Information

Is it possible to synthesize chiral version of an organism (incompatible with our pathogens)?

Is it possible to synthesize chiral version of an organism (incompatible with our pathogens)?



We are searching data for your request:

Forums and discussions:
Manuals and reference books:
Data from registers:
Wait the end of the search in all databases.
Upon completion, a link will appear to access the found materials.

In theory, it should be possible to synthesize chiral (mirror image) version of some organism: with all molecules replaced with their enantiomers, e.g. L-sugars in place of our D-sugars.

Direct application of such chiral life could be production of enantiomers of proteins in e.g. chiral E. Coli, what might be useful for example as new drugs.

However, the most interesting is that such chiral life would be incompatible with our pathogens, for example allowing to design really sterile ecosystems for harsh environments like Mars. Finally, such chiral human would be immune to most of our diseases.

Nice article: http://wikibin.org/articles/chiral-life-concept.html

The question is if it is technologically possible to synthesize e.g. a chiral version of E. Coli?

Update: Wikipedia https://en.wikipedia.org/wiki/Chiral_life_concept


First off, quick clarification: "chiral" simply means distinct from its mirror image. All current life is chiral, in that it is made up of molecules which have a "handedness". What you're asking about is life which is made up of molecules of the opposite handedness (which I'll term "mirror chiral").

Such a lifeform is theoretically possible - chemical and biological reactions and reaction rates are entirely the same between the two handednesses - presuming that all participants (substrates, products, catalysts, etc.) are flipped to the opposite chirality. A mirror chiral Jarek would look and function exactly like the normal chirality Jarek, although he would need to eat mirror chiral food instead of normal chirality food.

So what difficulties would you have to synthesize a mirror chiral organism? The biggest one is the bootstrapping issue. There's a large suite of molecules in the cell which are all needed in order to replicate the cell, or even just maintain it. All of these need to be present in order for the organism to work. The organism can make all these components from scratch, but to do so it already needs to have these components.

Synthetic organisms - that is, organisms with a genome assembled in a computer and synthesized abiologically - have been made. The trick, though, was that the synthetic DNA was then placed into already existing organisms to be replicated and to have its genes expressed. There's no limitation (besides perhaps cost) to synthesizing mirror-chirality DNA - the same machines which synthesize normal chirality DNA can do it if you swap out the reagent bottles. But what do you do with it once you have it? There's no existing mirror-chiral organisms you can place it in to replicate and express the genes.

If one was to make mirror-chiral organisms, you would need to synthesize all of the necessary cellular components in their mirror-image form. So you would need mirror RNA polymerase, mirror ribosomes, mirror t-RNA, mirror… If you do things right you can skip most of the cellular components, but you still need a large number of them.

This is not necessarily a pipe dream. Last I knew, the J. Craig Venter Institute, the people who made the first synthetic DNA organism, are actively looking into it. The trick is to trim things down to the bare minimal number of chiral components needed to bootstrap the system. That's (more-or-less) where their current efforts are focused, from what I understand: what's the minimal number of genes needed for a viable cell? Which of these are needed to "bootstrap" a cell from bare DNA?

Personally, I'm pretty confident that a mirror-chiral organism will be created one day. The time frame for this is a little unclear, but there's nothing theoretically limiting it. The biggest issue is our current technical limits in synthesizing sufficient quantities of (non-DNA) biomolecules in a chemical (abiological) fashion. But those are constantly improving, and with additional research into what's needed for bootstrapping a synthetic cell, we'll push down the requirements even further.

Of course, the first mirror-chiral organism will be pretty simple. It's going to be less complex than even E. coli. It will also need to be fed a diet of (expensive) mirror-chiral food (amino acids, sugars), so its use will be limited as well.


