Optimization of E. coli growth in D₂O (heavy water)

Optimization of E. coli growth in D₂O (heavy water)

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I would like to find a method of increasing the biomass of my D2O cultures because my current method is not yielding enough protein. I would like to also minimize the amount of H2O in my culture.

My current methodology is a small scale growth of 3mL with 75% D2O and LB grown for approximately 9hrs. This culture is then used to seed a 20mL 100% D2O and M9 culture overnight and this entire culture is used to seed a 1L D2O and M9 culture.

Thanks for your time.

It looks like you're very stringently avoiding $^1mathrm{H}$. You may want to consider replica plating your transformations onto $mathrm{D}_2mathrm{O}$ plates (selecting for $mathrm{D}_2mathrm{O}$ tolerance earlier.)

I assume you're doing protein NMR and want "triple labeling." Depending on how specific your carbon labelling is, you may want to grow your starter cultures on triple labeled agal hydrolyzate (something like "Bioexpress.") You can buy some unlabeled stuff for fairly cheap ($55 USD at CIL for 10 mL of 10X stock) and if your starters grow well you can spring for the same quantity of $^2mathrm{H}$,$^{13}mathrm{C}$,$^{15}mathrm{N}$ for around $405.

Optimization of E. coli growth in D₂O (heavy water) - Biology

a University of Graz, Institute of Molecular Biosciences, NAWI Graz, 8010 Graz, Austria, b BioTechMed Graz, 8010 Graz, Austria, c Field of Excellence BioHealth – University of Graz, Graz, Austria, d Institut Laue–Langevin, 38043 Grenoble, France, and e University of Tennessee, Center for Environmental Biotechnology, Knoxville, Tennessee, USA
* Correspondence e-mail: [email protected], [email protected]

A previously reported multi-scale model for (ultra-)small-angle X-ray (USAXS/SAXS) and (very) small-angle neutron scattering (VSANS/SANS) of live Escherichia coli was revised on the basis of compositional/metabolomic and ultrastructural constraints. The cellular body is modeled, as previously described, by an ellipsoid with multiple shells. However, scattering originating from flagella was replaced by a term accounting for the oligosaccharide cores of the lipopolysaccharide leaflet of the outer membrane including its cross-term with the cellular body. This was mainly motivated by (U)SAXS experiments showing indistinguishable scattering for bacteria in the presence and absence of flagella or fimbrae. The revised model succeeded in fitting USAXS/SAXS and differently contrasted VSANS/SANS data of E. coli ATCC 25922 over four orders of magnitude in length scale. Specifically, this approach provides detailed insight into structural features of the cellular envelope, including the distance of the inner and outer membranes, as well as the scattering length densities of all bacterial compartments. The model was also successfully applied to E. coli K12, used for the authors' original modeling, as well as for two other E. coli strains. Significant differences were detected between the different strains in terms of bacterial size, intermembrane distance and its positional fluctuations. These findings corroborate the general applicability of the approach outlined here to quantitatively study the effect of bactericidal compounds on ultrastructural features of Gram-negative bacteria without the need to resort to any invasive staining or labeling agents.

1. Introduction

Escherichia coli is among the most studied Gram-negative bacterial strains in life sciences, with numerous reports on its structure and composition (Breed & Dotterrer, 1916 Lieb et al. , 1955 Maclean & Munson, 1961 Neidhardt et al. , 1990 Seltmann & Holst, 2002 Silhavy et al. , 2010 ). Transmission electron microscopy (TEM) has been an indispensable tool to derive the cell's ultrastructure, i.e. the few tens of nanometres thick structure of the bacterial cell wall (Milne & Subramaniam, 2009 ). It took, however, significant efforts to minimize limitations originating from the invasive nature of the technique (Hobot et al. , 1984 Matias et al. , 2003 ). Moreover, TEM does not allow dynamic studies of the cellular ultrastructure under physiological relevant conditions and thus real-time insight on the modification of E. coli by bactericidal compounds, such as antimicrobial peptides. Time-resolved small-angle scattering experiments (Huxley et al. , 1980 ), capable of probing structural heterogeneities on the (sub)micrometre to subnanometre length scales without the need of using either bulky labels or invasive staining techniques, are possible solutions for this issue [see e.g. Zemb & Lindner (2002 ) for an overview on scattering techniques]. In terms of static, equilibrium experiments, small-angle neutron scattering (SANS) has been used, for example, to probe the response of thylakoid membranes in live chloroplasts to external stimuli (Liberton et al. , 2013 Nagy et al. , 2014 ), while a principle component analysis of small-angle X-ray scattering (SAXS) data was applied to get some qualitative insight on the effect of antibiotics on E. coli (von Gundlach, Garamus, Gorniak et al. , 2016 von Gundlach, Garamus, Willey et al. , 2016 ).

Obtaining quantitative insight on a live cell's ultrastructure using either SAXS or SANS is challenging, however. This is simply because both techniques provide a global average of the entire cellular content, making it very difficult to single out individual contributors (Semeraro et al. , 2020 ). This can be addressed with extensive use of the contrast variation capabilities of SANS. For example Nickels et al. (2017 ) were able to grow fully deuterated Bacillus subtilis fed with mixtures of protiated and deuterated fatty acids, which allowed them to highlight nanoscopic domains within the bacteria's cytoplasmic membrane using SANS. Yet, it is also possible to obtain insight on cellular ultrastructure without the need to grow bacteria in D 2 O. In particular, we recently reported a multi-scale model that successfully describes the scattered intensities of live E. coli K12 originating from ultra-SAXS (USAXS), SAXS and SANS experiments at five D 2 O/H 2 O ratios (Semeraro et al. , 2017 ). Specifically, the model applies a core–shell description of the cell's body, composed of an ellipsoidal cytoplasmic space and a multilayered cellular wall, and includes contributions from flagella in terms of self-avoiding polymer chains (Doi & Edwards, 1988 ). The last component was found to significantly contribute at intermediate to high magnitudes of the scattering vector q .

Continuing our efforts to use elastic scattering techniques for exploiting the ultrastructure of E. coli led us to perform SAXS experiments on E. coli ATCC 25922 with either regular or short flagella, and the ATCC flagellum-free mutant Δ fliC with the surprising result of basically superimposable scattering patterns (see Fig. S1 in the supporting information). This prompted us to thoroughly revise our analytical form factor model on the basis of robust estimates of the molecular composition of the bacteria and their structural integration. These estimates include the sizes, volumes, concentrations and distributions of all major bacterial components.

Briefly, the most important changes of our revised bacterial model are as follows: (i) constraints for the average scattering length densities (SLDs) of different cell compartments were derived by considering their constituting macromolecules as separate bodies, including estimates of SLDs of the metabolome and membrane (ii) contributions from flagella were replaced by the oligosaccharide cores of lipopolysaccharide (LPS), modeled as grafted polymers and (iii) variations of the inter-membrane distance are modeled by a log-normal probability distribution function (PDF) along with removing the negligible polydispersity over the cell radius from the model.

The model was tested against USAXS/SAXS and very small angle neutron scattering (VSANS)/SANS data at ten different contrasts of E. coli ATCC 25922, yielding highly satisfactory fits over the complete range of recorded scattering vector magnitudes (3 ×󈇾 𕒷 < q < 7 nm 𕒵 ). These tests also include a more complex model, considering a heterogeneously structured cytosol and including specifically the scattering originating from ribosomes. Our analysis showed, however, that the ribosome contribution to the overall scattering is overwhelmed by that of the cell wall. The new model was also successfully applied to USAXS/SAXS data of E. coli K12, previously used for devising our multi-scale model (Semeraro et al. , 2017 ), as well as the fimbra-free JW4283 and the strain Nissle 1917, revealing distinct differences in ultrastructural features.

The paper is structured as follows. First we briefly summarize the experimental methods and samples, before we detail the revised modeling in Section 3 , including a comprehensive list of compositional data in the supporting information. Section 4 describes an analytical model for scattering from a heterogeneous cytosol accounting for ribosomes and Section 5 summarizes the involved parameters and applied optimization strategy. Results of applying the modeling to experimental data of five different E. coli strains are described and discussed in Section 6 , before we conclude in Section 7 .

2. Materials and methods

2.1. Bacterial samples

Bacterial colonies of E. coli strains ATCC 25922, K12 5K, K12 JW4283 (Baba et al. , 2006 ) and Nissle 1917 (Sonnenborn, 2016 ) were grown in lysogeny broth (LB)–agar (Carl Roth, Karlsruhe, Germany) plates at 310 K. Overnight cultures (ONCs) were derived from these colonies by inoculating a single colony in 3 ml of LB medium (Luria/Miller, Carl Roth) in sterile polypropylene conical tubes (15 ml), allowing for growth under aerobic conditions for 12󈝼 h in a shaking incubator at 310 K. Main cultures were prepared by suspending an aliquot of the ONCs in 10 ml of LB medium in 50 ml sterile polypropylene conical tubes, allowing for bacterial growth under the same conditions as applied to ONCs up to the middle of the exponential growth phase. Cells were then immediately washed twice and re-suspended in nutrient-free and isotonic phosphate-buffered saline (PBS) solution (phosphate buffer 20 m M , NaCl 130 m M ) at pH 7.4 (Sigma Aldrich, Vienna, Austria). Turbidity measurements were used to control the bacterial concentration. Optical density values at wavelength λ = 600 nm (OD 600 ) were acquired with the spectrophotometer Thermo Spectronic Genesys 20 (Thermo Fisher Scientific, Waltham, MA, USA) [OD 600 = 1 ≃ 8 ×󈇾 8 colony forming units (CFU) per millilitre]. In these samples, 1 CFU corresponds to one single cell. In the case of samples containing D 2 O, bacterial suspensions were washed twice with either PBS or 90 wt% D 2 O PBS solutions, in order to obtain two concentrated stock solutions for both buffer conditions. These two stocks were mixed and diluted down to the required bacterial concentrations and D 2 O contents according to the experimental settings.

The preparation of the ATCC samples was conducted with the maximum care in order to preserve the integrity of the flagella. ATCC cells with mechanically fragmented flagella were prepared by shearing the suspension five times through a 3 ml syringe equipped with a 22-gauge needle, as described by Turner et al. (2012 ). The suspension was washed in PBS to eliminate flagellum fragments in the supernatant.

The ATCC 25922 Δ fliC strain was constructed by phage transduction as described by Silhavy et al. (1984 ). The P1vir phage was propagated on strain YK4516 (Komeda et al. , 1980 ) using the plate lysate method. YK4516 contains a Tn10 insertion in fliC (fliC5303::Tn10) and was purchased from The Coli Genetic Stock Center (Yale University, New Haven, CT, USA). Strain ATCC 25922 served as recipient. Transductands were selected on tetracycline-containing plates (10 µg ml 𕒵 ) and the fli − phenotype was confirmed by spotting on semi-solid agar plates (0.3% agarose).

2.2. Experimental setup

2.2.1. USAXS

USAXS/SAXS measurements were performed on the TRUSAXS beamline (ID02) at the ESRF, Grenoble, France. The instrument uses a monochromatic beam that is collimated in a pinhole configuration. Measurements were performed with a wavelength of 0.0995 nm and sample-to-detector distances of 30.8, 3.0 and 1.2 m, covering a q range of 0.001𔃅 nm 𕒵 (Narayanan et al. , 2018 ). The measured two-dimensional scattering patterns were acquired on a Rayonix MX170 detector, normalized to absolute scale and azimuthally averaged to obtain the corresponding one-dimensional USAXS/SAXS profiles. The normalized cumulative background from the buffer, sample cell and instrument was subtracted to obtain the final I ( q ). Samples with a bacterial concentration of 󕽺 10  cells ml 𕒵 were measured at 310 K and contained in quartz capillaries of 2 mm diameter, mounted on a flow-through setup in order to maximize the precision of the background subtraction.

2.2.2. VSANS

VSANS/SANS measurements were acquired on the D11 instrument at the Institut Laue–Langevin (ILL), Grenoble, France, with a multiwire 3 He detector of 256 ×� pixels (3.75 ×𔀵.75 mm). Four different setups (sample-to-detector distances of 2, 8, 20.5 and 39 m with corresponding collimations of 5.5, 8, 20.5 and 40.5 m, respectively), at a wavelength λ = 0.56 nm ( Δ λ / λ = 9%), covered a q range of 0.014𔃁 nm 𕒵 . To reach very low q , a combination of large wavelength ( λ = 2.1 nm), two focusing MgF 2 lenses and a mirror to cancel deleterious gravity effects (loss of neutrons in the collimation and loss of resolution due to gravity smearing on the detector) were used the setup is described by Cubitt et al. (2011 ). Samples (concentration 󕽺 10  cells ml 𕒵 ) were measured at 310 K and contained in quartz Hellma 120-QS banjo-shaped cuvettes of 2 mm pathway. They were mounted on a rotating sample holder, which prevented the bacteria from sedimenting. Data were reduced with the Lamp program from ILL, performing flat-field, solid angle, dead time and transmission correction, normalizing by incident flux (via a monitor), and subtracting the contribution from an empty cell. The experimental setup and data are available at

2.2.3. In-house SAXS

A SAXSpace compact camera (Anton Paar, Graz, Austria) equipped with an Eiger R 1 M detector system (Dectris, Baden-Daettwil, Switzerland) was used for laboratory SAXS experiments. Cu  K α ( λ = 1.54 Å) X-rays were provided by a 30 W-Genix 3D microfocus X-ray generator (Xenocs, Sassenage, France). Samples were taken up in glass capillaries (diameter: 1 mm Anton Paar) and equilibrated at 310 K for 10 min prior to measurement using a Peltier-controlled sample stage (TC 150, Anton Paar). The total exposure time was 30 min (6 frames of 5 min), setting the sample-to-detector distance to 308 mm. Data reduction, including sectorial data integration and corrections for sample transmission and background scattering, was performed using the program SAXSanalysis (Anton Paar).

