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Which enzymes degrade dynorphins and what drugs are there available to inhibit said enzymes?
This paper was looking at inhibition of dynorphin converting enzymes and their importance. Although they don't seem to know if it is a single enzyme, or if it is multiple enzymes.
This paper looked at bikunin as an endogenous inhibitor of dynorphin convertase in human cerebrospinal fluid.
This paper looked at several inhibitors (some of which are known to irreversibly inhibit other enzymes), like N-peptidyl-O-acyl hydroxylamines and their effects on hCSF-DCE, the enzyme that cleaves dynorphin A, dynorphin B and alpha-neoendorphin to release Leu-enkephalin-Arg6. However, it is known to inhibit serine and cysteine proteinases.
However, I don't see any actual medications that are meant to inhibit Dynorphin enzymes. Instead I found Al-Fayoumi et. al were actually looking to identify stabilized dynorphin A derivatives instead of just inhibition of enzymes.
Mechanism of Drug Action -Drug Enzyme Interactions
Drug enzyme interaction is similar to drug receptor interactions. The drugs resemble the natural substrates, bind enzymes and cause change in their activity. This may take place by:
In therapeutic drugs causing inhibition on enzymes are generally used. This combination of drugs with the enzyme may be:
Non competitive response is irreversible until new enzyme is generated.
Examples of Competitive Inhibition
- ACE inhibitors e.g., captopril
- Reversible anticholinestrases e.g., Neostigmine, physostigmine
1. ACE inhibitors(angiotensin converting enzyme inhibitors)
ACE inhibitors convert angiotensin I into angiotensin II, which is a potent vasoconstrictor. ACE inhibitors are used in the treatment of hypertension.
2. Levo dopa
Levo dopa is metabolized by dopa decarboxylase in the periphery. Carbidopa competes with levo dopa for the dopa decarboxylase enzyme. Thus peripheral metabolism of levo dopa is decreased, more levo dopa enters brain producing more efficacy.
Ethanol (alcohol) undergoes metabolism in body in two steps:
a. Ethanol is converted into acetaldehyde by alcohol dehydrogenase
b. Acetaldehyde is converted into water and carbon dioxide by aldehdyde dehydrogenase.
Neostigmine acts as a reversible acetylcholine esterase inhibitor. Thus in treatment of myasthenia gravis, acetyl choline levels are reversibly increased in the NMJ.
Disulfiram is used in alcohol aversion therapy. It inhibits aldehyde dehydrogenase enzyme. When patient takes alcohol, increase in plasma levels of acetaldehyde cause bad symptoms like nausea, vomiting and flushing.
Allopurinol is used in treatment of gout. Xanthine oxidase is inhibited which converts xanthine and hypoxanthine into uric acid.
Non Competitive Inhibition
The effects of non competitive inhibition are prolonged. These include:
- Irreversible anticholinestrases e.g., Organophosphate compounds
- MAO Inhibitors e.g., Iproniazid, Phenelzine
- Proton Pump Inhibitors e.g., Omeprazole, Esomeprazole
1. Irreversible anticholinestrases
These include the insecticides and the war gases. These are toxic compounds which can be absorbed through the skin.
Aspirin is an analgesic used in headache, it inhibits cyclooxygenase enzyme in the platelets. It inhibits the synthesis of prostaglandins especially thromboxane A2. Life of platelets is only seven days. On maintenance therapy, aspirin is taken in low doses by cardiac patients.
3. Monoamine oxidase inhibitors
These are used to treat depression. They inhibit the monoamino oxidase enzyme which breaks down catecholamines. Thus decreased levels of noradrenalin and serotonin are coped by MAO inhibitors and increased levels are achieved.
4. Proton pump inhibitors
Proton pump inhibitors inhibit the hydrogen potassium ATPase in parietal cells of stomach, thus inhibit HCl secretion.
Owing to a broad spectrum of functions performed by neuropeptides, this class of signaling molecules attracts an increasing interest. One of the key steps in the regulation of biological activity of neuropeptides is proteolytic conversion or degradation by proteinases that change or terminate biological activity of native peptides. These enzymes, in turn, are regulated by inhibitors, which play integral role in controlling many metabolic pathways. Thus, the search for selective inhibitors and detailed knowledge on the mechanisms of binding of these substances to enzymes, could be of importance for designing new pharmacological approaches. The aim of this review is to summarize the current knowledge on the inhibitors of enzymes that convert selected groups of neuropeptides, such as dynorphins, enkephalins, substance P and NPFF fragments. The importance of these substances in pathophysiological processes involved in pain and drug addiction, have been discussed.
