascular side effects, was shown to have efficacy similar to a caraway oil/peppermint oil formulation10.

Complete and final results from a real-world, observational study of 600 patients who took FDgard, called FDACT™ (Functional Dyspepsia Adherence and Compliance Trial), were selected after peer review and presented by William D. Chey, M.D., FACG, at the World Congress of Gastroenterology at ACG 2017 in Orlando, Florida. The data showed there was a consistently high level of patient satisfaction and rapid improvement of FD symptoms with the product. A majority of patients (95 percent) reported major or moderate improvement in their overall FD symptoms, while many patients (86.4 percent) indicated experiencing relief from symptoms within 2 hours after taking FDgard. The findings from FDACT substantiate the data reported in FDREST.

The usual adult dose of FDgard is 2 capsules, as needed, up to two times a day, not to exceed six capsules per day. Many physicians are now recommending taking FDgard daily and proactively 30-60 minutes before a meal, as this enables the supportive effect of FDgard to start as early as possible. While FDgard does not require a prescription and is available in retail outlets and online, it is a medical food that should be used under medical supervision.

About IM HealthScience®

IM HealthScience® (IMH) is the innovator of IBgard®and FDgard®for the dietary management of Irritable Bowel Syndrome (IBS) and Functional Dyspepsia (FD or recurring, meal-triggered indigestion), respectively. In 2017, IMH added Fiber Choice®, a line of prebiotic fibers, to its product line via an acquisition. The sister subsidiary of IMH, Physician’s Seal®, also provides REMfresh®,

a well-known continuous release and absorption melatonin (CRA-melatonin™) supplement for sleep.

IMH is a privately held company based in Boca Raton, Florida. It was founded in 2010 by a team of highly experienced pharmaceutical research and development and management executives. The company is dedicated to developing products to address overall health and wellness, especially in digestive health conditions with a high unmet medical need. The IM HealthScience advantage comes from developing products based on its patented, targeted-delivery technologies called Site Specific Targeting (SST). For more information, visit www.imhealthscience.com to learn about the company, or www.IBgard.com,

 www.FDgard.com, www.FiberChoice.com, and www.Remfresh.com.

References

1.        Mahadeva S, Goh KL. Epidemiology of functional dyspepsia. A global perspective. World J Gastroenterol. 2006. doi:10.3748/wjg.v12.i17.2661.

2.        Lacy BE, Weiser KT, Kennedy AT, Crowell MD, Talley NJ. Functional dyspepsia: the economic impact to patients. Aliment Pharmacol Ther. 2013;38(May):170-177. doi:10.1111/apt.12355.

3.        Amato A, Liotta R, Mulè F. Effects of menthol on circular smooth muscle of human colon: Analysis of the mechanism of action. Eur J Pharmacol. 2014. doi:10.1016/j.ejphar.2014.07.018.

4.        Liu B, Fan L, Balakrishna S, Sui A, Moris JB, Jordt S-E. TRPM8 is the Principal Mediator of Menthol-induced Analgesia of Acute and Inflammatory Pain. Pain. 2013;154(10):2169-2177. doi:10.1016/j.pain.2013.06.043.TRPM8.

5.        Juergens U, Stober M, Vetter H. The anti-inflammatory activity of L-menthol compared to mint oil in human monocytes in vitro: a novel perspective for its therapeutic use in inflammatory diseases. Eur J Med Res. 1998;3(12):539-545.

6.        Alhaider A, Al-Mofleh I, Mossa J, Al-Sohaibani M, Rafatullah S, Qureshi S. Effect of Carum carvi on experimentally induced gastric mucosal damage in Wistar albino rats. Int J Pharmacol. 2006;2(3):309-315.

7.        Micklefield G, Jung O, Greving I, May B. Effects of intraduodenal application of peppermint oil (WS 1340) and caraway oil (WS 1520) on gastroduodenal motility in healthy volunteers. Phyther Res. 2003;17:135-140. doi:10.1002/ptr.1089.

8.        May B, Köhler S, Schneider B. Efficacy and tolerability of a fixed combination of peppermint oil and caraway oil in patients suffering from functional dyspepsia. Aliment Pharmacol Ther. 2000;14:1671-1677. doi:10.1046/j.1365-2036.2000.00873.x.

9.        Rich G, Shah A, Koloski N, et al. A randomized placebo-controlled trial on the effects of Menthacarin, a proprietary peppermint- and caraway-oil-preparation, on symptoms and quality of life in patients with functional dyspepsia. Neurogastroenterol Motil. 2017;29(May):e13132. doi:10.1111/nmo.13132.