2.2.4. Dynamic light scattering

Measurements were performed using a Zetasizer Nano ZSP (Malvern Panalytical, Malvern, UK). ATCC 25922 and K12 5K samples (concentration 󕽺 7  cells ml 𕒵 ) were suspended in glass cuvettes of 1 cm path length and equilibrated at 310 K. This bacterial concentration provided an optimal signal-to-noise ratio and avoided multiple-scattering effects. Data were automatically analyzed by the supplied software (Malvern), which, via a standard cumulant analysis, provided a monomodal probability distribution of the bacterial population as a function of the hydrodynamic radius R H . Each distribution had an average polydispersity index of 𕙘.25, due to the variations of the cell lengths during growth and division. Average R H values and the associated errors were calculated from 18 measurements (six frames of three different sample volumes). The absence of energy sources in PBS and vigorous vortexing of the samples (fragmentation of flagella) minimized cell motility, making Brownian random walk the dominant dynamics of this sample.

3. Overall scattering contributions and compositional modeling

A holistic description of elastic scattering from complex Gram-negative prokaryotic cells can be derived by considering first their prevalent molecular and supramolecular components, each of them having a well defined range of lengths, volumes and densities, which serve as a guide to construct a comprehensive scattering-form-factor model. The here-applied scattering contribution estimates are based on the latest experimental and computational reports on E. coli , including isolated cell components. Specifically, we used compositional and structural information about E. coli and its cell wall (Neidhardt et al. , 1990 Seltmann & Holst, 2002 Schwarz-Linek et al. , 2016 ) the cytoplasmic space and its components (Zimmerman & Trach, 1991 Tweeddale et al. , 1998 Prasad Maharjan & Ferenci, 2003 Bennett et al. , 2009 Guo et al. , 2012 Lebedev et al. , 2015 ) the bacterial ultrastructure (Hobot et al. , 1984 Beveridge, 1999 Matias et al. , 2003 ) the lipid membrane composition and structure (De Siervo, 1969 Oursel et al. , 2007 Lohner et al. , 2008 Pandit & Klauda, 2012 Kučerka et al. , 2012 , 2015 Leber et al. , 2018 ) the LPS specifics (Heinrichs et al. , 1998 Müller-Loennies et al. , 2003 Kučerka et al. , 2008 Kim et al. , 2016 Rodriguez-Loureiro et al. , 2018 Micciulla et al. , 2019 ) the periplasmic space (Burge et al. , 1977 Labischinski et al. , 1991 Pink et al. , 2000 Gan et al. , 2008 ) and the external bacterial components (Yamashita et al. , 1998 Whitfield & Roberts, 1999 Stukalov et al. , 2008 Turner et al. , 2012 ). All this information has been condensed into SLDs ρ , which are summarized in Fig. 1 . For a detailed description, see the supporting information (SI).

Figure 1
Schematic of E. coli structure and composition, including typical sizes, as well as X-ray and neutron SLDs of the most relevant constituents. The bacterial shape is conveniently modeled by an ellipsoid, as detailed by Semeraro et al. (2017 ).

The benefit of this detailed description becomes clear when considering the ability of SANS to nullify or enhance contrast for a given molecular entity, depending on the applied H 2 O/D 2 O ratio. For homogeneous scatterers in a dilute regime, i.e. when their volume fraction , the forward scattering intensity, I (0), is related to the scattering invariant, Q , as

where V is the volume of the scatterer and 〈 Δ ρ 2 〉 is the average squared scattering contrast (Porod, 1982 ). For inhomogeneous systems, such as complex live cells, the average contrast can be calculated as a volume-fraction-weighted average of the contrast of each cell compartment/species , where ϕ i is the volume fraction of the i th component. Hence, the estimates ϕ i and Δ ρ i enable us to approximate Q for all bacterial components [Fig. 2 see also Nickels et al. (2017 )]. This approximation leads to a `matching' point of the entire cell at about 40 wt% D 2 O. At higher D 2 O content the total scattering intensity is increasingly dominated by the acyl chains of the membrane lipids, because they are devoid of water. Toward lower D 2 O contents, cytoplasmic components, such as ribosomes and proteins, are the dominating scattering contributors in turn.

Figure 2
Square root of the estimated Porod invariant Q as a function of D 2 O wt%, calculated for each component using equation (1) and multiplied by the cell volume. The inset shows the differences between the contribution of the LPS oligosaccharide cores (solid green) and flagella (dashed pink).

3.1. Multi-scale scattering model

As reported previously (Semeraro et al. , 2017 ), the main body of E. coli can be described in terms of the scattering amplitude of an ellipsoid with multiple shells (multi-core–shell):

where ρ i are the SLDs given by the compositional E. coli averages of each shell of width Δ i ( ρ M +1 is the SLD of the buffer) ψ is the angle related to every possible orientation of a prolate in suspension R 1 is the minor radius of the cytoplasmic core (CP) and ɛ > 1 is the ratio between the major, ɛ R 1 , and minor radii of the ellipsoid. Furthermore,

is the product of the volume and normalized scattering amplitude of the ellipsoid (Pedersen, 1997 ), where

Despite the fact that cylinders would be more realistic bacterial shape models, prolates do not incur instabilities during fitting and differences between a prolate or cylinder geometries are negligible in reciprocal space (Semeraro et al. , 2017 ). A significant difference from the previous model (Semeraro et al. , 2017 ) relates to the ρ i values used as fitting constraints. Here, we considered, given the available q range, scattering contributions from every macromolecular species (proteins, ribosomes, DNA etc .) individually for calculating the average SLDs. This affects, compared with our previously used values, in essence the ρ i estimates of the cytoplasm – now based on metabolomic analysis – and the phospholipid membranes (Fig. 1 ). In contrast to elastic scattering experiments on lipid-only mimics, structural parameters of each single bilayer cannot be resolved in the context of a whole-cell analysis (Semeraro et al. , 2020 ). Hence, the Δ i and ρ i values of both membranes were considered as fixed parameters (see Fig.ف more details about Δ i and ρ i are given in the SI).

The volume fraction of the bacterial suspensions was 𕟨.007 hence the presence of an inter-cellular structure factor of interactions is unlikely.

Arguably the most distinct differences compared with the former model result from flagella, whose scattering was previously added in terms of a polymer-like structure factor yielding significant contributions to I ( q ) for q > 0.06 nm 𕒵 (Semeraro et al. , 2017 ). Surprisingly, however, a comparison of SAXS data of native ATCC 25922, ATCC with physically broken, short flagella (Turner et al. , 2012 ) and the flagellum-free Δ fliC ATCC mutant showed indistinguishable scattering intensities in the q range previously thought to be dominated by flagella (Fig. S1). Consequently, flagella do not contribute significantly to the E. coli scattering signal. Note that the integrity of flagellar filaments in common bacterial cultures is not guaranteed, as excessive centrifuging or careless sample manipulation steps easily lead to their fragmentation (Schwarz-Linek et al. , 2016 ). Even if we cannot guarantee that the reference ATCC sample possessed fully intact flagella, the bacterial suspensions used in this work were prepared with the utmost care. The same sample preparation protocol allowed us to obtain motile bacteria (Semeraro et al. , 2018 ), suggesting that the flagellar integrity was preserved at least to some degree.

Attempting to rectify the missing scattering intensities in our multi-scale model led us to consider contributions originating from the oligosaccharide (OS) inner and outer cores. Initially, the function was modified by a new shell describing the OS cores, which resulted in nonphysical results (Fig. S2). In particular, ρ X-ray ≃ 15 ×󈇾 𕒸  nm 𕒶 for the OS layer suggested that water is expelled, which is inconsistent with neutron reflectometry experiments on supported LPS layers showing that the hydration of the inner and outer cores ranges from 40 to 80 vol% (Clifton et al. , 2013 Rodriguez-Loureiro et al. , 2018 Micciulla et al. , 2019 ). We therefore decided to model the OS cores in terms of grafted blocks on the outer cell surface. Each core was approximated by a Gaussian chain polymer, entailing the application of the polymer-grafted colloid formalism (Pedersen & Gerstenberg, 1996 ). The scattering form factor of such a system is given by (Pedersen, 2000 )

where N OS is the number of OS cores and β OS = V OS ( ρ OS − ρ BF ) is the product of each volume and SLD contrast to the buffer,

is the structure factor of a Gaussian chain, and

its scattering amplitude. x = ( qR g ) 2 , where R g is the radius of gyration of the OS core. The term Φ ( q ,  ψ ) in equation (5) is related to the `cross-term' resulting from a uniform distribution of OS cores all along the ellipsoidal surface of a single bacterium (Pedersen, 2000 ) and is given by

where, in accordance with (4) , + . Here, R outer = R 1 + Δ outer is the radius describing the external surface of the cell.

The total scattering intensity for a suspension of live E. coli cells of number density n then reads as

where is the orientational average and describes the polydispersity of the thickness of the periplasmic space. Specifically, we applied a log-normal distribution function L ( r ). The constant in equation (9) takes into account scattering background at high q originating from unidentified contributions. The log-normal distribution of the periplasmic thickness takes care of the lower cut-off in intermembrane distance fluctuations, given by the finite size of the cell-wall architecture. Note that cell size variations were not considered due to overparameterization in fact, the polydispersity over the cell radius brought about insignificant changes in the middle to high q range, i.e. q ≥ 0.05 nm 𕒵 .

4. Considering a heterogeneously structured cytosol

It is legitimate to question whether the internal cytosolic structure can be resolved at least in part by SAXS/SANS. In order to address this issue, we derived a more complex analytical scattering function that can be tested against experimental data.

Let us start by considering the general case of a sphere (radius: R 0 SLD: ρ 0 ) suspended in a medium of ρ M , containing N smaller identical spherical beads ( R 1 , ρ 1 ), where the relative distance between two beads r i < R 0R 1 . The scattering amplitude of this system is

where β 0 = ( ρ 0 − ρ M ) V 0 and β 1 = ( ρ 1 − ρ 0 ) V 1 , and V 0 , V 1 , A 0 and A 1 are, respectively, the volumes of the sphere and a single bead, and the normalized scattering amplitudes of the sphere and a bead. The vector r m defines all relative distances between the internal beads. The total form factor [ P ( q ) = | A ( q )| 2 ] then reads as

where the first term is the form factor of the sphere the second term is the total scattering intensity of the N internal beads and the third describes the cross-term between the sphere and the beads. An analogous approach was reported for modeling the internal structure of a `biphasic' copolymer system (Keerl et al. , 2009 ). Here, by assuming R1 << R0 and N → ∞, one can approximately describe the interior of this sphere as a macroscopic canonical system. This enables the application of the canonical ensemble average to the summations of equation (11) (Klein & D'Aguanno, 1996 ), leading to

where S ss ( q ) corresponds to the structure factor of interactions among the beads. The summation within the integral defines the microscopic density of N beads. Its ensemble average corresponds to the single-particle density, which, because of the translational invariance of a homogeneous system, is equal to the average bead density N / V 0 (Klein & D'Aguanno, 1996 ). Hence, the whole integral is simply equal to NA 0 ( q ), yielding the final form of the spherical bead system form factor:

The cross-term is thus modulated by the normalized scattering amplitude of the sphere, A 0 ( q ), which is equivalent to a convolution of a homogeneous distribution of beads within the volume of a sphere of radius R 0 . Importantly, this final form of the cross-term does not depend on the original volume of the sphere hosting the small spheres. That is, even if the beads are constrained within a specific region of the sphere – such as in the case of ribosomes, which mainly partition into the non-nucleoid region of the cytoplasm – the scaling of this cross-term does not change.