Substrate activation of insulin-degrading enzyme (insulysin). A potential target for drug development
The rate of the insulin-degrading enzyme (IDE)-catalyzed hydrolysis of the fluorogenic substrate 2-aminobenzoyl-GGFLRKHGQ-ethylenediamine-2,4-dinitrophenyl is increased 2-7-fold by other peptide substrates but not by peptide non-substrates. This increased rate is attributed to a decrease in Km with little effect on Vmax. An approximately 2.5-fold increase in the rate of amyloid beta peptide hydrolysis is produced by dynorphin B-9. However, with insulin as substrate, dynorphin B-9 is inhibitory. Immunoprecipitation of differentially tagged IDE and gel filtration analysis were used to show that IDE exists as a mixture of dimers and tetramers. The equilibrium between dimer and tetramer is concentration-dependent, with the dimer the more active form. Bradykinin shifted the equilibrium toward dimer. Activation of substrate hydrolysis is not seen with a mixed dimer of IDE containing one active subunit and one subunit that is catalytically inactive and deficient in substrate binding. On the other hand, a mixed dimer containing one active subunit and one subunit that is catalytically inactive but binds substrate with normal affinity is activated by peptides. These findings suggest that peptides bind to one subunit of IDE and induce a conformational change that shifts the equilibrium to the more active dimer as well as activates the adjacent subunit. The selective activation of IDE toward amyloid beta peptide relative to insulin suggests the potential for development of compounds that increase IDE activity toward amyloid beta peptide as a therapeutic intervention for the treatment of Alzheimer's disease.
Drug metabolizing enzymes are responsible for degradation of drugs and environmental pollutants and are important determinants of drug action. An example is the polymorphism in acetylation that is mediated by N-acetyltransferase isoenzymes NAT1 and NAT2 in the liver [ 53 ]. More than 25 NAT2 genotypes and about 20 NAT1 genotypes have been reported. Based on NAT2 phenotype, individuals are characterized as rapid, intermediate, or slow acetylators. Isoniazid ( Figure 2 ) and some sulfonamides, such as sulfadimidine, are typical substrates for NAT2, while NAT1 metabolizes para-aminosalicylic acid and para-aminobenzoic acid. Caffeine is metabolized by both enzymes. There is significant variation in the distributions of various phenotypes in different parts of the world. The Inuit and Japanese have the lowest rates for slow acetylators (about 10%), while in India it is high at around 60%. This has implications for the metabolism and detoxification of isoniazid. Slow acetylators have an increased risk of peripheral neuropathy during therapy with isoniazid while rapid acetylators are more likely to have treatment failure and relapse if they take isoniazid twice weekly. Hepatotoxic reactions may be more common in slow acetylators, who also have an increased susceptibility to phenytoin toxicity. There is an increased risk of lupus-like syndrome among slow acetylators who take isoniazid, hydralazine, or procainamide.
Figure 2 . Isoniazid metabolism.
Hepatic dysfunction and acetylator status during treatment with isoniazid plus rifampicin has been re-examined in 77 Japanese patients with pulmonary tuberculosis [ 54 ]. There was a marked increase in the risk of hepatotoxicity amongst slow acetylator NAT2* genotypes (a combination of mutant alleles) compared with the rapid acetylator genotype (homozygous NAT2*4) the relative risk was 28 (95% CI = 4.1, 192). Despite a small sample size (seven slow acetylator genotypes, 42 intermediate, and 28 rapid) the relative risk was highly significant, which is not surprising if all seven of the slow acetylators and only one of the 28 rapid acetylators developed hepatotoxicity. A unique feature of this study was the determination of acetylator status by genotyping rather than phenotyping. There is generally good concordance between the two methods, but in the presence of hepatic dysfunction the phenotype assessment may not reflect the genotype. Furthermore, 42 of the 77 patients were assigned to the intermediate acetylator genotype, based on heterozygosity for NAT2*4 and a mutant allele. However, phenotyping by estimation of concentrations of metabolites of the commonly used probes does not consistently result in identification of intermediate acetylators. The Japanese are mostly fast acetylators (
90%) compared with Caucasians or Indians (40–50%). It is unlikely, however, that the observed association between slow acetylator genotype and the high risk of hepatic dysfunction was affected by any of these considerations. The dose of isoniazid was rather large (
8 mg/kg/day) and this may have increased the risk of hepatotoxicity. Furthermore, hepatotoxicity was defined as an increase in transaminases to one and a half times the top of the reference range. This degree of hepatic dysfunction is not uncommon in patients taking antituberculosis drugs, and is no indication for withdrawal or modification of treatment. It is not possible to assess the risk of severe hepatotoxicity during treatment, owing to lack of detailed information in the published report.