A majority opinion seems to have emerged in scholarly analysis of the assortment of technologies that have been given the label “synthetic biology.” According to this view, society should allow the technology to proceed and even provide it some financial support, while monitor­ing its progress and attempting to ensure that the development leads to good outcomes. The near-consensus is captured by the U.S. Presidential Commission for the Study of Bioethical Issues in its report New Directions: The Ethics of Synthetic Biology and Emerging Technologies, which arguably marked the end of a preliminary round of analysis about the ethical and policy questions raised by synthetic bi­ology. Like a number of other, earlier documents issued by various groups around the world, the re­port called attention to questions about how the technology will be used whether it might be mis­used what sorts of accidents might happen along the way the economic, environmental, and social impacts of the eventual applications whether the very idea of “synthetic biology” should be trou­bling and how the debate over all of these ques­tions will be conducted. Also like most similar documents, however, while it called for careful monitoring and oversight of technology, it did not recommend any significant new constraints on its development and use. The commission's stance was that it would be “imprudent either to declare a moratorium on synthetic biology until all risks can be determined and mitigated, or to simply ‘let science rip,’ regardless of the likely risks.”

In this report, we will take stock of the current consensus, comment on some of the major points of disagreement, and identify the next steps for the debate. In part I, we offer a brief overview of the research and applications commonly grouped together under the heading of synthetic biology, partly in order to set the stage for the rest of the discussion and partly because we want to highlight some conceptual problems that attend the very la­bel given this field. In parts II, III, and IV, we take up, respectively, three broad classes of concerns that arise in the context of synthetic biology: con­cerns about the intrinsic or inherent value of do­ing synthetic biology, concerns about the concrete harms and benefits of doing synthetic biology, and concerns about justice. Addressing these concerns requires a method for bringing the public's values to bear on policy-making concerning emerging biotechnologies in part V, we discuss the chal­lenges in developing such a method.


Soil, science and civilization

All sciences are influenced by their own history. The founders of ecology were either botanists such as Arthur Tansley, Frederick Clements and Henry Gleason, principally interested in community ecology and patterns of vegetation, or zoologists such as Charles Elton, whose focus was on the behaviour of animal populations. All of them worked on terrestrial or freshwater systems. Marine biology had already developed its own body of concept and practice by the time ecology became an identifiable science hence, marine ecology is typically the province of oceanographers rather than ecologists. A similar narrative applies to soil science. Vasiliy Dokuchaev in Russia and Hans Jenny in America, two of the founders of the discipline, would not have regarded themselves as ecologists, but as geomorphologists or agronomists. The subsequent development of soil science and ecology as separate disciplines has not been to either's advantage.

Virtually all terrestrial ecosystems are founded on soil. Plants rely on it for water and nutrients, as consequently does everything else in the ecosystem, including us. Yet our species’ blithe disregard for soil is evidence of our reluctance to understand its fundamental role in our welfare. Edward Hyams was one of the first to highlight this blind spot in his classic book Soil and Civilization ( Hyams 1952 ), a work that should be required reading for all ecologists. He charted the links between the longevity of civilizations and their good fortune in being founded either on soils that were annually renewed by winter flooding and silting (Nile, Ganges, Yellow River), or on soils that were young (because of recent glaciation) and in climatic zones that enabled them to generate new soil at a rate to match our destructive power (much of western Europe). These soils are resilient to damage. Others are much less so. Many of the great ecological disasters in history occurred when inappropriate farming techniques were applied to fragile soils, a well known example being the dust-bowl of the American mid-west that inspired John Steinbeck's classic novel The Grapes of Wrath ( Steinbeck 1939 ).


INTRODUCTION

Carbohydrate and sugar metabolism occur in nearly all living organisms. Many of these metabolic pathways and regulations have been elucidated. The physiological functions of these metabolic pathways are to provide the cells with energy, reducing power, and building blocks for the synthesis of other biomolecules. In today's postgenome era, however, there are still enigmas related to sugar metabolism disorders and diseases, which cannot be explained by our current knowledge of sugar metabolism. The existence of an alternative glycogen and starch catabolism pathway, which may be operative under special physiological and environmental conditions, was once an open question. Work on the hypothesis of such a pathway started with fungi and algae and has now led to the elucidation of an alternative glycogen degrading pathway, the Anhydrofructose (AF) pathway (Scheme 1) and its regulation mechanism ( 1-6 ). The AF pathway that we revealed and named became accepted by IUBMB in 2006 ( 7 ). The approaches used for its elucidation included enzyme discovery, metabolite identification, and advanced analysis ( 8-12 ).