In the next step, we translate the spherical bead system to the case of a heterogeneously structured bacterial cytosol. This requires a few approximations. First of all, encouraged by the fact that the scattering intensity scales proportionally to the square of the particle volume, we focus on contributions originating from ribosomes. More specifically, ribosomes with a volume V rb ≃ 2800 nm 3 , a total number N rb ≃ (1 − 6) ×󈇾 4 and R g = 8.77 nm (Zimmerman & Trach, 1991 Lebedev et al. , 2015 ) should be the dominating cytosolic scatterers in the case of X-rays or neutrons in the absence of heavy water (Fig. 2 and SI, for cytoplasmic composition and volume fractions). Secondly, we approximate the scattering of ribosomes at very low q , i.e. in the Guinier regime up to the first minimum of the scattering form factor, by the scattering of an `effective' sphere of V rb = 2800  nm 3 with R rb = 11 nm. Thirdly, we assume that mixed interactions with macromolecules of different size, shape and net charge (cytosolic proteins) lead to an overall effective S ss ( q ) ≃ 1. Furthermore, we simplify the cross-term modulation by a compact ellipsoid, neglecting that ribosomes sequester to the non-nucleotide region, and do not include the grafted OS cores in the cross-term.

Then the final form of the scattered intensity, considering contributions from ribosomes, is given by

where β rb = V rb ( ρ rb − ρ cyto ), A rb is the normalized scattering amplitude of ribosomes approximated by an equivalent sphere and A cyto is the normalized scattering amplitude of a prolate describing the cytoplasmic space ( i.e. only the central part of the core–shell system defining ).

5. Parameterization and optimization strategy

All parameters needed to describe elastic SAS from E. coli are summarized in Table 1 . In general we differentiate between parameters that either do or do not depend on the individual experiment ( e.g. sample concentration, scattering contrast). Experiment-specific parameters are termed `local', while others are designated as `global' parameters.

Table 1
Overview of parameters of the revised multi-scale model for live E. coli scattering ( cf . equations 9 and 14 )

Parameters describing structural details of the cytoplasmic membrane (CM) and outer membrane (OM) were fixed according to values reported from experiments and simulations on membrane mimic systems of E. coli cell membranes (De Siervo, 1969 Oursel et al. , 2007 Lohner et al. , 2008 Pandit & Klauda, 2012 Kučerka et al. , 2012 , 2015 Leber et al. , 2018 ), as detailed in Table 2 (see also Fig. 1 and the SI). This decision can be rationalized by the lack of distinct scattering features for q > 0.27 nm 𕒵 , corresponding to distances of 󕾄 nm. Consequently, structural features 󖼤 nm are, although contributing to the overall scattering, difficult to resolve with appropriate accuracy. Note that membrane proteins are treated similarly to proteins in other compartments as bodies adding individually to the scattered intensity and thus do not contribute to the average SLDs of the inner and outer membranes. Compared with the overall scattering arising from the cell body, their overall contribution can be shown to be negligible. Similarly, the width of the peptidoglycan layer (PG), W PG , and the radius of gyration of the OS core, R g,OS , were fixed to the values reported in Table 2 after analyzing their contributions. W PG values of 𕙞 nm have been reported (Matias et al. , 2003 ), and simulations in the range 2.5𔃅.5 nm (Labischinski et al. , 1991 ) led to insignificant variations of ρ PG . Furthermore, our estimates show that R g,OS < 1 nm (see SI). However, variations of its value within this constraint do not lead to significant changes within our scattering model, because R outerR g,OS .

Table 2
List of fixed parameter values

Finally, the ratio between major and minor ellipsoidal radii, ɛ , was fixed for the ATCC and K12 strains. This choice was driven by the lowest available q range, which does not reach the Guinier plateau of the bacterial scattering and thus does not allow for an accurate determination of the cell length. We therefore used dynamic light scattering to first estimate the average length, L c , from , using R g  ≃  R H and typical values reported for R outer in the literature. This led to  nm and  nm. The resulting ɛ  = L c /(2 R outer ) values were subsequently refined in some test runs of the optimization procedure for USAXS/SAXS data with R outer as adjustable parameter and then fixed for the detailed USAXS/SAXS and VSANS/SANS analysis, yielding the values reported in Table 2 .

Owing to the complexity of the system and the high number of parameters, optimization of the adjustable parameters was performed with a Monte Carlo genetic selection algorithm (Banzhaf et al. , 1998 ). In brief, the algorithm is schematized in a series of steps that are repeated at every cycle ( generation ). As step zero, nine low-discrepancy sequences (quasi-random numbers) were created for each parameter ( gene ) within specific boundaries, based on compositional estimates (see the SI). Nine sets of parameters ( individuals ) were then used as input for testing an equal number of possible scattering intensity curves (step 1, evaluation ). This involved the calculation of nine standard weighted chi-squared values as

where N free is the number of free parameters and σ i is the error associated with the measured I data, i at a given q i . Only the four individuals with the lowest χ 2 values were then selected (step 2, selection ) to generate the first offspring , consisting of eight new individuals that were created by randomly shuffling the genes of the four selected parents (step 3, recombination ). The new ninth individual was a copy of the parent with the lowest χ 2 value (rule 1: inheritance of the best ). After the recombination , each gene had a finite probability of being altered (step 4, mutation ). In addition, there was a finite probability of replacing an entire individual with a brand new set of randomly created genes (rule 2: the stranger ). This construction of the new offspring ended the first cycle, and each individual was again evaluated on the basis of the χ 2 values (jumping back to step 1). The process was repeated until the changes of the lowest were less than 0.5% for 25 consecutive generations .

The mutation step and rule 2 allow the algorithm to skip possible local minima in the χ 2 landscape, whereas rule 1 enables a fast convergence of the fitting. Note that, except for the creation of the initial set of individuals , pseudo-random numbers were used in the whole algorithm for the decision-making processes of the recombination and mutation steps, and for rule 2. Note also that a larger initial population (>9 individuals ) did not result in a gain of computational time. Each new pseudo-random gene was always constrained within the initial boundaries, in order to ensure the preservation of a physical meaning of the results. In total, each scattering curve was fitted 500 times, and only converging fittings (convergence criterion ) were used to retrieve average values and standard deviations for each parameter.

In the case of SANS, the q -dependent instrumental smearing was additionally taken into account. This was accomplished with a standard convolution

where G (q) is a normalized Gaussian profile of width Δ q ( q ). The Δ q ( q ) values as a function of q were fixed parameters during the fitting and were provided by the D11 primary data treatment. In contrast, the effect of the instrumental smearing was negligible for SAXS data.

6. Results and discussion

6.1. SAXS/SANS global analysis

The revised multi-scale model was tested against USAXS/SAXS and VSANS/SANS data on E. coli strain ATCC 25922. Ten SANS data sets with different contrast conditions were collected, varying D 2 O from 0 to 90 wt% (increments of 10 wt%). Results of the combined SAXS/SANS data analysis are reported in Fig. 3 and Tables 3 and S1. Fig. 3 ( a ) highlights the different contributions from the multi-core–shell model, the OS cores and the sum of the two cross-terms [see equation (5) ] in the USAXS and SAXS regimes. The scattering contribution from the core–shell function, i.e. cell body plus cell wall, dominates over the scattering intensity originating from the OS cores, owing to the huge difference in mass. However, the cross-term, being a function of the whole cell surface, is mainly responsible for modulating the scattered intensities between q ≃ 0.1 nm 𕒵 and q ≃ 0.3 nm 𕒵 . This leads to an average slope between q 𕒵.5 and q 𕒶 in this regime, which is a typical signature of grafted systems, also called `blob scattering' (Pedersen, 2000 ). Previously, this regime was taken to be dominated by flagella, described by a self-avoiding-walk polymer term (Semeraro et al. , 2017 ). This, however, does not describe the apparent change of slope at q ≃ 0.04 nm 𕒵 and the scattering feature at q ≃ 0.1 nm 𕒵 , which appears to be specific to the ATCC strain (see Fig. S3) but not K12 (see below). Hence, the OS-core cross-term enables a full description of the q range between 0.03 and 0.2 nm 𕒵 .

Table 3
Fit results for the global parameters describing USAXS/SAXS and VSANS/SANS of E. coli ATCC 25922 strain

Errors were calculated from standard deviations of the ensemble of converged fittings. See Table S1 for results on local parameters.

Figure 3
( a ) USAXS/SAXS data analysis of E. coli ATCC 25922 using equation (9) , highlighting contributions from different terms (negative values of the cross-term are not shown). ( b ) Alternative analysis of the same data using equation (14) , showing contributions from ribosomes. Comparison with a fit using equation (9) (black dashed line) shows negligible differences. ( c ) VSANS/SANS data of the same strain at selected D 2 O contrasts (see Fig. S4 for additional neutron data). Scattering curves were scaled for better visibility.

Attempts to fit the same data with the more complex model accounting for ribosomes [equation (14 )] demonstrated insignificant scattering contributions from the macromolecules [Fig. 3 ( b )]. In particular, the goodness of fit and results for the common adjustable were identical within the error of the analysis. Thus, the ribosome term, along with its related cross-term, is negligible compared with the cell-wall contribution. Note that only SAXS or SANS data at the lowest wt% of D 2 O are sensitive to test for this contribution. SANS data are dominated by the acyl-chain contribution at higher heavy-water content (Fig. 2 ). Furthermore, ribosomes are made up of amino acids and RNA, which will be differently matched, thus challenging the analysis. Note that N rb was the only adjustable parameter for this analysis. V rb and R rb values were fixed as detailed in Section 4 . Interestingly, the outcome of this test resulted in a much smaller number of ribosomes ( N rb ≃ 500) than our estimate of N rb ≃ 10 4 following Zimmerman & Trach (1991 ) (see the SI). This is possibly related to the low contrast of these molecules in the local cytoplasmic environment. As the scaling is proportional to N rb ( ρ rb − ρ CP ) 2 , small differences in the effective contrast easily skew the determination of the number of macromolecules.

Importantly, this analysis demonstrates not only that the effective scattering signal from ribosomes is negligible but also that similar considerations can be applied to the other cytoplamic components. However, because of their smaller size (proteins) or smaller volume fraction (DNA and RNA), they will contribute even less to the overall scattered intensity. Hence, elastic scattering techniques are not suitable for discriminating differently structured compartments within the cytosol in live bacterial cells. The same applies to membrane proteins (see above) or proteins present in the periplasmic space and peptidoglycan layer.

Analysis of selected VSANS/SANS data at selected contrasts is presented in Fig. 3 ( c ) (the entire set of neutron scattering data and fits is presented in Fig. S4). Clearly, fits using equation (9) neatly capture all changes of scattered intensities upon varying D 2 O concentrations, lending strong support to our modeling approach. The resulting parameters forming the X-ray and neutron SLD profiles of the bacterial envelope are summarized in Fig. 4 , again at selected neutron contrasts (see Fig. S5 for all neutron SLD profiles). Small differences between distances in X-ray and neutron profiles, as well as global parameters (Table 3 ), are due to biological variability of the samples, but are, with the exception of N OS , still within the confidence range of the results. At 10 wt% D 2 O, the contrast differences between different slabs are comparable to those obtained from SAXS data. The scattering intensities in these cases were also comparable in terms of scattering features at q ≃ 0.1 and 0.3 nm 𕒵 (Fig. 3 ). In turn, at 40 D 2 O wt% (and similarly up to 90 wt% D 2 O), the contrast of highly hydrated bacterial subcompartments (PG layer etc .) is much lower than the major contrast of the hydrophobic regions of the two membranes [Fig. 4 ( b )]. This characteristic leads to the shift in the scattering feature from q ≃ 0.27 nm 𕒵 to q ≃ 0.2 nm 𕒵 [Fig. 3 ( c )], which is primarily related to the intermembrane distance. This is in good qualitative agreement with the invariant estimation (Fig. 2 ), which suggests that the scattering intensity is dominated by the contribution from the acyl-chain region for D 2 O ≥ 40 wt%.

Figure 4
( a ) X-ray SLD profile of the bacterial ultrastructure of ATCC 25922 strain, corresponding to the fit shown in Fig. 3 ( a ). The panel highlights the average positions of both cytoplasmic and outer membrane, and the peptidoglycan layer. The abscissa describes the distance from the cell center along the minor radius R . ( b ) Selected neutron SLD profiles of the same strain [ cf . Fig. 3 ( c )]. See also Table S1.

In order to test our modeling strategy, we report the variation of the various SLDs with D 2 O content. Since the solvent freely accesses the cytoplasmic and periplasmic spaces, such plots should display a linear dependency. Indeed, the trends followed the expected behavior, which also enabled us to calculate the match points for the individual compartments [Fig. 5 ( a )𔃃 ( c )]. Note that the SLD values of the hydrated phospholipid headgroups were fixed (Table 2 ). In the case of β OS , scattering contributions are superseded by the signal originating from the intermembrane distance for D 2 O < 60 wt%. The linear trend for β OS was therefore determined in the range 0󔼺 wt%, and then extrapolated to higher D 2 O concentrations by using a confidence boundary of . Results from this analysis were used to derive the measured effective invariant as a function of D 2 O wt% [Fig. 5 ( d )]. The comparison with the estimated Q shows a shift in the minimum from the estimated 40 wt% to the measured 50 wt% D 2 O, possibly due to a larger contribution from the components that dominate at D 2 O ≤ 30 wt% (Fig. 2 ). On the other hand, these components are the very macromolecules that were proven to have a negligible scattering contribution.

