Moreover, the cell envelope of Gram-negative bacteria such as E. coli contains two membranes with differing compositions.

To this end, we report the first molecular dynamics simulation study of the interaction of the antimicrobial peptide, polymyxin B1 with complex models of both the inner and outer membranes of E. coli .

Our simulations add the missing biochemical details and in doing so, reveal that the mechanisms of interaction of polymyXin B1 With the inner and outer membranes of E. coli , are really rather different.

We thus investigate the molecular-level mechanisms of PMBl binding, insertion, and bilayer disruption for both IM and OM models of the envelope of the archetypal Gram-negative bacterial species, E. coli .

To study the initial stages of AMP interaction with the envelope of Gram-negative bacteria, we simulated PMBl in the presence of a realistic model of the asymmetric E. coli OM, composed of Re LPS in the outer leaflet and a mixture of phospholipids (including phosphatidylethanol-amine, phosphatidylglycerol, and cardiolipin) in the inner leaflet.

To determine the process of PMBl binding to the E. coli OM, we setup two independent simulations (Sim_OM6), each initially composed of siX PMBl lipopeptides randomly placed above the Re LPS leaflet.

A symmetric lipid A bilayer, containing 16 lipid A molecules in each leaflet of the bilayer, was taken from our previous published study of the E. coli OM [27].

The E. coli outer membrane model used in this work contained the minimal Re LPS (i.e.

While E. coli mutants containing this level of LPS are far more susceptible to, for example, hydrophobic compounds [40], this level of LPS was deliberately chosen so as to maximize the likelihood of observing membrane disruption during the outer membrane simulations with polymyxin B 1.

Here, to address these issues, we quantitatively characterized dynamics of survival and death of starving E. coli cells.

Also, the second finding has bearing on understanding why in the cells we studied ( E. coli ), extracellular signaling was not evolved (Fig.

For example, E. coli cells can grow as fast as ~ 20 min per doubling under ideal growth conditions.

If this rate continues, a single E. coli bacterium can generate the mass of the earth in a couple of days.

However, our molecular-level knowledge is still far from complete even for model systems such as E. coli .

Previously, it was known that the master regulator of the general stress response rpoS plays an important role for survival of E. coli cells under various environmental stresses [7—10].

Note that NCFU of starving wild-type E. coli cells reported previously in the literature can be well approximated by a single-phase exponential decay [17—19].

When we repeated this experiment using other carbon sources or using a different E. coli strain, we observed similar density-dependent biphasic kinetics of NCFU (S3 Fig and S4 Fig).

Temporal survival kinetics of starving E. coli cells.

To test this hypothesis, we measured the uptake dynamics of our engineered bacteria ( E. coli expressing arabinose-inducible invasin) in several mammalian cell lines, grown under the same condition.

Likewise, our modeling results demonstrate a positive correlation between invasin-mediated uptake of E. coli and the relative abundance of available Bl-integrins expressed by hosts.

Though simple, the power-law description of uptake is robust in describing invasin-mediated bacterial uptake by mammalian cells, and the extracted power-law parameters can be useful in distinguishing pairs of E. coli strains (expressing varying levels of invasion) and mammalian cell lines.

Host E. coli

Mammalian cells were co-cultured with the indicated strain of GFP-expressing E. coli at 1000 MOI as described in (A).

In addition, for each host cell line, [3 and K Defic were lower for E. coli harboring pBAC-AraIm/ relative to pSCT7Im/ plasmid, possibly resulting from the lower invasin expression from pBAC-AraIm/ under these conditions.

A single colony of E. coli ToplOF’ strain containing pBACr-AraINV and pTetGFP was cultured overnight at 37°C in LB media for 16 hours.

After co-culture of increasing amounts of GFP-expressing E. coli harboring a plasmid permitting arabinose-inducible control of invasin (pBACr-Aralnv) with HeLa cells, flow cytometry was conducted to obtain data shown in Fig 3A.

These behaviors were reproduced using the GFP-expressing E. coli strain with invasin expressed from a different promoter (pSCT7Inv; SSF—SSH Fig).

In particular, we engineered non-pathogenic E. coli to express invasin from Yersinia pseudotuberculosis [17] (Materials and Methods).

To track and quantify uptake in individual hosts, we engineered E. coli to constitutively express a green fluorescent protein (GFP).

UraA is an example of a symporter, and is responsible for the proton-driven uptake of uracil in bacteria like E. coli .

That these were not seen in the crystal structure is perhaps not surprising given that the purifica-tion/ crystallization procedures extracts the majority of any bound lipids and that CL is a relatively minor (5%) component of the E. coli inner membrane.

UraA is a member of the nucelobase/ascorbate transporter (NAT) family, of which there are 10 family members in E. coli .

How conserved are these residues in the NAT family in E. coli ?

We explored these questions using the E. coli UraA H+-uracil symporter, the crystal structure of which was determined with bound uracil in the detergent n-nonyl-B-D-glucopyrano-side (NG; Fig.