The frequency of NAT2 polymorphisms, the NAT2 acetylation profile and its relation to the incidence of gastrointestinal adverse drug reactions, antituberculosis drug-induced hepatotoxicity, and the clinical susceptibility factors for hepatotoxicity have been studied in Brazilian patients taking isoniazid, rifampicin, and pyrazinamide [ 55 ]. Of 254 patients, 69 (27%) were slow acetylators and 185 (73%) were fast acetylators 65 (26%) were HIV-positive 33 (13%) developed gastrointestinal adverse effects and 14 (5.5%) developed hepatotoxicity. Of the latter, nine were slow acetylators and five were fast acetylators. Slow acetylator status and HIV infection were identified as susceptibility factors for hepatotoxicity, but not age, sex, hepatitis C virus infection, alcohol abuse, or baseline transaminase activities.
Polymorphisms of the NAT2 and/or CYP2E1 genes have been studied in 132 Korean patients, of whom 18 developed antituberculosis drug-induced hepatotoxicity [ 56 ]. Slow NAT2 acetylators had a higher incidence of hepatotoxicity than rapid acetylators (37% versus 9.7%) and had a 3.8-fold greater risk of hepatotoxicity. There was no significant association between any CYP2E1 genotype and antituberculosis drug-induced hepatotoxicity.
Furin as SARS-CoV-2’s partner in crime
In May, a group at the Germany Primate Centre led by Stefan Pöhlmann reported that initial cleavage of the S1 and S2 subunits of the spike protein is essential for SARS-CoV-2 entry into human lung cells and for the virus to fuse infected cells with other cells. This fusion phenomenon, called syncytium, is driven by the so-called fusion peptide that is released by the spike as it is cleaved.
A French group reported that cells infected with SARS-CoV-2 are studded with spike proteins, which fuse with neighbors sporting ACE2 surface enzymes. Fused cells have been seen in postmortem lung tissues of COVID-19 patients. This cell-cell fusion has also been observed in tissues from people infected with the coronaviruses that cause SARS and MERS.
Syncytium allows virus-infected cells join other cells, creating contiguous viral factories of 10 to 20 cells. The strategy enables the virus to hijack new cells without exiting into the body, thus eluding immune surveillance. The French group concluded that cell-cell fusion is a feature of severe COVID-19 and is facilitated by TMPRSS2, another protease involved in snipping the spike protein.
Another German group reported in July evidence backing TMPRSS2’s importance in infection and that SARS-CoV-2 replication was strongly inhibited by a synthetic furin inhibitor, MI-1851, in human airway cells.
In a preprint posted to bioRxiv in August, Vineet Menachery of the University of Texas Medical Branch and his colleagues reported that deletion of the spike protein’s furin cleavage site from SARS-CoV-2 ramped up replication in a monkey cell line, but attenuated replication in respiratory cells and pathogenesis in vivo in hamsters.
“My expectation would be that the same holds true for SARS-CoV-2 in infected human patients,” Pöhlmann says of the hamster result. As for the conflicting data from the monkey cells, the researchers are still looking for answers.
There are two main types of drug interaction: pharmacokinetic and pharmacodynamic. Pharmacokinetic interactions involve the effect of one drug on the absorption,metabolism, excretion or protein binding of another drug. On the other hand,pharmacodynamic interactions are caused by several effects (additive, synergistic or antagonistic effects) of the combined treatment at the site of biological activity,changing the pharmacological action of the drugs, even at standard blood concentrations. Pharmacokinetic interactions focused on P450 are described in this paper. The incidence of side-effects is markedly higher in the elderly and those with more severe symptoms. Thus, understanding the mechanism underlying drug interactions is useful, not only in preventing drug toxicity or adverse effects, but also in devising safer therapies for disease.