Figure 5
( a ), ( b ) Plots of the cytoplasm (red circles), periplasm (green squares) and peptidoglycan (orange triangles) SLDs, along with linear fittings and matching points. The SLDs of the phospholipid head-group layers were fixed parameters (blue triangles). ( c ) Plot of the β OS (purple circles) values, along with linear fits and matching points. ( d ) Comparison between estimated scattering invariant and extrapolated forward scattering.

The answer to this paradox can be found in the absence of a net size distinction between macromolecules that scatter individually and are included in SLD averages. Translating the molecular mass distribution from gel filtration of cytosolic proteins (Zimmerman & Trach, 1991 ) into a size probability distribution function yields a maximum value at the smallest protein size detected in our experiments (see SI). Hence, it is likely that a fraction of the smallest bacterial proteins need to be included in the parameter ρ CP . Our fitted values for ρ CP are in fact larger than the estimated SLDs of the metabolites in the case of both SAXS and SANS analysis (compare Fig. 1 and Table S1). In any case, the estimated invarant was used only as a valuable guide for our modeling. Obviously, equation (1) loses its validity in the case of dense and crowded suspensions, and a new formula accounting for the each volume fraction should be used for improved estimates (Porod, 1982 ).

6.2. Comparison between ATCC and K12-related strains

After successful verification of our revised multi-scale model for live E. coli ATCC, we tested whether the new model is also applicable to other E. coli strains. Fig. 6 shows the USAXS/SAXS data of the K12 5K, JW4283 and Nissle 1917 strains in comparison to ATCC. Strikingly, the scattering patterns of K12 5K, JW4283 (which is fimbria free) and Nissle 1917 are superimposable for q > 0.06 nm 𕒵 , suggesting that the main ultrastructural features are conserved in these strains and confirming that the presence of fimbriae does not contribute to SAXS. Note also that our previous model would perfectly fit all K12 strains. Different minimum positions of the scattered intensities at lower q values are due to the different sizes of the different strains, instead.

Figure 6
Multi-scale analysis of USAXS/SAXS data of the ATCC, K12, fimbria-free K12 JW4283 and Nissle 1917 strains. The inset shows the plots of the log-normal PDF of Δ OM values for ATCC (solid red) and K12 (dashed green) strains. The PDFs of JW4283 and Nissle 1917 are comparable to K12 and are thus not shown.

Importantly, our new model is capable of fitting all strains, as demonstrated by the overall excellent agreement with experimental data (Fig. 6 ). Structural parameters resulting from this analysis are reported in Tables 4 and S2. Most of these parameters are of comparable magnitude. Significant differences concern the cell size ( R , ɛ ) – as observed in the different positions of the scattering minima (Fig. 6 ), the number of OS cores ( N OS ) and the intermembrane distance ( Δ OM , σ OM ). The last is related to the actual periplasmic space thickness via Δ OM = (2 W ME + D CM + D OM )/2 (see Fig. S6 for the X-ray SLD profiles). Both periplasmic thickness and its fluctuation are smaller for K12-related strains than for ATCC. Note that Δ OM for K12 5K is consistent with our previously reported value for a similar strain (Semeraro et al. , 2017 ). Despite the different Δ OM and σ OM for ATCC and K12 strains, the magnitude of the relative fluctuations σ OM / Δ OM ≃ (0.16– 0.22) is roughly conserved.

Table 4
Fitting results for the set of local free parameters for USAXS/SAXS analysis of ATCC 25922, K12 5K, JW4283 and Nissle 1917 strains

Considering cell size differences we find, according to the cell surface, an order that follows K12 5K (2.8 ×󈇾 6  nm 2 ) < Nissle 1917 (3.3 ×󈇾 6  nm 2 ) ≃ ATCC 25922 (3.4 ×󈇾 6  nm 2 ) < JW4283 (4.5 ×󈇾 6  nm 2 ). Differences in cell size are expected to be coupled to the number of LPS molecules dominating the outer leaflet of the cellular envelope. Indeed, N OS follows roughly the order observed for the bacterial outer surface (Table 4 ). Normalizing N OS values by the bacterial outer surface leads to an LPS surface density of 1.3𔂿.5 nm 𕒶 . However, as the cross-sectional area per LPS is 𕙙.6 nm 2 (Clifton et al. , 2013 Micciulla et al. , 2019 Kim et al. , 2016 ), the expected surface density is 𕙘.6 nm 𕒶 . The discrepancy between the two estimates is most likely due to an underestimation of the bacterial surface by considering the prolate approximation, or uncertainties introduced by β OS , which, like N OS , scales scattering contributions from oligosaccharides and their cross-terms [equation (5) ]. An additional factor could be related to the roughness of the bacterial surface (Alves et al. , 2010 ), which results in a larger effective surface than considered here in our simple estimate.

Finally, the center-to-center distance between the PG layer and the OM Δ PG ≃ 17 nm, with an X-ray SLD ρ PG ≃ 10.2 ×󈇾 𕒸  nm 𕒵 for all presently studied E. coli strains. Previously, we reported Δ PG ≃ 11 nm (Semeraro et al. , 2017 ), which appears to be more consistent with the length of the lipoproteins cross-linking the peptidoglycan strands to the outer membrane. This deviation from the expected value might be due to the fluctuation modes of Δ PG that are not fully correlated to those of Δ OM , which here is modeled by a log-normal distribution function. Devising a separate/partially coupled distribution function for variations of Δ PG , is beyond the present experimental resolution, however. In contrast, our new value for (and also for ATCC) is now consistent with reported hydration values of the peptidoglycan layer, i.e. 80󈟆 vol% (Labischinski et al. , 1991 Pink et al. , 2000 ). The previously reported value, 󕽻.6 ×󈇾 𕒸  nm 𕒵 , included the presence of macromolecular species in the SLD average.

7. Conclusion

The similarity of SAXS data of native and flagellum/fimbria-free E. coli strains led us to revise our previously reported scattering-form-factor model (Semeraro et al. , 2017 ) of the Gram-negative bacterium E. coli . The flagellar contribution was replaced by considering the scattering from the oligosaccharide inner and outer cores of the lipopolysaccharides, in terms of a grafted-polymer model. The model presented here is based on detailed compositional and structural estimates of characteristic lengths, volumes and scattering length densities for each cellular component and thus unifies decades of research on E. coli ultrastructure and molecular composition into a single comprehensive scattering function. The applicability of the derived model to X-ray and neutron scattering experiments enables the use of the powerful technique of contrast variation in order to highlight or nullify contributions from specific bacterial compartments.

Interestingly, we found that combined (U)SAXS/(V)SANS experiments are not sensitive to the structural heterogeneity of the cytoplasm as the scattering signal of its constituent macromolecules is overwhelmed by the contribution from the cell envelope. Likewise, the combined analysis is not able to report differences in the sub-nanometre range, in particular for cytoplasmic or outer membranes, such as thickness or compositional asymmetry to name but a few. The underlying SLD variations for CM and OM, were therefore fixed at values detailed in Table 2 , along with the width of the peptidoglycan layer and the effective R g of each oligosaccharide core. In turn, our technique is highly sensitive to the overall cellular size, the average contrast of the cytoplasmic and periplasmic space, and the structure of the cellular envelope. The last includes the distance between cytoplasmic and outer membranes, as well as its average fluctuations, and the distance between the peptidoglycan layer and the outer membrane. A potential caveat of our model is that the parameters β OS , N OS and Δ PG can only be determined qualitatively. Specifically, the overall number of LPS molecules (oligosaccaride cores) is affected by approximating the bacterium's envelope by an ellipsoidal surface, whereas the distance between the peptidoglycan layer and the outer membrane seems to depend on the used intermembrane distance distribution function. Overall, the robustness of our model is demonstrated by an excellent agreement of the derived parameters with a large body of literature on E. coli ultrastructure.

In conclusion, elastic scattering experiments on live E. coli provide ensemble-averaged values of specific ultrastructural bacterial features without the need of invasive labeling, and are complementary to transmission electron microscopy or optical microscopy. Here we report differences between five E. coli strains, which were mainly due to overall size and intermembrane distances (Table 6 ). Future research may exploit this platform to detect effects of different sample growth conditions or the effects of bactericidal compounds such as antibiotics. In particular, the combination of our analysis with millisecond time-resolved (U)SAXS enables kinematographic detection of their activity. Our laboratory is currently exploring such an approach for antimicrobial peptides. We also note that devising analogous models for different strains (including Gram-positive bacteria, other simple organisms and cells) requires a similar quality of complementary information to set appropriate physical constraints for the adjustable parameters. Nevertheless, the here-presented model provides ways and guidelines as to how to approach such endeavors.

Supporting information


ESRF – The European Synchrotron and the Institut Laue–Langevin (ILL) are acknowledged for provision of, respectively, SAXS (proposal LS-2869) and SANS (exp. 8-03-910) beamtimes. The authors also thank the visitors' laboratory support at EMBL Grenoble for providing access to the laboratory equipment for bacterial sample preparation during SAXS and SANS experiments. The authors are grateful for the support of T. Narayanan for the USAXS/SAXS measurements, as well as for fruitful discussions and advice, and to N. Malanovic for sharing her expertise about bacterial cultures. Finally, the authors thank the staff of the Institute of Mol­ecular Biosciences, beamline ID02 and the D11 instrument for support and availability.

Funding information

This work was conducted in the framework of the Austrian Science Fund (FWF) project No. P30921 (awarded to KL).


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Protein phosphorylation plays an essential role in cell signaling events, and its dysregulation can have pathologic consequences. 1,2 Much effort has been focused on the global analysis of protein phosphorylation sites and on the enzymes that control phosphosite occupancy. 3 While the most well-studied phosphorylated amino acid residues are serine, threonine, and tyrosine, phosphorylation of other amino acids has been observed and in some cases has been known for many decades. 4𢄦 In particular, phosphohistidine (pHis) was first discovered over 50 years ago in bovine liver mitochondria, 7 and has since been detected in other eukaryotic and prokaryotic systems. In prokaryotes, pHis plays important roles in two-/multicomponent signaling systems and in facilitating sugar uptake through the phosphoenolpyruvate phosphotransferase system (PTS). 8,9 In eukaryotes, pHis has been observed as a dynamic regulatory modification or a direct enzymatic participant in the context of chromatin, central carbon metabolism, and ion channel activity. 4 Nonetheless, in contrast to phosphoserine (pSer), phosphothreonine (pThr), and phosphotyrosine (pTyr), still relatively little is known about the pHis modification. Progress in the study of pHis has been significantly hindered by the lack of available research tools 4 due in large part to the labile nature of the pHis phosphoramidate moiety. Hydrolysis of the phosphoramidate in pHis releases roughly twice the energy of the phosphoester of pSer, pThr, or pTyr (ΔG° of hydrolysis � to � kcal/mol vs 𢄦.5 to 𢄩.5 kcal/mol). Accordingly, under acidic conditions, pHis is rapidly hydrolyzed to phosphoric acid and histidine, with a half-life of 㰰 s in 1 M HCl. Thus, the facile dephosphorylation of pHis has made studies of pHis biology extremely difficult. 4

To overcome these limitations, our lab has recently developed antibodies that can recognize pHis. Initially, we developed a sequence-specific rabbit polyclonal antibody to phosphorylated histone H4, employing a synthetic histone H4 peptide with the stable analogue, phosphoryltriazolyl alanine (pTza). 10 More recently, we generated a sequence-independent (pan) pHis antibody using the small molecule, phosphoryltriazolyl ethylamine (pTze), as a hapten. 11 Using this pan-pHis antibody, together with mass spectrometric (MS) analyses, we characterized histidine phosphorylation of Escherichia coli PEP synthase (PpsA) and the changes in the levels of this modification as a function of cell state. 11 During the course of that study, we noticed that pHis peptide ions consistently displayed a set of distinct neutral losses upon fragmentation by collision-induced dissociation (CID). Specifically, we observed a prominent neutral loss of 98 Da by CID, consistent with previous observations by others, 12� with ancillary losses of 80 and 116 Da. Indeed these neutral losses often dominated the ion current in the MS/MS spectra, leading to reduced efficiency of peptide backbone fragmentation and downstream identification by database search engines.

The neutral loss of phosphoric acid (𹒘 Da) by CID has long been recognized as a hallmark of peptides bearing pSer and pThr. 16 Loss of phosphoric acid occurs at the pSer or pThr residue site by β-elimination, or a charge-directed mechanism, which has been exploited to determine the site of phosphorylation on the phosphopeptide. 17,18 For pHis peptides, the CID-induced neutral loss of 80 Da is not surprising, as this constitutes loss of HPO3 upon fragmentation of the labile P–N bond in the pHis residue. However, the observation of 𹒘 Da as the most prominent CID-induced neutral loss for pHis peptides is puzzling. It suggests that, in addition to losing HPO3 (𹒀 Da) by fragmentation at the labile P–N bond, an additional water moiety (𹐘 Da) is somehow concomitantly lost from the peptide at a location other than the histidine residue.