Including UraA, there are ten members of the NCS2 family of transporters in E. coli .

Mutation of the conserved residues of the motif in the Aspergillus nidulans uric acid/xan-thine permease, UapA and the E. coli xanthine permease XanQ (Yng) have shown that residues within the NAT motif contribute to the substrate specificity [19].

We analyzed the realistic genome-scale stoichiometric model iAF126O of Escherichia coli ( E. coli ) metabolism [34].

Interestingly, in aerobic growth conditions ubiquinone-8 is the major quinone in E. coli [35, 36].

For the E. coli iAF126O model with splitting, secondary optimization reduced the solution space to only one or a few vertices (Table 2).

To illustrate this, the 120 - 106 vertices enumerated for E. coli under aerobic growth conditions originate from eight subnetworks with respectively 6, 3, 5184, 3, 2, 54, 2, 2 vertices.

In this research, we further demonstrated the use of CoPE-FBA 2.0 for the E. coli iAF126O genome-scale model by determining PL and PC for each enumerated vertex for different growth conditions.

If the objective of E. coli would be to minimize PC (or P;), we would not expect E. coli to exploit the unique optimal solution since the difference between this optimal solution and many suboptimal solutions is almost negligible.

Examples of rays in the E. coli iAF1260 genome-scale metabolic model.

Another 3 genes are involved in the entero-bactin synthesis (entA, entE, fepA), a siderophore that has been very recently revealed to be related to the growth of E. coli in M9 [40].

WT samples were identified from experiments that didn’t undergo genetic and environmental perturbations from the three platforms (7 for Affymetrix E. coli An-tisense Genome Array, 6 for Affymetrix E. coli Genome 2.0 Array, and 6 for RNA-Seq).

Single-molecule real-time (SMRT) sequencing technology has been recently applied to reading of genome-scale methyla-tion states in a pathogenic E. coli [51] and the technology would provide higher-resolution of molecular information of bacteria, enabling fine-scale predictive characterization based on it.

Indeed, after aggregating all high-throughput transcriptional data that is currently available for E. coli , the most well-studied model microbe, we are still limited to a few thousands microarray or RNA-Seq experiments that cover more than 30 strains, a dozen different media and a multitude of other genetic (knockout, over-expressions, re-wirings), or environmental (carbon limitation, chemicals, abiotic factors) perturbations.

Affymetrix E. coli Genome 2.0, Affymetrix E. coli Antisense) and technologies (e.g.

To achieve this, we have extended, normalized and annotated a compendium that was compiled recently [29] to incorporate all published high-quality Affymetrix mi-croarray and RNA-Seq datasets in E. coli (2258 samples in total, Fig.

We downloaded 83 RNA-Seq E. coli transcriptional profiles from 17 different GEO entries [30] that correspond to 8 strains, LB and MOPS media in wild-type (WT), gene knockouts (KOs), double KOs and environmental perturbations.

We integrated the RNA-Seq dataset (64 samples) to the E. coli Microarray Compendium (EcoMAC) that consists of 2198 microarrays of 4189 genes for which raw files were downloaded and normalized by RMA (robust multichip average) method [29].

Global map of genetic interactions for E. coli is reconstructed from [72] with pathway modules that functionally cluster genes based on the Pathway Ontology and transporter complexes curated in EcoCyc [73].

By combining stabilizing mutations predicted by our method, we created a highly stable catalytically active E. coli DHFR mutant with measured denaturation temperature 72°C higher than WT.

D27 is known to be a key catalytic residue of E. coli DHFR [51].

Here, we use a Monte Carlo protein unfolding approach (MCPU) with an all-atom simulation method and knowledge-based potential developed earlier in our lab [16,30,31] to simulate unfolding and predict melting temperatures for all possible single point mutants of E. coli Dihydrofolate Reductase (DHFR).

WT DHFR and all mutants used in this study were cloned into a pET24 expression vector and overexpressed in the BL21(DE3) pLys E. coli strain.

A single colony of the transformed E. coli carrying the wild type or mutation dhfr was cultured in Luria-Bertani liquid medium containing 50 ug/mL kanamycin (LB-kana) at 30°C overnight, and then inoculated to fresh LB-kana (1:100 dilution) and incubated again at 30°C.

The verified plasmids were transformed into competent E. coli BL21(DE3) cells for expression.

To illustrate this, we sampled the kinetic behaviours of the mammalian glucokinase and the phosphoenolpyruvate carboxylase of E. coli .

The particular kinetic features of the PEPC have been shown lately to play a key role in the regulation of anapleurosis in E. coli .

[58] have demonstrated that E. coli is able to turn off PEP consumption quickly upon glucose removal thanks to the ultrasensitive response of the PEPC upon FBP depletion.

This behaviour has been previously attributed to play a key role in the rapid adaptation of E. coli from normal-growing culture conditions to carbon-starvation or acetate switch conditions [58].