4 Enzyme Production Through Solid-State Fermentation
Solid-state fermentation has a great potential for enzyme production [ 18 ]. This process offers several advantages over the SmF process, such as high product titer, lesser effluent generation, use of simple fermentation equipment, less trained labor, and so on [ 27 ]. However, SSF is in general more suitable for those processes where the crude fermented products themselves are used as the final product rather than the isolated enzymes.
Agroindustrial residues are commonly used substrates for the SSF processes, including those used to produce enzymes. A variety of substrates have been used for the cultivation of enzyme producing microorganisms. Examples of the substrates used are wheat bran, rice bran, sugar cane bagasse, wheat straw, rice straw, saw dust, corncobs, banana waste, cassava waste, palm oil mill waste, oil cakes, and so on [ 18, 28 ].
Neglected Diseases: Extensive Space for Modern Drug Discovery
Cecilia Pozzi , . Stefano Mangani , in Annual Reports in Medicinal Chemistry , 2018
2.3.2 Quinazoline Derivatives
The quinazoline derivatives studied as PTR inhibitors can be divided into two classes, 2,4-diaminoquinazoline, first developed toward DHFR enzymes, and 2-amino-4-oxo-quinazoline, previously studied as inhibitors of TS enzymes.
The precursor of the 2,4-diaminoquinazoline class was the compound 2,4,6-triaminoquinazoline (TAQ, Fig. 7 ) for which an IC50 of 2.0 μM was measured toward LmPTR1. 22 The crystal structure of TAQ in complex with LmPTR1 (PDB id 1W0C ) shows the inhibitor occupying the biopterin-binding pocket in which it adopts a methotrexate-like orientation by forming a parallel-displaced interaction within the π-sandwich formed by Phe113 and the cofactor nicotinamide ( Fig. 8 A ). A network of H-bonds entailed with Ser111 and Tyr194 contributes to stabilize TAQ in this site explaining its moderate potency toward LmPTR1.
Fig. 7 . Chemical structures of quinazoline derivatives.
Fig. 8 . Active-site view of (A) LmPTR1 (in cartoon, residues in sticks, light cyan carbon atoms) in complex with 2,4,6-triaminoquinazoline (TAQ ( Fig. 7 ), in sticks, green carbons) (PDB id 1W0C ). 22 (B) LmPTR1 (in cartoon, residues in sticks, light cyan carbon atoms, partner subunit in light green) in complex with CB3717 ( Fig. 7 , in sticks, purple carbons) (PDB id 2BFA ). 18 (C) TbPTR1 (in cartoon, residues in sticks, white carbons) in complex with trimetrexate (5-methyl-6-[(3,4,5-trimethoxyphenyl)aminomethyl]quinazoline-2,4-diamine ( Fig. 7 ), in sticks, cyan carbons) (PDB id 2X9V ). 23 (D) TbPTR1 (in cartoon, residues in sticks, white carbons) in complex with DDD00066641 (D66641, 6-p-tolyl-quinazoline-2,4-diamine ( Fig. 7 ), in sticks, orange carbons) (PDB id 2VZ0 ). 24 Water molecules are shown as red spheres and H-bonds as red dashed lines.
At a later stage, two 2,4-diaminoquinazoline derivatives have been reported as potent PTR inhibitors. Trimetrexate (5-methyl-6-[(3,4,5-trimethoxyphenyl)aminomethyl]quinazoline-2,4-diamine, Fig. 7 ), formerly developed toward DHFR enzymes, 25 resulted also a potent TbPTR1 inhibitor with a reported Ki of
70 nM. 23 The second derivative DDD00066641 (6-p-tolyl-quinazoline-2,4-diamine, Fig. 7 ) was specifically designed to target PTR enzymes improving the potency toward TbPTR1 to a Ki app of 9.8 (± 2.6) nm. 24 The binding mode of both compounds was clarified in their TbPTR1 complexes (trimetrexate, PDB id 2X9V 23 DDD00066641, PDB id 2VZ0 24 ), showing that their quinazoline scaffold adopts a methotrexate-like orientation inside the catalytic cavity (as their precursor TAQ) ( Fig. 8 C and D). Furthermore, this binding mode places the molecular core in an optimal position to optimize the hydrogen-bonding network within the active site, forming interactions with the cofactor, Ser95, Tyr174, and Asp161 (three residues shared by all PTR enzymes). The trimethoxyphenyl moiety of trimetrexate is accommodated in the hydrophobic pocket of the active site lined by Phe97, Val206, Leu209, Pro210, Met213, and Trp221, in which it is stabilized through van der Waals interactions. Despite the reduced flexibility, also the 6-p-tolyl moiety of DDD00066641 is perfectly accommodated within the same hydrophobic cavity in which it entails van der Waals interactions with the same residues.