To resolve this enigma, here we explored the gas-phase reaction mechanism of the pHis peptide 𹒘 Da neutral loss, using isotopic labeling and peptide fragmentation under CID conditions. Furthermore, we have exploited the pattern of pHis peptide neutral losses that we observe by CID (dominant loss of 98 Da and accompanying losses of 80 and 116 Da) to enhance our ability to detect and characterize pHis peptides from proteomic samples. For this purpose, we developed a software tool, which we call TRIPLET, to identify MS/MS spectra that exhibit the characteristic CID neutral loss triplet pattern (𹒘, 𹒀 and � Da) of pHis peptides. We also designed an MS-based workflow that incorporates this software tool in two ways: (1) to aid in the database search assignment of CID MS/MS spectra to pHis peptides, and (2) to flag potential pHis peptides for subsequent reanalysis by LC–MS using alternative fragmentation techniques. Finally, we employed this workflow in conjunction with the first reported peptide-level enrichment of pHis by immunoprecipitation using a pan-pHis antibody to achieve a global MS-based pHis proteomic analysis of E. coli cells.


Model Construction.

Herein we describe the reconstruction of our computable multiscale description of ROS damage to metalloproteins. Here, multiscale means that we model reactions involved in the processes of protein expression (slow) and metabolism (fast), as described previously (9). These rates can span 15 orders of magnitude, so we use specialized (quad-precision) solvers to compute steady-state solutions (10). We begin with a published reconstruction of E. coli’s integrated metabolic and macromolecular expression (ME) networks (11). This ME model accounts for 1,678 genes, 7,031 metabolites, and 12,655 reactions. The model includes detailed pathway reconstruction for transcription, translation, complex formation, and prosthetic group engraftment (12 ⇓ –14). The model also maps protein complex–metal stoichiometries, including 43 complexes that incorporate mononuclear iron or iron–sulfur clusters. We reconstruct ROS-based damage and cellular repair processes for these metalloproteins, yielding the OxidizeME model (Fig. 1) as described below.

OxidizeME: a multiscale description of metabolism and macromolecular expression that accounts for damage by ROS to macromolecules. (A) Mononuclear Fe(II) proteins are demetallated by ROS and mismetallated with alternative divalent metal ions. (B) Iron–sulfur clusters are oxidized and repaired. (C) Unincorporated Fe(II) spontaneously reacts with H2O2 via Fenton chemistry, generating hydroxyl radicals that damage DNA, while the Dps protein stores unincorporated iron and protects DNA from damage. (D) Protein structural properties are computed to estimate the probability of metal cofactor damage by ROS (RSA: relative solvent accessibility). (E) Processes in A–D are integrated into a multiscale oxidative model, named OxidizeME. OxidizeME is used to compute the scope of macromolecular damage and the cellular response for varying intracellular concentrations of superoxide, hydrogen peroxide, and divalent metal ions (Fe(II), Mn(II), Co(II), Zn(II)) see SI Appendix for details.

First, we define mathematical expressions to quantitatively describe the damage of iron–sulfur (Fe–S) clusters by superoxide and H 2 O 2 . The net reactions for Fe–S cluster damage are (4) [ 4Fe - 4S ] 2 + + O 2 ⋅ − + 2 H + → [ 3Fe - 4S ] 1 + + Fe 2 + + H 2 O 2 , [ 4Fe - 4S ] 2 + + H 2 O 2 + 2 H + → [ 3Fe - 4S ] 1 + + Fe 3 + + 2 H 2 O . Assuming that the ROS concentration [ R O S ] ≪ K M , the rate of Fe–S cluster damage v dmg depends on [ R O S ] , the rate constant k cat / K M dmg , and the Fe–S protein concentration E, v dmg = k cat K M dmg [ R O S ] ⋅ E = k cat K M dmg [ R O S ] ⋅ v dil / μ , [1] where μ is the cell’s specific growth rate in h −1 and v dil is the dilution rate of the protein.

Second, we describe Fe–S cluster repair. We assume that yggX (15) or ytfE (16) repairs Fe–S clusters using NADH as the electron donor and define the net repair reaction: [ 3Fe - 4S ] 1 + + Fe 2 + + NADH → [ 4Fe - 4S ] 2 + + NAD + . The repair rate v repair is constrained by the concentrations of the set of repair proteins R = < YggX, YtfE >and their rate constants of Fe–S cluster repair k repair : v repair ≤ ∑ j ∈ R k repair , j ⋅ E j = ∑ j ∈ R k repair , j ⋅ v j dil / μ . [2] Third, we describe the demetallation and mismetallation of mononuclear iron metalloproteins. Assuming [ R O S ] ≪ K M , the demetallation rate of protein j by the ROS k ∈ O = < O 2 ⋅ − , H 2 O 2 >is defined as v j k demet = k j k demet [ R O S ] v j dil / μ , [3] where k j k demet is the demetallation rate constant. Next, to describe mismetallation by competing metals, we assume that metallation occurs rapidly and is close to equilibrium (17). We use the metal–protein stability constant of metal i ( β j i ) relative to β j Fe , along with relative metal concentrations ( [ Metal i ] / [ Fe ( II ) ] ). We then define the rate that protein j is metallated with metal i as v j metal , i = β j i [ Metal i ] β j Fe [ Fe ( II ) ] ∑ k ∈ O v j k demet + v j dil . [4] We consider the set of alternative metals M = < Mn ( II ) , Co ( II ) , Zn ( II ) >. We then scale the catalytic efficiency k eff of the alternatively metallated enzymes based on estimates from published data (18, 19).

Finally, we formulate an optimization problem to compute the metabolic and proteomic state of E. coli under ROS stress. In the original ME model, the flux state (v)—for metabolic and macromolecular expression reactions—that maximizes growth rate is computed by solving the problem (10, 11) max μ , v μ subject to S ( μ ) ⋅ v = 0 , l ≤ v ≤ u , [5] where S ( μ ) is a stoichiometric matrix that includes coefficients that depend on μ, and l, u are lower and upper flux bounds. In OxidizeME, the corresponding problem is the following: max μ , v μ subject to S ( μ ) ⋅ v = 0 , l ≤ v ≤ u , v j dmg − v j repair − v j dil = 0 , ∀ j ∈ D , v j dmg = k cat K M j dmg [ R O S ] v j dil / μ , ∀ j ∈ D , v j dil ≥ ∑ i ∈ R μ k repair i v i j repair , ∀ j ∈ D , ∑ j ∈ D v i j repair ≤ ∑ i ∈ R k repair , i ⋅ v i dil / μ , v j k demet = k j k demet [ R O S ] v j dil / μ , v j metal , i = β j i [ Metal i ] β j Fe [ Fe ( II ) ] ∑ k ∈ O v j k demet + v j dil , ∀ i ∈ M , ∀ j ∈ D . [6] Comparing simulations with measured proteomics (20), we find that OxidizeME computes up to 85% of the E. coli proteome by mass (Dataset S1). Code and documentation for OxidizeME are available at

Amino Acid Auxotrophy under Oxidative Stress.

A hallmark response to ROS damage for E. coli is the deactivation of branched-chain and aromatic amino acid biosynthesis pathways, which is alleviated by supplementing these amino acids (4). Compared with supplementing all 20 amino acids, OxidizeME correctly predicted that excluding Ile and Val had a greater impact on growth rate than did excluding Phe, Trp, and Tyr (Fig. 2A). The reason that E. coli cannot grow under ROS stress without supplementation of branched-chain amino acids is that the iron–sulfur clusters of dihydroxy-acid dehydratase and isopropylmalate isomerase are inactivated by ROS, thus debilitating the branched-chain amino acid biosynthetic pathway (21). The auxotrophy for aromatic amino acids was originally attributed to inactivation of the transketolase reaction (22), but was recently traced to the mismetallation of the mononuclear iron cofactor in 3-deoxy-D-arabinheptulosonate 7-phosphate (DAHP) synthase (19). OxidizeME correctly predicted these molecular mechanisms and their phenotypic consequences (Fig. 2).

Systemic consequences of ROS stress. (A) Predicted optimal growth rate under low and high superoxide concentrations with different supplementation of amino acids (AAs). “All AAs” refers to all 20 common amino acids, and “–Ile & Val” means all amino acids except Ile and Val were supplemented. (B) Predicted optimal growth rate vs. superoxide concentration in various sulfurous amino acid supplementation media. (C) Same as B but simulated without damage to CysI by ROS. (D) Simulated damage fluxes for growth on glycolate vs. D-galactose. AROL: shikimate kinase II. (E) Growth rate of MG1655 on glycolate minimal medium with 0 to 0.6 μM PQ, with and without 50 μM shikimate supplementation. (F) Same as E but for growth on D-galactose minimal medium. * denotes that the growth rate changes significantly between two PQ concentrations (2-tailed Welch’s t test, P < 0.01 ).

Meanwhile, the basis of sulfurous amino acid auxotrophy in E. coli remains inconclusive despite multiple investigations (23, 24). OxidizeME correctly predicted auxotrophy for sulfurous amino acids (cysteine and methionine) under ROS stress (Fig. 2A). We traced a plausible mechanism to damage of the iron–sulfur cluster in CysI, which catalyzes the sulfite reductase step of Cys biosynthesis. Sulfite reductase binds four cofactors: iron–sulfur, FAD, FMN, and siroheme. Consistent with prior studies (25), our structural model estimated the siroheme group of sulfite reductase to be difficult to reach by ROS, mainly due to the depth of the cofactor binding residue (Dataset S2). Previous studies showed that the iron–sulfur cluster is likely not autoxidized with molecular oxygen because it is not solvent exposed (25). However, our structural model predicted that the iron–sulfur cluster is reached by ROS when considering both solvent exposure and depth of the cluster-binding residue from the solvent-accessible surface (Dataset S2). Simulations confirmed that alleviating damage to sulfite reductase was sufficient to reverse the observed growth rate defect and enable growth at higher ROS concentrations in the absence of Cys and Met (Fig. 2 B and C). Our hypothesis that sulfite reductase is deactivated by ROS is consistent with studies in Salmonella enterica showing that the activity of this enzyme is indeed reduced by elevated superoxide (26). Furthermore, the deactivation of sulfite reductase is consistent with accumulation of its substrate, sulfite, and explains the previously observed accumulation of sulfite (24). We note that CysI inactivation does not exclude the possibility that superoxide additionally leads to cell envelope damage, facilitating leakage of small molecules (27). Thus, OxidizeME can be used to understand and predict the basis for amino acid auxotrophies as a systemic response to specific macromolecular vulnerabilities to ROS.

Computing and Explaining the Environment Dependency of ROS Tolerance.

To investigate how environmental context affects ROS tolerance, we simulated growth under superoxide stress in 180 carbon sources (SI Appendix, Fig. S2). We then compared pairs of carbon sources in terms of the complexes that are most damaged by ROS. In particular, from simulations we predicted that a key bottleneck to growth on D-galactose under ROS stress is inactivation of shikimate kinase II, AroL (Fig. 2D). In contrast, AroL was predicted to not be a direct bottleneck to growth on glycolate (Fig. 2D). To validate this prediction, we measured growth of E. coli MG1655 on these two carbon sources in 0 to 0.6 μM paraquat (PQ). PQ is a divalent cation that is taken up opportunistically, typically by polyamine transmembrane transporters, and then undergoes reduction and autoxidation cycles catalyzed by any of three E. coli PQ diaphorases to generate superoxide (28). To directly test whether AroL is a bottleneck, we also supplemented the cultures with 50 μM shikimate. As predicted, shikimate did not alleviate PQ-induced growth defects during growth on glycolate (Fig. 2E). Meanwhile, shikimate alleviated growth defects by PQ during growth on D-galactose (Fig. 2F). These results confirm that OxidizeME is able to accurately predict ROS-induced amino acid auxotrophies in different environmental contexts. This predictive capability is rooted in its ability to compute molecular and macromolecular mechanisms.

We then used OxidizeME to explain why growth on glycolate and galactose exhibited different ROS tolerances. First, ROS stress globally increases redox balancing and energy production requirements to counter the lowered metabolic and protein expression efficiencies resulting from metalloprotein damage. Thus, the difference in E. coli’s capacity to replenish these metabolic capacities under different carbon sources can explain differences in ROS sensitivity.

During growth on D-galactose, the primary source of NADPH was the oxidative pentose phosphate pathway (Gnd and Zwf), with and without ROS stress. Under ROS stress with D-galactose as the carbon source, simulations indicated increased methylenetetrahydrofolate dehydrogenase (FolD) activity to supplement NADPH production by the PPP (pentose phosphate pathway), although PPP was still the major source of NADPH. Meanwhile, NADH production relied greatly on the glycine cleavage system with ROS, whereas glyceraldehyde-3-phosphate dehydrogenase was the primary source of NADH without ROS. The increase in FolD and glycine cleavage system fluxes to replenish NADPH and NADH both increased the requirement for tetrahydrofolate and its derivatives, which created a new metabolic bottleneck under ROS stress. In contrast, with glycolate as the carbon source, optimal NADPH production was predicted to switch from the TCA cycle (no ROS) to malic enzyme (with ROS). Thus, the difference between ROS tolerance capacities for galactose and glycolate as carbon sources can be explained by flexible NADPH production during growth on glycolate vs. rigid NADPH production during growth on galactose.