10.      Madisch A, Heydenreich C, Wieland V, Hufnagel R, Hotz J. Treatment of Functional Dyspepsia with a Fixed Peppermint Oil and Caraway Oil Combination Preparation as Compared to Cisapride – A multicenter, reference-controlled double-blind equivalence study. Arzneimittelforsch Drug Res. 1999;49(II):925-932.

This information is for educational purposes only and is not meant to be a substitute for the advice of a physician or other health care professional. This information should not be used for diagnosing a health problem or disease. While medical foods do not require prior approval by the FDA for marketing, they must comply with regulations. It should not be assumed that medical foods are alternatives for FDA-approved drugs. Only doctors can definitively diagnose functional dyspepsia. Use under medical supervision. The company will strive to keep information current and consistent but may not be able to do so at any specific time. Generally, the most current information can be found on www.fdgard.com. Individual results may vary.

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

2017

Series D: BioMedicine & Immunology https://pharmaceuticalintelligence.com/biomed-e-books/series-d-e-books-on-biomedicine/

2015

The relationship of stress hypermetabolism to essential protein need

https://pharmaceuticalintelligence.com/2015/10/25/the-relationship-of-stress-hypermetabolism-to-essential-protein-needs/

Liposomes, Lipidomics and Metabolism

https://pharmaceuticalintelligence.com/2015/11/02/liposomes-lipidomics-and-metabolism/

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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.

 

References:

 

https://health.ucsd.edu/news/releases/Pages/2019-03-20-two-enzymes-linked-to-pancreatic-cancer-survival.aspx?elqTrackId=b6864b278958402787f61dd7b7624666

 

https://www.ncbi.nlm.nih.gov/pubmed/30904392

 

https://www.ncbi.nlm.nih.gov/pubmed/29513138

 

https://www.ncbi.nlm.nih.gov/pubmed/18511290

 

https://www.ncbi.nlm.nih.gov/pubmed/28476658

 

https://www.ncbi.nlm.nih.gov/pubmed/28283201

 

https://www.ncbi.nlm.nih.gov/pubmed/24231509

 

https://www.ncbi.nlm.nih.gov/pubmed/28112438

 

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

 

The bacterial makeup of human milk is influenced by the mode of breastfeeding, according to a new study. Although previously considered sterile, breast milk is now known to contain a low abundance of bacteria. While the complexities of how maternal microbiota influence the infant microbiota are still unknown, this complex community of bacteria in breast milk may help to establish the infant gut microbiota. Disruptions in this process could alter the infant microbiota, causing predisposition to chronic diseases such as allergies, asthma, and obesity. While it’s unclear how the breast milk microbiome develops, there are two theories describing its origins. One theory speculates that it originates in the maternal mammary gland, while the other theory suggests that it is due to retrograde inoculation by the infant’s oral microbiome.

 

To address this gap in knowledge scientists carried out bacterial gene sequencing on milk samples from 393 healthy mothers three to four months after giving birth. They used this information to examine how the milk microbiota composition is affected by maternal factors, early life events, breastfeeding practices, and other milk components. Among the many factors analyzed, the mode of breastfeeding (with or without a pump) was the only consistent factor directly associated with the milk microbiota composition. Specifically, indirect breastfeeding was associated with a higher abundance of potential opportunistic pathogens, such as Stenotrophomonas and Pseudomonadaceae. By contrast, direct breastfeeding without a pump was associated with microbes typically found in the mouth, as well as higher overall bacterial richness and diversity. Taken together, the findings suggest that direct breastfeeding facilitates the acquisition of oral microbiota from infants, whereas indirect breastfeeding leads to enrichment with environmental (pump-associated) bacteria.

 

The researchers argued that this study supports the theory that the breast milk microbiome is due to retrograde inoculation. Their findings indicate that the act of pumping and contact with the infant oral microbiome influences the milk microbiome, though they noted more research is needed. In future studies, the researchers will further explore the composition and function of the milk microbiota. In addition to bacteria, they will profile fungi in the milk samples. They also plan to investigate how the milk microbiota influences both the gut microbiota of infants and infant development and health. Specifically, their projects will examine the association of milk microbiota with infant growth, asthma, and allergies. This work could have important implications for microbiota-based strategies for early-life prevention of chronic conditions.

 

References:

 

https://www.genomeweb.com/sequencing/human-breast-milk-microbiome-affected-mode-feeding#.XIOH0igzZPY

 

http://childstudy.ca/2019/02/13/breastmilk-microbiome-linked-to-method-of-feeding/

 

https://gizmodo.com/pumping-breast-milk-changes-its-microbiome-1832568169

 

https://www.sciencedaily.com/releases/2019/02/190213124445.htm

 

https://www.cell.com/cell-host-microbe/fulltext/S1931-3128(19)30049-6

 

https://www.unicef.org.uk/babyfriendly/news-and-research/baby-friendly-research/infant-health-research/epigenetics-microbiome-research/

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