The second class of quinazoline derivatives includes compounds having an amine and a ketone group in positions 2 and 4, respectively, on the molecular core (2-amino-4-oxo-quinazoline). This type of quinazoline derivatives was formerly studied and developed as TS inhibitors, resulting in the identification of CB3717 ( Fig. 7 ) as potent TS inhibitor having IC50 values in the 30–60 nM range. 26 In vitro assays on 2-amino-4-oxo-quinazoline derivatives toward PTR enzymes showed that this class of compounds is also active as inhibitors of these parasite enzymes (IC50 > 10 μM). 27 Structural analysis performed on CB3717 in complex with LmPTR1 (PDB id 2BFA ) revealed that it adopts a quite peculiar binding mode in the catalytic cavity ( Fig. 8 B). 18 The 2-amino-4-oxoquinazoline core of CB3717 occupies the biopterin-binding pocket in which it assumes a substrate-like orientation. The bicyclic system is stacked in the π-sandwich formed by Phe113 and the cofactor nicotinamide, and it is further stabilized by a network of H-bonds entailed with the cofactor, Arg17, and Ser111. The pABA moiety and the γ-Glu tail of the inhibitor adopt a quite unusual orientation in the catalytic cavity since they are directed toward the wall of the active site formed by the C-terminus of the partner subunit. In this conformation the pABA ring is accommodated in a pocket formed by the side chains of Leu188, Leu226, Leu229, and His241. At the bottom of the cavity the amide oxygen of the pABA moiety accepts two H-bonds from Tyr283 and Arg287′ (of the neighboring subunit). The terminal γ-Glu is placed on the solvent-exposed protein surface where it entails water-mediated interactions with the C-terminal residues of the neighboring subunits.
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Extended Data Fig. 1 Engineering an acoustic sensor of TEV endopeptidase activity.
a, Coomassie-stained SDS-PAGE gel of OD500nm-matched samples of GVWT incubated with dTEV and TEV protease, before and after buoyancy purification (labeled pre b.p. and post b.p., respectively). N = 3 biological replicates. b, Scatter plots showing normalized OD500nm of GVSTEV as a function of hydrostatic pressure. (N = 3 biological replicates for GVSTEV + TEV and N = 4 for GVSTEV + dTEV.) c, Scatter plots showing the ratio of nonlinear (x-AM) to linear (B-mode) ultrasound signal as a function of applied acoustic pressure for all the replicate samples used in the x-AM voltage ramp imaging experiments for GVSTEV. N = 3 biological replicates and total number of replicates is 8. d, Scatter plots showing normalized OD500nm of GVWT as a function of hydrostatic pressure. (N = 3 biological replicates for GVWT + dTEV and N = 4 for GVWT + TEV.) e, Representative ultrasound images of agarose phantoms containing GVWT incubated with TEV or dTEV protease at OD500nm 2.2. The B-mode image was acquired at 132kPa and the x-AM image at 569 kPa. Similar images acquired for N = 3 biological replicates, with each N consisting of 3 technical replicates. CNR stands for contrast-to-noise-ratio, and color bars represent relative ultrasound signal intensity on the dB scale. Scale bars represent 1 mm f, Scatter plots showing the ratio of nonlinear (x-AM) to linear (B-mode) ultrasound signal as a function of applied acoustic pressure for all the replicate samples used in the x-AM voltage ramp imaging experiments for GVWT. N = 3 biological replicates, with each N consisting of 3 technical replicates. Solid curve represents the mean of all the replicates.