OxidizeME Delineates Stress-Specific Differential Gene Expression from Global Expression Changes.

Next, we assessed the systemic response of E. coli to ROS stress. We measured the transcriptome of E. coli under superoxide stress using PQ treatment and identified 914 differentially expressed genes (DEGs), of which 501 were accounted for in OxidizeME (Fig. 3 and SI Appendix, Fig. S3). In particular, 87 genes were up-regulated. Using OxidizeME, we determined that these 87 genes were more likely activated due to damage that is specific to iron metalloproteins than to any other protein ( P < 0.001 ). Furthermore, of the DEGs that were correctly predicted, a large fraction (84%) of the repressed genes changed due to decreased growth rate from PQ treatment, while 95% of the activated genes were specific responses to stress (Fig. 3). Gene expression is expected to respond to ROS stress directly—e.g., by up-regulating ROS detoxification genes—and indirectly—in response to decreased metabolic rates caused by ROS damage. The responses we identified as being specific to ROS, not to growth rate, spanned eight cellular processes (Fig. 3): ROS detoxification, central metabolism, anaerobic respiration, amino acid biosynthesis, cofactor synthesis and repair, translation, iron homeostasis, and transcriptional regulation by the rpoS sigma factor.

Validation of the consequences and responses to ROS stress. (A and B) DEGs (|log2(fold change)|> 0.9, FDR [false discovery rate] < 0.01) that are activated (A) and repressed (B). Correctly predicted DEGs are distinguished from global growth-associated regulation using OxidizeME. (C) Cellular processes involved in a systemic response to iron metalloprotein damage by ROS.

ROS-evolved cells deregulate Fe–S cluster biosynthesis.

E. coli possesses two alternative systems to synthesize Fe–S clusters: ISC (iron–sulfur cluster) and SUF (sulfur assimilation). Each system can synthesize Fe–S clusters in the absence of the other (29). While ISC is predominant under normal growth conditions, SUF is activated and can become the primary system under oxidative or iron limitation stress (4, 30). One reason for this switch to SUF is that ROS lowers the efficiency of ISC-based Fe–S assembly by increasing mismetallation of labile iron–sulfur clusters on the scaffold proteins IscU and SufA (31). In principle, switching from ISC to SUF is not the only mechanism for sustaining Fe–S assembly under ROS stress. For example, Mycobacterium tuberculosis possesses only the ISC operon, yet this pathogen is able to grow under oxidative stress including inside macrophages, presumably by up-regulating its ISC operon (32). A possible explanation is that M. tuberculosis’s ISC scaffold proteins are less sensitive to ROS than those in E. coli or are repaired. However, E. coli also possesses several putative Fe–S cluster repair genes, including ygfZ (33), yggX (15), and ytfE (16). Overall, gaps exist in our understanding of the cost–benefit tradeoffs between ISC and SUF under ROS stress. Here, we investigate this problem using OxidizeME and experimental validation.

First, we detected DEGs in wild-type E. coli MG1655 in response to 0.25 mM PQ, using RNA-Seq in glucose minimal medium. We detected repression of iscRSUA (mean l o g 2 (fold change) = − 1.53 , FDR-adjusted P < 0.001 ). We then repeated this experiment with an E. coli strain (called BOP1000) that had been evolved to grow rapidly on glucose (34). As with MG1655, strain BOP1000 repressed iscRSUA (mean l o g 2 (fold change) = − 1.32 , FDR-adjusted P < 0.053 ) (Dataset S3).

Finally, we obtained a laboratory-evolved strain of E. coli (called PQ3), which was evolved to grow on 0.8 mM PQ (SI Appendix, SI Materials and Methods). The starting strain for PQ3 is the glucose-evolved BOP1000. We cultured PQ3 in 0.2 and 0.6 mM PQ and identified DEGs using RNA-Seq. Under 0.6 mM PQ, strain PQ3 down-regulated the sufABCDSE transcription unit (mean l o g 2 (fold change) = − 2.01 , FDR-adjusted P < 0.034 ) (Dataset S3). Furthermore, under 0.2 mM PQ, strain PQ3 maintained higher expression of ISC compared with the preevolved BOP1000 strain. Specifically, we observed higher expression of the transcription units iscRSUA (mean l o g 2 (fold change) = 3.47 , FDR-adjusted P < 0.001 ) and hscBA-fdx-iscX (mean l o g 2 (fold change) = 1.99 , FDR-adjusted P < 0.001 ) (Dataset S3).

The contrasting transcriptomic response of the PQ-evolved strain from those of the glucose-evolved and wild-type strains prompted us to investigate genetic and systems-level mechanisms for ROS adaptation.

Genetic and Systems-Level Mechanisms of Optimal Fe–S Cluster Biosynthesis.

The genetic basis for the PQ-evolved response of ISC and SUF was a mutation in iscR. IscR regulates the transcription of both ISC and SUF based on coordination of 2Fe–2S at its Cys92, Cys98, and Cys104 residues (29, 35). The evolved strain had mutation C104S in iscR. This mutation may hinder IscR’s ability to incorporate 2Fe–2S and to regulate expression of the ISC and SUF systems under ROS stress (35).

We then investigated why increasing ISC and repressing SUF improve fitness under sustained ROS stress. Clearly, we expect a tradeoff between the rate of Fe–S inactivation at IscU and the fitness advantage of using SUF. Indeed, simulations show that below a threshold rate of Fe–S inactivation at IscU ( ∼ 0.78 s −1 ), sulfur transfer during Fe–S assembly occurs almost exclusively by IscS rather than by SufSE (SI Appendix, Fig. S4). Interestingly, IscU expression is predicted to increase proportionally to Fe–S inactivation rate up until the threshold, indicating an initial compensatory response to lowered Fe–S assembly efficiency at IscU. However, above the threshold, expression of IscU and IscS drops sharply, while SufSE and SufBCD expression increases. One reason for the fitness advantage of ISC over SUF is the cost of protein expression for each system. Considering just the sulfur transfer and scaffold complexes, IscS and IscU require 118 kDa of protein translated, while SufSE and S u f B C 2 D require 227 kDa—93% more than ISC.

Thus, increased ISC expression suggests that strain PQ3 may experience lowered Fe–S inactivation at IscU. To investigate this possibility, recall that E. coli possesses several genes associated with repair or oxidation resistance of Fe–S clusters, including ygfZ (33), yggX (15), and ytfE (16). RNA-Seq (Dataset S3) showed that none of these genes were differentially expressed by strain PQ3 in response to PQ. There was also no difference in expression level compared with strain BOP1000 under PQ treatment. However, DNA-Seq revealed a mutation (T108P) in ygfZ, a gene thought to contribute to Fe–S cluster synthesis or repair (33). Alternatively, ygfZ may directly degrade PQ, since it was shown to degrade plumbagin, another redox cycling compound (36). Either adaptive function would be consistent with lessened damage to Fe–S clusters overall however, it is unclear whether protecting Fe–S clusters at IscU is sufficient to reproduce the observed increase of ISC expression (and repression of SUF).

We thus performed simulations where we set the damage rate to Fe–S clusters at IscU to zero and kept damage processes for all other iron and Fe–S cluster-containing complexes. We then simulated growth of E. coli under basal (0.2 nM) and high (2 nM) intracellular concentrations of superoxide and identified in silico DEGs. Simulated DEGs were consistent with RNA-Seq of PQ3: hscBA-fdx-iscX and iscRSUA operons were up-regulated, and sufABCDSE was repressed (Dataset S4). This result indicates that protecting Fe–S clusters at IscU is sufficient to make ISC more favorable than SUF under ROS stress. The PQ-evolved strain potentially achieves this protection through ygfZ and in turn switches to the more advantageous ISC by mutation of iscR.

Raman microspectroscopy, surface-enhanced Raman scattering microspectroscopy, and stable-isotope Raman microspectroscopy for biofilm characterization

Biofilms represent the predominant form of microbial life on our planet. These aggregates of microorganisms, which are embedded in a matrix formed by extracellular polymeric substances, may colonize nearly all interfaces. Detailed knowledge of microorganisms enclosed in biofilms as well as of the chemical composition, structure, and functions of the complex biofilm matrix and their changes at different stages of the biofilm formation and under various physical and chemical conditions is relevant in different fields. Important research topics include the development and improvement of antibiotics and medical devices and the optimization of biocides, antifouling strategies, and biological wastewater treatment. Raman microspectroscopy is a capable and nondestructive tool that can provide detailed two-dimensional and three-dimensional chemical information about biofilm constituents with the spatial resolution of an optical microscope and without interference from water. However, the sensitivity of Raman microspectroscopy is rather limited, which hampers the applicability of Raman microspectroscopy especially at low biomass concentrations. Fortunately, the resonance Raman effect as well as surface-enhanced Raman scattering can help to overcome this drawback. Furthermore, the combination of Raman microspectroscopy with other microscopic techniques, mass spectrometry techniques, or particularly with stable-isotope techniques can provide comprehensive information on monospecies and multispecies biofilms. Here, an overview of different Raman microspectroscopic techniques, including resonance Raman microspectroscopy and surface-enhanced Raman scattering microspectroscopy, for in situ detection, visualization, identification, and chemical characterization of biofilms is given, and the main feasibilities and limitations of these techniques in biofilm research are presented. Future possibilities of and challenges for Raman microspectroscopy alone and in combination with other analytical techniques for characterization of complex biofilm matrices are discussed in a critical review.

Applicability of Raman microspectroscopy for biofilm analysis

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About the Cover:

The cover depicts a new approach for the rapid assembly of chemical biosensors in yeast using G-protein coupled receptors (GPCRs). Pairing off-the-shelf actuators (transcription factors) and sensory units (GPCRs) allows the assembly of biosensors for a wide variety of chemicals. Represented is the sensor for decanoic acid, a biofuel precursor. Artwork by Jorge Luis Peralta-Yahya based on DOI:10.1021/sb500365m.

In this issue:
In This Issue
In This Issue
Introducing Our Authors
Introducing our Authors
GPCR-Based Chemical Biosensors for Medium-Chain Fatty Acids
  • Kuntal Mukherjee ,
  • Souryadeep Bhattacharyya , and
  • Pamela Peralta-Yahya*

A key limitation to engineering microbes for chemical production is a reliance on low-throughput chromatography-based screens for chemical detection. While colorimetric chemicals are amenable to high-throughput screens, many value-added chemicals are not colorimetric and require sensors for high-throughput screening. Here, we use G-protein coupled receptors (GPCRs) known to bind medium-chain fatty acids in mammalian cells to rapidly construct chemical sensors in yeast. Medium-chain fatty acids are immediate precursors to the advanced biofuel fatty acid methyl esters, which can serve as a “drop-in” replacement for D2 diesel. One of the sensors detects even-chain C8–C12 fatty acids with a 13- to 17-fold increase in signal after activation, with linear ranges up to 250 μM. Introduction of a synthetic response unit alters both dynamic and linear range, improving the sensor response to decanoic acid to a 30-fold increase in signal after activation, with a linear range up to 500 μM. To our knowledge, this is the first report of a whole-cell medium-chain fatty acid biosensor, which we envision could be applied to the evolutionary engineering of fatty acid-producing microbes. Given the affinity of GPCRs for a wide range of chemicals, it should be possible to rapidly assemble new biosensors by simply swapping the GPCR sensing unit. These sensors should be amenable to a variety of applications that require different dynamic and linear ranges, by introducing different response units.

Metabolic Engineering of Synechocystis sp. PCC 6803 for Production of the Plant Diterpenoid Manoyl Oxide
  • Elias Englund ,
  • Johan Andersen-Ranberg ,
  • Rui Miao ,
  • Björn Hamberger , and
  • Pia Lindberg*

Forskolin is a high value diterpenoid with a broad range of pharmaceutical applications, naturally found in root bark of the plant Coleus forskohlii. Because of its complex molecular structure, chemical synthesis of forskolin is not commercially attractive. Hence, the labor and resource intensive extraction and purification from C. forskohlii plants remains the current source of the compound. We have engineered the unicellular cyanobacterium Synechocystis sp. PCC 6803 to produce the forskolin precursor 13R-manoyl oxide (13R-MO), paving the way for light driven biotechnological production of this high value compound. In the course of this work, a new series of integrative vectors for use in Synechocystis was developed and used to create stable lines expressing chromosomally integrated CfTPS2 and CfTPS3, the enzymes responsible for the formation of 13R-MO in C. forskohlii. The engineered strains yielded production titers of up to 0.24 mg g–1 DCW 13R-MO. To increase the yield, 13R-MO producing strains were further engineered by introduction of selected enzymes from C. forskohlii, improving the titer to 0.45 mg g–1 DCW. This work forms a basis for further development of production of complex plant diterpenoids in cyanobacteria.