Extended Data Fig. 2 Engineering an acoustic sensor of calpain activity.
a, Individual scatter plots for Fig. 2b. N = 5 biological replicates for +Calp/+Ca 2+ , 6 for -Calp/+Ca 2+ and +Calp/-Ca 2+ , 7 for -Calp/-Ca 2+ . b, Coomassie-stained SDS-PAGE gel of OD500nm-matched samples of GVScalp incubated in the presence (+) or absence (-) of calpain (first + /-) and calcium (second + /-), before and after buoyancy purification (labeled pre b.p. and post b.p. respectively). N = 3 biological replicates. c, Representative TEM images of GVScalp after incubations in the presence or absence of calpain and/or calcium. Scale bars represent 100 nm. At least 20 GV particles were imaged for each condition. d, DLS measurements showing the average hydrodynamic diameter of GVScalp and GVWT samples after calpain/calcium incubations (N = 2 biological replicates for GVScalp + /-, +/+, GVWT + / + and 3 for other conditions, individual dots represent each N and horizontal line indicates the mean). Error bars indicate SEM when N = 3. e–g, Individual scatter plots for Fig. 2d, f, h. N = 3 biological replicates with each N consisting of 2 technical replicates (total number of replicates is 18 for + /+ and 6 for each of the remaining conditions). Solid line represents the mean of all the replicates for (a, e–g). h, Scatter plots for Fig. 2i N = 3 biological replicates, individual dots represent each N and solid blue line showing the fitted curve (a Hill equation with a coefficient of 1, with a half-maximum effective concentration (EC50) of 140 μM).
Extended Data Fig. 3 Characterization of GVWT sample with calpain protease.
a–c, Representative ultrasound images of agarose phantoms containing GVWT incubated in the presence (+) or absence (-) of calpain (first + /-) and calcium (second + /-), at OD500nm 2.2. The B-mode images were taken at 132 kPa for a, b and c and the x-AM images corresponding to the maximum difference in non-linear contrast between the + /+ sample and the negative controls were taken at 438 kPa for a, b and at 425 kPa for c. CNR stands for contrast-to-noise-ratio and color bars represent ultrasound signal intensity in the dB scale. Scale bars represent 1 mm. N = 2 biological replicates for a–c. d–f, Scatter plots showing the ratio of x-AM to B-mode ultrasound signal as a function of increasing acoustic pressure for GVWT after incubation in the presence or absence of calpain and/or calcium (N = 2 biological replicates). g, Hydrostatic collapse curves of GVWT after incubations in the presence (+) or absence (-) of calpain and/or calcium. The legend lists the midpoint collapse pressure for each condition (±95% confidence interval) determined from fitting a Boltzmann sigmoid function (N = 5 biological replicates for -/+ and N = 6 for other conditions) h, Coomassie-stained SDS-PAGE gel of OD500nm-matched samples of GVWT incubated in the presence (+) or absence (-) of calpain/calcium, before and after buoyancy purification (labeled pre b.p. and post b.p., respectively, N = 1). Individual dots in d–g represent each N and solid line represents the mean of all the replicates.
Extended Data Fig. 4 Engineering an acoustic sensor of ClpXP proteolytic activity.
a, b, Scatter plots for Fig. 3d, g. N = 5 biological replicates. c, Coomassie-stained SDS-PAGE gel of OD500nm-matched GVWT samples incubated in a reconstituted cell-free transcription-translation (TX-TL) system containing a protease inhibitor cocktail or ClpXP. N = 3 biological replicates. d, Coomassie-stained SDS-PAGE gel of 30x diluted content of TX-TL system containing ClpXP. N = 2 biological replicates(e) DLS measurements showing the average hydrodynamic diameter of GVSClpXP and GVWT samples, after incubations with protease inhibitor or ClpXP (N = 2 biological replicates, individual dots represent each N and horizontal line indicates the mean). f, g, Scatter plots showing the ratio of x-AM to B-mode acoustic signal as a function of applied acoustic pressure for all the replicate samples used in the x-AM voltage ramp experiments for GVSClpXP (f) and GVWT (g). N = 3 biological replicates, with each N consisting of 3 technical replicates. Individual dots represent each N and solid line represents the mean of all the replicates for a, b, f, g.