Synthetic Auxotrophs with Ligand-Dependent Essential Genes for a BL21(DE3) Biosafety Strain

Synthetic auxotrophs are organisms engineered to require the presence of a particular molecule for viability. They have potential applications in biocontainment and enzyme engineering. We show that these organisms can be generated by engineering ligand-dependence into essential genes. We demonstrate a method for generating a Synthetic auxotroph based on a Ligand-Dependent Essential gene (SLiDE) using 5 essential genes as test cases: pheS, dnaN, tyrS, metG, and adk. We show that a single SLiDE strain can have a 1 × 108-fold increase in viability when chemically complemented with the ligand benzothiazole. The optimized SLiDE strain engineering protocol required less than 1 week and $100 USD. We combined multiple SLiDE strain alleles into the industrial Escherichia coli strain BL21(DE3), yielding an organism that exceeds the biosafety criteria with an escape frequency below the limit of detection of 3 × 10–11.

Engineering Synthetic cis-Regulatory Elements for Simultaneous Recognition of Three Transcriptional Factors in Bacteria
  • Gerardo Ruiz Amores ,
  • María-Eugenia Guazzaroni , and
  • Rafael Silva-Rocha*

Recognition of cis-regulatory elements by transcription factors (TF) at target promoters is crucial to gene regulation in bacteria. In this process, binding of TFs to their cognate sequences depends on a set of physical interactions between these proteins and specific nucleotides in the operator region. Previously, we showed that in silico optimization algorithms are able to generate short sequences that are recognized by two different TFs of Escherichia coli, namely, CRP and IHF, thus generating an AND logic gate. Here, we expanded this approach in order to engineer DNA sequences that can be simultaneously recognized by three unrelated TFs (CRP, IHF, and Fis). Using in silico optimization and experimental validation strategies, we were able to obtain a candidate promoter (Plac-CFI1) regulated by only two TFs with an AND logic, thus demonstrating a limitation in the design. Subsequently, we modified the algorithm to allow the optimization of extended sequences, and were able to design two synthetic promoters (PCFI20-1 and PCFI22-5) that were functional in vivo. Expression assays in E. coli mutant strains for each TF revealed that while CRP positively regulates the promoter activities, IHF and Fis are strong repressors of both the promoter variants. Taken together, our results demonstrate the potential of in silico strategies in bacterial synthetic promoter engineering. Furthermore, the study also shows how small modifications in cis-regulatory elements can drastically affect the final logic of the resulting promoter.

Pterin-Dependent Mono-oxidation for the Microbial Synthesis of a Modified Monoterpene Indole Alkaloid

Monoterpene indole alkaloids (MIAs) have important therapeutic value, including as anticancer and antimalarial agents. Because of their chemical complexity, therapeutic MIAs, or advanced intermediates thereof, are often isolated from the native plants. The microbial synthesis of MIAs would allow for the rapid and scalable production of complex MIAs and MIA analogues for therapeutic use. Here, we produce the modified MIA hydroxystrictosidine from glucose and the monoterpene secologanin via a pterin-dependent mono-oxidation strategy. Specifically, we engineered the yeast Saccharomyces cerevisiae for the high-level synthesis of tetrahydrobiopterin to mono-oxidize tryptophan to 5-hydroxytryptophan, which, after decarboxylation to serotonin, is coupled to exogenously fed secologanin to produce 10-hydroxystrictosidine in an eight-enzyme pathway. We selected hydroxystrictosidine as our synthetic target because hydroxylation at the 10′ position of the alkaloid core strictosidine provides a chemical handle for the future chemical semisynthesis of therapeutics. We show the generality of the pterin-dependent mono-oxidation strategy for alkaloid synthesis by hydroxylating tyrosine to L-DOPA—a key intermediate in benzylisoquinoline alkaloid (BIA) biosynthesis—and, thereafter, further converting it to dopamine. Together, these results present the first microbial synthesis of a modified alkaloid, the first production of tetrahydrobiopterin in yeast, and the first use of a pterin-dependent mono-oxidation strategy for the synthesis of L-DOPA. This work opens the door to the scalable production of MIAs as well as the production of modified MIAs to serve as late intermediates in the semisynthesis of known and novel therapeutics. Further, the microbial strains in this work can be used as plant pathway discovery tools to elucidate known MIA biosynthetic pathways or to identify pathways leading to novel MIAs.

Development of a Synthetic Malonyl-CoA Sensor in Saccharomyces cerevisiae for Intracellular Metabolite Monitoring and Genetic Screening

Genetic sensors capable of converting key metabolite levels to fluorescence signals enable the monitoring of intracellular compound concentrations in living cells, and emerge as an efficient tool in high-throughput genetic screening. However, the development of genetic sensors in yeasts lags far behind their development in bacteria. Here we report the design of a malonyl-CoA sensor in Saccharomyces cerevisiae using an adapted bacterial transcription factor FapR and its corresponding operator fapO to gauge intracellular malonyl-CoA levels. By combining this sensor with a genome-wide overexpression library, we identified two novel gene targets that improved intracellular malonyl-CoA concentration. We further utilized the resulting recombinant yeast strain to produce a valuable compound, 3-hydroxypropionic acid, from malonyl-CoA and enhanced its titer by 120%. Such a genetic sensor provides a powerful approach for genome-wide screening and could further improve the synthesis of a large range of chemicals derived from malonyl-CoA in yeast.

Polymerase Chain Reaction on a Viral Nanoparticle
  • James Carr-Smith ,
  • Raúl Pacheco-Gómez ,
  • Haydn A. Little ,
  • Matthew R. Hicks ,
  • Sandeep Sandhu ,
  • Nadja Steinke ,
  • David J. Smith ,
  • Alison Rodger ,
  • Sarah A. Goodchild ,
  • Roman A. Lukaszewski ,
  • James. H. R. Tucker* , and
  • Timothy R. Dafforn*

The field of synthetic biology includes studies that aim to develop new materials and devices from biomolecules. In recent years, much work has been carried out using a range of biomolecular chassis including α-helical coiled coils, β-sheet amyloids and even viral particles. In this work, we show how hybrid bionanoparticles can be produced from a viral M13 bacteriophage scaffold through conjugation with DNA primers that can template a polymerase chain reaction (PCR). This unprecedented example of a PCR on a virus particle has been studied by flow aligned linear dichroism spectroscopy, which gives information on the structure of the product as well as a new protototype methodology for DNA detection. We propose that this demonstration of PCR on the surface of a bionanoparticle is a useful addition to ways in which hybrid assemblies may be constructed using synthetic biology.

Tunable Riboregulator Switches for Post-transcriptional Control of Gene Expression
  • Malathy Krishnamurthy ,
  • Scott P. Hennelly ,
  • Taraka Dale ,
  • Shawn R. Starkenburg ,
  • Ricardo Martí-Arbona ,
  • David T. Fox ,
  • Scott N. Twary ,
  • Karissa Y. Sanbonmatsu* , and
  • Clifford J. Unkefer*

Until recently, engineering strategies for altering gene expression have focused on transcription control using strong inducible promoters or one of several methods to knock down wasteful genes. Recently, synthetic riboregulators have been developed for translational regulation of gene expression. Here, we report a new modular synthetic riboregulator class that has the potential to finely tune protein expression and independently control the concentration of each enzyme in an engineered metabolic pathway. This development is important because the most straightforward approach to altering the flux through a particular metabolic step is to increase or decrease the concentration of the enzyme. Our design includes a cis-repressor at the 5′ end of the mRNA that forms a stem-loop helix, occluding the ribosomal binding sequence and blocking translation. A trans-expressed activating-RNA frees the ribosomal-binding sequence, which turns on translation. The overall architecture of the riboregulators is designed using Watson–Crick base-pairing stability. We describe here a cis-repressor that can completely shut off translation of antibiotic-resistance reporters and a trans-activator that restores translation. We have established that it is possible to use these riboregulators to achieve translational control of gene expression over a wide dynamic range. We have also found that a targeting sequence can be modified to develop riboregulators that can, in principle, independently regulate translation of many genes. In a selection experiment, we demonstrated that by subtly altering the sequence of the trans-activator it is possible to alter the ratio of the repressed and activated states and to achieve intermediate translational control.

Engineering a Lysine-ON Riboswitch for Metabolic Control of Lysine Production in Corynebacterium glutamicum

Riboswitches are natural RNA elements that regulate gene expression by binding a ligand. Here, we demonstrate the possibility of altering a natural lysine-OFF riboswitch from Eschericia coli (ECRS) to a synthetic lysine-ON riboswitch and using it for metabolic control. To this end, a lysine-ON riboswitch library was constructed using tetA-based dual genetic selection. After screening the library, the functionality of the selected lysine-ON riboswitches was examined using a report gene, lacZ. Selected lysine-ON riboswitches were introduced into the lysE gene (encoding a lysine transport protein) of Corynebacterium glutamicum and used to achieve dynamic control of lysine transport in a recombinant lysine-producing strain, C. glutamicum LPECRS, which bears a deregulated aspartokinase and a lysine-OFF riboswitch for dynamic control of the enzyme citrate synthase. Batch fermentation results of the strains showed that the C. glutamicum LPECRS strain with an additional lysine-ON riboswitch for the control of lysE achieved a 21% increase in the yield of lysine compared to that of the C. glutamicum LPECRS strain and even a 89% increase in yield compared to that of the strain with deregulated aspartokinase. This work provides a useful approach to generate lysine-ON riboswitches for C. glutamicum metabolic engineering and demonstrates for the first time a synergetic effect of lysine-ON and -OFF riboswitches for improving lysine production in this industrially important microorganism. The approach can be used to dynamically control other genes and can be applied to other microorganisms.

Tn7-Based Device for Calibrated Heterologous Gene Expression in Pseudomonas putida
  • Sebastian Zobel ,
  • Ilaria Benedetti ,
  • Lara Eisenbach ,
  • Victor de Lorenzo* ,
  • Nick Wierckx , and
  • Lars M. Blank

The soil bacterium Pseudomonas putida is increasingly attracting considerable interest as a platform for advanced metabolic engineering through synthetic biology approaches. However, genomic context, gene copy number, and transcription/translation interplay often introduce considerable uncertainty to the design of reliable genetic constructs. In this work, we have established a standardized heterologous expression device in which the promoter strength is the only variable the remaining parameters of the flow have stable default values. To this end, we tailored a mini-Tn7 delivery transposon vector that inserts the constructs in a single genomic locus of P. putida’s chromosome. This was then merged with a promoter insertion site, an unvarying translational coupler, and a downstream location for placing the gene(s) of interest under fixed assembly rules. This arrangement was exploited to benchmark a collection of synthetic promoters with low transcriptional noise in this bacterial host. Growth experiments and flow cytometry with single-copy promoter–GFP constructs revealed a robust, constitutive behavior of these promoters, whose strengths and properties could be faithfully compared. This standardized expression device significantly extends the repertoire of tools available for reliable metabolic engineering and other genetic enhancements of P. putida.

Evolutionary Design of Choline-Inducible and -Repressible T7-Based Induction Systems
  • Kohei Ike ,
  • Yusuke Arasawa ,
  • Satoshi Koizumi ,
  • Satoshi Mihashi ,
  • Shigeko Kawai-Noma ,
  • Kyoichi Saito , and
  • Daisuke Umeno*

By assembly and evolutionary engineering of T7-phage-based transcriptional switches made from endogenous components of the bet operon on the Escherichia coli chromosome, genetic switches inducible by choline, a safe and inexpensive compound, were constructed. The functional plasticity of the BetI repressor was revealed by rapid and high-frequency identification of functional variants with various properties, including those with high stringency, high maximum expression level, and reversed phenotypes, from a pool of BetI mutants. The plasmid expression of BetI mutants resulted in the choline-inducible (Bet-ON) or choline-repressible (Bet-OFF) switching of genes under the pT7/betO sequence at unprecedentedly high levels, while keeping the minimal leaky expression in uninduced conditions.

Memory and Combinatorial Logic Based on DNA Inversions: Dynamics and Evolutionary Stability
  • Jesus Fernandez-Rodriguez ,
  • Lei Yang ,
  • Thomas E. Gorochowski ,
  • D. Benjamin Gordon , and
  • Christopher A. Voigt*

Genetic memory can be implemented using enzymes that catalyze DNA inversions, where each orientation corresponds to a “bit”. Here, we use two DNA invertases (FimE and HbiF) that reorient DNA irreversibly between two states with opposite directionality. First, we construct memory that is set by FimE and reset by HbiF. Next, we build a NOT gate where the input promoter drives FimE and in the absence of signal the reverse state is maintained by the constitutive expression of HbiF. The gate requires ∼3 h to turn on and off. The evolutionary stabilities of these circuits are measured by passaging cells while cycling function. The memory switch is stable over 400 h (17 days, 14 state changes) however, the gate breaks after 54 h (>2 days) due to continuous invertase expression. Genome sequencing reveals that the circuit remains intact, but the host strain evolves to reduce invertase expression. This work highlights the need to evaluate the evolutionary robustness and failure modes of circuit designs, especially as more complex multigate circuits are implemented.