Extended Data Fig. 5 Constructing intracellular acoustic sensor genes for dynamic monitoring of protease activity and circuit-driven gene expression.
a, Normalized pressure-sensitive optical density at 600 nm of WT Nissle cells expressing either ARGWT or ASGClpXP. The legend lists the midpoint collapse pressure for each cell type (±95% confidence interval) determined from fitting a Boltzmann sigmoid function (N = 5 biological replicates and 8 total replicates for ASGClpXP N = 3 biological replicates for ARGWT and 6 total replicates). b, Representative ultrasound images of WT Nissle cells expressing either ARGWT or ASGClpXP at OD600nm 1.5 (N = 4 biological replicates and the number of total replicates is 10). c, Scatter plots showing x-AM/B-mode ratio as a function of applied acoustic pressure for WT Nissle cells expressing either ARGWT or ASGClpXP at OD600nm 1.5 (N = 4 biological replicates and the number of total replicates is 10). d, Scatter plots for Fig. 4b, N = 3 biological replicates. e, f, Scatter plots showing the ratio of x-AM to B-mode acoustic signal as a function of acoustic pressure for all the replicate samples used in the x-AM voltage ramp experiments for ΔclpXP Nissle cells expressing ASGClpXP and araBAD driven clpXP, with or without L-arabinose induction (e) and WT Nissle cells expressing ASGClpXP and pTet-TetO driven WT gvpC, with or without aTc induction (f). N = 3 biological replicates, with each N having 3 technical replicates for (e) and N = 5 biological replicates for (f). Individual dots represent each N and solid line represents the mean of all the replicates for a, c–f.
Extended Data Fig. 6 Schematic illustrating the in vivo ultrasound imaging experiment.
Cells in cylindrical hydrogel with the indicated cross-sectional arrangements were injected into the GI tract of mice and imaged with ultrasound.
Extended Data Fig. 7 Ultrasound imaging of bacteria expressing acoustic sensor genes in the gastrointestinal tract of mice.
a, Schematic illustrating two orientations of the wild type (WT) E. coli Nissle cells expressing ARGWT or ASGClpXP introduced into the mouse colon as a hydrogel. b, c, Representative transverse ultrasound images of the colon for two mice used in the in vivo imaging experiments, with orientation #1 (b) and with orientation #2. (c). Cells are injected at a final concentration of 1.5E9 cells ml -1 . B-mode signal is displayed using the bone colormap and x-AM signal is shown using the hot colormap. Color bars represent B-mode and x-AM ultrasound signal intensity in the dB scale. Scale bars represent 2 mm. d, e, B-mode and xAM contrast-to-noise ratio (CNR) in vivo, for WT Nissle cells expressing ARGWT or ASGClpXP in orientation #1 (d) and orientation #2. (e). N = 5 mice for orientation #1 (b, d) and N = 4 mice for orientation #2 (c, e). Error bars indicate SEM. P = 0.0014 for x-AM signal from cells expressing ASGClpXP versus the ARGWT control in orientation #1, and P = 0.0016 for that in orientation #2. P = 0.0570 for B-mode signal in orientation #1 and P = 0.3445 in orientation #2. P-values were calculated using a two-tailed paired t-test. Individual dots represent each N and horizontal line indicates the mean.
Extended Data Fig. 8 ASGClpXP -expressing cells showed higher contrast to tissue with nonlinear imaging.
B-mode and xAM contrast-to-tissue ratio (CTR) in vivo, for WT Nissle cells expressing ARGWT or ASGClpXP in both orientations. P = 7.8E-5 for the CTR from xAM imaging of cells expressing ASGClpXP versus CTR from xAM imaging of cells expressing ARGWT. P = 1.4E-6 for the CTR from xAM imaging of cells expressing ASGClpXP versus CTR from B-mode imaging of cells expressing ASGClpXP and P = 4.9E-7 for the CTR from xAM imaging of cells expressing ASGClpXP versus CTR from B-mode imaging of cells expressing ARGWT. Individual dots represent each N, and the thick horizontal line indicates the mean. Error bars indicate SEM. N = 9 mice. P-values were calculated using a two-tailed paired t-test for each comparison independently. Individual dots represent each N and horizontal line indicates the mean.
Extended Data Fig. 9 Absence of memory effect from imaging at sequentially increasing acoustic pressure.
Ratio of sensor-specific signal (xAM/B-mode) acquired at the indicated acoustic pressures in the process of voltage ramping (comprising 36 points from 458 kPa to 1.6 MPa) or stepping the transducer output directly to corresponding pressure in a single step, for WT Nissle cells expressing either ARGWT or ASGClpXP. N = 3 biological replicates, with each N having 3 technical replicates. Individual dots represent each replicate, and the thick horizontal line indicates the mean. Error bars indicate SEM derived from biological replicates (see Online Methods).