Figure 4

Figure 4. Total and selected SIMS images show the localization of TET signal to individual E. coli. The bacteria were cultured on Si and treated with 20 μg/mL TET. The total positive ion images in (a, b) show the outline of bacteria undergoing beam erosion from top surface to the depth of 800 nm. The distribution of Si (mapped by m/z 167.9) and TET (mapped by summing the molecular ion with fragments at m/z 410.1, 427.2, and 445.2) from top to the depth of 800 nm are in (c)–(f). The signal overlay images in (g) and (h) demonstrate colocalization of TET (yellow) to E. coli, represented by the black regions within the Si (blue) background.

Effect of TetA Efflux Pump on TET Accumulation

drug signal (m/z 410 + 427 + 445) counts/pixel ± weighted STDEV
dose of TET (μg/mL)depth (nm)TetAvector control
00–4008.8 ± 2.38.1 ± 6.0
400–8007.0 ± 2.57.9 ± 5.1
200–4009.1 ± 6.815.2 ± 6.3
400–8006.2 ± 6.67.0 ± 4.8
1800–40020.1 ± 7.626.4 ± 12.2
400–80014.8 ± 10.223.5 ± 9.8

Author information


Department of Chemistry and Biochemistry, Center for Biofilm Engineering, and Thermal Biology Institute, Montana State University, Bozeman, MT, USA

Roland Hatzenpichler, Viola Krukenberg, Rachel L. Spietz & Zackary J. Jay

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R.H. designed the concept for this Review. All authors wrote the manuscript.

Corresponding author

Sponsored Projects

Title: Studies on Mass Transport of Oxygen without Gas-Liquid Resistance as Applied to Biosystems.

Amount of grant: Rs. 3.79 lakhs

Period and status: 1995-1997, completed

Abstract:Mass transport of oxygen through gas-liquid mass transfer is a limiting factor for optimal operation of many industrially important systems, such as aerobic bioreactors. This project studied oxygen mass transport through a methodology which involves no gas-liquid resistance. The methodology involved liquid phase in situ breakdown of hydrogen peroxide to oxygen and water. This novel methodology for oxygen transport markedly improved specific productivity of bioprocesses.

Sponsoring Agency: Board of Research in Nuclear Sciences (BRNS)

Title: Treatment of Ammoniacal Wastewaters from Heavy Water Plants.

Amount of grant: Rs. 10.25 lakhs

Period and status: 1997-1999, completed

Co-investigators: S.R.Asolekar, K.V.Venkatesh (IIT) R.R.Sonde (BARC)

Abstract:Reactor grade heavy water production results in large amounts of liquidwastewater containing unacceptable levels of ammonia. The process employed in heavy water plants to reduce ammonia levels in wastewater was highly energy intensive and also did not address the problem completely. Therefore, an effluent treatment system which utilizes microbial conversion of ammonia, which overcomes the problems posed by the above method, was highly desirable. The project addressed a microbial method of ammonia conversion to harmless nitrogen and nitrate. This involved use of different species of bacteria of the Nitrosomonas and Nitrobacter variety. An additional advantage of this method is that the nitrate produced can be used as a `fertilizer’ for lawn and tree growth. The optimal level of nitrate obtainable from the effluent treatment facility, based on the actual `fertilizer’ need was also addressed. In addition, the various biological processes involved in this effluent treatment were optimized based on energy (metabolic) status of the culture.

Sponsoring Agency: All India Council for Technical Education (AICTE)

Title: Improvement of Bioreactor Yields Using Culture Status in Viscous Fermentations.

Amount of grant: Rs. 5 lakhs

Period and status: 1997-2000, completed

Co-investigators: A. K. Suresh

Abstract:The method mentioned in the DST project above, was optimized. To optimize the method, we needed information on state of cells in culture, the actual factories producing the product. Such information is available through culture status measurement. Culture status is measured through culture fluorescence which is a non-invasive measure of the NADH level in culture.

Sponsoring Agency: Department of Science and Technology (DST)

Title: Improvement in Enzyme Productivities from Shear Sensitive Aerobic Systems

Amount of grant: Rs. 10.75 lakhs

Period and status: 2000-2003, completed

Co-investigators: A. K. Suresh

Abstract:A novel couette flow bioreactor (CFB) in which the entire cultivation can beperformed under defined shear conditions was developed. Oxygen supply, the normal limiting factor for entire cultivations under defined shear conditions, was achieved by passing air through a teflon membrane fixed on the inner cylinder of the CFB. More importantly, analyses of the oxygen transfer capabilities as well as the shear rates show that in this CFB, the effects of defined shear can be studied without interference from the effects of oxygen supply. The specific intracellular reactive oxygen species (ROS) level was found to be the maximum at the onset of the stationary phase when grown in the CFB, and the mode of cell death is suspected to be apoptosis.

Sponsoring Agency: Ministry of Human Resource Development (MHRD)

Title: Improvement in the Performance of Bioreactors Employing Recombinant Organisms Using the Liquid Phase Oxygen Supply (LPOS) Strategy.

Amount of grant: Rs. 12 lakhs

Period and status: 2003-2006, completed

Co-investigators: S. B. Noronha, A. K. Suresh

Abstract: The liquid phase oxygen-supply strategy (LPOS), which is characterized by oxygen evolution in the liquid phase through catalasic decomposition of hydrogen peroxide (H2O2) pulses, was employed for the cultivation of a recombinant system – recombinant Escherichia coli that can be induced to produce streptokinase by a change in temperature. Toxicity studies showed that an initial H2O2concentration of 7.5mM was optimal. The specific yieldof streptokinase in the cultivation with LPOS was 2- fold of the value obtained with conventional cultivation based on oxygen supply through aeration. Interestingly, the plasmid loss was about 10-15% lower with LPOS compared to conventional cultivation. To obtain optimal feed profiles of H2O2, a process model was developed. The model was optimized using a robust stochastic optimization solver based on genetic algorithm to get optimal H2O2feed rates. A model-predicted approached was used, and also the unknown model parameters were predicted using the genetic algorithm. The optimal H2O2addition profiles, which were also experimentally verified,resulted in 4-fold higher specific yields of streptokinase compared to that obtained from cultivations with aeration, and 2-fold higher specific yields compared to the cultivations without optimization.

Sponsoring Agency: Department of Biotechnology (DBT)

Title: Biotechnological Approaches for Production and Cultivation of Patchouli.

Amount of grant: Total: Rs. 85.14 lakhs (multi-institutional project) IIT Bombay: Rs. 26.45 lakhs

Period and status: 2003-2006, completed

Co-investigators: Kelkar Education Trust’s Scientific Research Centre (Mumbai, Maharashtra), Keva Biotech. Limited (Mumbai, Maharashtra), Central Plantation Crops Research Institute (Kasargod, Kerala), National Research Centre for Medicinal and Aromatic Plants (Boriavi, Gujarat), University of Agricultural Sciences (Dharwad, Karnataka), Dr. Balasaheb Sawant Konkan Krishi Vidyapeet (Dapoli, Maharashtra)

Abstract: Clonal propagation of plants by tissue culture was then established as the best method of obtaining planting material for vegetatively propagated plants. A problem with the tissue culture system is the high cost of the plants due to the specialized inputs. Micropropagation of plants is largely a manual operation, and is restricted by the limitations of ease of handling by skilled operators and stringent sterility conditions.

Bioreactor systems are an alternative method to increase the scale of operations and rate of production of the plants. Considering the increased output efficiency at lower costs, if large-scale micropropagation of plants is changed to the bioreactor method, the horticultural industry will better realize the advantages of using tissue cultured plants for cultivation. Although bioreactors are very common, and are extensively used in the bio-industry, bioreactors for plant shoot culture are rare. A novel, reusable, shoot culture bioreactor was designed, built and operated as a part of this project.

The developed shoot culture bioreactor did not require plantlets to grow on the liquid surface. Thus, the main drawback of hyper-hydration or vitrification was completely avoided when the developed bioreactor is used. Further, the developed bioreactor was re-usable, and allowed easy, continuous monitoring of many important bioreactor variables such as dissolved oxygen (DO), pH, temperature, shoot mass, and others that are essential for optimization of the process. Also, in the developed bioreactor it was possible to manipulate the atmosphere toward desirable results such as high growth rates, desirable contents of plant parts such as the leaf, and others. In addition, there were several features in the currently developed bioreactor that were attractive and patentable.

Sponsoring Agency: Indo-US Forum

Title: Reactive Oxygen Species aspects in Drinking Water

Amount of grant: US $ 30,000

Period and status: 2008-2010, completed

Partner: Jeanne VanBriesen, Carnegie Mellon University

Abstract:Chlorination of drinking water is a primary mode of disinfection. However, many organisms of interest are resistant to chlorination (e.g. Mycobacterium). The mechanism for microbial inactivation by chlorine is not well understood and thus, understanding of chlorine resistance mechanisms is also lacking. We hypothesized that chlorine-based microbial inactivation is mediated by reactive oxygen species (ROS). We evaluated this hypothesis,and the work resulted in a better understanding of the effect of chlorine on Mycobacterium, as well as an improved understanding of the mechanism of microbial inactivation by chlorine.

Sponsoring Agency: Department of Biotechnology (DBT)

Title: Development of a Photobioreactor for Algal Biofuels

Amount of grant: Rs. 1.3 crores

Period and status: 2009-2011, completed

Co-investigator: Shrikumar Suryanarayan

Abstract: The scope of this project was to develop a design parameter for the effective scale-up of a photobioreactor to produce oil (lipids) from Chlorella vulgaris. Chlorella vulgaris was chosen as the model algal system because of the significant background information available on it, compared to any other algae that has the potential for cultivation under saline conditions. The saline conditions were of prime importance, since we were asked to concentrate on marine aspects by the DBT. A large lab-scale photobioreactor of 15 litre capacity was designed and fabricated for the study. The effects of parameters such as temperature, pH, salinity, and light on Chlorella vulgariswere studied. Also, some work on the effect of parameters on Dunaliella parva and Chaetoceros mullieri was also done. Then the photobioreactor scale-up aspects were addressed in detail.

Sponsoring Agency: Divashri

Title: Algal biofuels

Amount of grant: Rs. 25 lakhs+

Period and status: 2008-2013, completed

Abstract: This project initially addressed the development of alternate cultivation systems for microalgae, and then some studies on improvement of many cultivation aspects of microalgae such as electroflocculation.

Sponsoring Agency: Department of Science and Technology (DST)

Title:Reactive Species for Improved Bio-oil Yields from Microalgae

Amount of grant: Rs. 32.83 lakhs

Period and status:6 th February 2013 – 5 th February 2016, work completed

Abstract:A challenge in algae based bio-oil production is to simultaneously enhance specific growth rates and specific lipid content. We demonstrated simultaneous increases in both the above in Chlorella vulgaris through reactive species induced under UVA and UVB light treatments, and achieved an 8.8-fold increase in volumetric lipid productivity.

Further, we reported for the first time that the endogenous, pseudo-steady-state, specific intracellular levels of the hydroxyl radical oscillate in an ultradian fashion (model system: the microalga, Chlorella vulgaris), and also characterized the various rhythm parameters. We reset the endogenous rhythm through entrainment with UVA radiation, and generated two new ultradian rhythms. The reset increased the window of maximum lipid accumulation from 6 h to 12 h concomitant with the onset of the ultradian rhythms.

In addition, we reported a novel application: Photon up-conversion, a process of converting lower energy radiations to those at higher energy via the use of appropriate phosphor systems, was employed as anovel strategy for improving microalgal growth and lipid productivity.

Sponsoring Agency: Department of Science and Technology (DST)

Title: Entrainment of Rhythms for Improved Cancer Therapy

Amount of grant: Rs. 38.3 lakhs

Period and status:25 th January 2017 – January 2020, on-going


Protein precipitation in organic solvent is an effective strategy to deplete sodium dodecyl sulfate (SDS) ahead of MS analysis. Here we evaluate the recovery of membrane and water-soluble proteins through precipitation with chloroform/methanol/water or with acetone (80%). With each solvent system, membrane protein recovery was greater than 90%, which was generally higher than that of cytosolic proteins. With few exceptions, residual supernatant proteins detected by MS were also detected in the precipitation pellet, having higher MS signal intensity in the pellet fraction. Following precipitation, we present a novel strategy for the quantitative resolubilization of proteins in an MS-compatible solvent system. The pellet is incubated at −20 °C in 80% formic acid/water and then diluted 10-fold with water. Membrane protein recovery matches that of sonication of the pellet in 1% SDS. The resolubilized proteins are stable at room temperature, with no observed formylation as is typical of proteins suspended in formic acid at room temperature. The protocol is applied to the molecular weight determination of membrane proteins from a GELFrEE-fractionated sample of Escherichia coli proteins.