The anhydrofructose pathway of glycogen catabolism.

It is now known that this pathway is operative under biotic and abiotic stress conditions in fungi and red algae, whereas in other microorganisms, this pathway maybe involved in starvation response and signal transduction ( 13 ). In mammals, including human beings, roles of the metabolites of this pathway (Scheme 1) remain to be solved. Reviews on the AF pathway with different focuses have appeared before ( 10, 11 ). Herein, we present an updated review on what has been achieved in the understanding of the AF pathway of glycogen catabolism with respect to its biocatalysts and metabolites in the last decade and critically analyze the published literature in this area. Action points for essential further work are also suggested and discussed. Furthermore, biochemical reactions and enzymes that metabolize AF are presented, which could be part of the AF pathway in the relevant organisms as a salvage of AF for energy metabolism.


Results

Expanded teleost-specific tlrs in Atlantic cod

Homology searches for tlr21, tlr22 and tlr23 paralogues in the cod genome assembly identified 15 open reading frames that encode proteins with homology to these teleost-specific tlrs. In silico gene prediction analysis confirmed the presence of one tlr21, 12 tlr22 paralogues and two tlr23 paralogues, all encoding a typical Tlr protein (Table  1 ). A partial tlr21 cDNA of 3047𠂛p was sequenced, including the 134𠂛p 5 ′ -UTR and the 2913𠂛p complete coding region corresponding to a 970 aa protein. Cod Tlr21 shares more than 50% identity with its orthologues in zebrafish, tiger pufferfish and medaka, as well as with Tlr21a and Tlr21b of orange-spotted grouper (Epinephelus coioides). Based on the genome assembly, the tlr21 partial sequence was found to be encoded by a single exon (Figure  1 ). Full length cDNA sequences along with the 5 ′ - and 3 ′ -UTR regions were obtained for four tlr22 paralogues. Tlr22b, tlr22d, tlr22g and tlr22i were 3406, 3252, 3082 and 3219𠂛p long and encoding 942, 959, 842 and 954 aa proteins, respectively. They were composed of five, three, three and three exons, respectively (Figure  1 ). At the protein level, they are 62 to 75% identical to each other and share up to 73% similarity with other teleost Tlr22 proteins. Partial coding sequences for seven of the tlr22 paralogues were obtained either with or without the UTR regions from a minimum length of 1612𠂛p up to 2847𠂛p, encoding partial proteins of 453 aa to 865 aa. In the case of tlr22k, it was only possible to obtain a short sequence of 384𠂛p, including the 3 ′ -UTR and coding for a 96 aa partial protein (Table  1 ). Complete cDNA sequences were determined for both tlr23 paralogues in cod. Tlr23a was 3427𠂛p while tlr23b was only 2165𠂛p. Tlr23a and tlr23b were encoded by 5 and 3 exons, respectively (Figure  1 ), corresponding to proteins of 949 and 578 aa, respectively. At the nucleotide level, tlr23a and tlr23b were 45% identical to each other and shared 47% identity at the protein level with tiger pufferfish and green-spotted pufferfish Tlr23.

Gene structure of teleost-specific tlrs in Atlantic cod. Graphical representation of Atlantic cod tlr21, tlr22 and tlr23 gene structures. Exons and UTRs are represented in light blue and red, respectively. Introns are indicated by continuous lines. PCR amplicons are highlighted in dark blue. Scale bar represents 500𠂛p.

In general, all tlrs analysed in this study had an N-terminal LRR domain, a transmembrane domain and a C-terminal TIR signalling domain (Figure  2 ). Leucine rich repeats (LRRs) were mapped manually and the LRR C-terminal (LRRCT) domain was also identified. Tlr21 contained 27 LRRs and a typical CxCx24Cx15C motif in its LRRCT domain. Full length cDNAs from tlr22b, tlr22d, tlr22g and tlr22i encoded for 27 LRRs and had a CxCx24Cx18C motif at its LRRCT domain. Tlr23a and Tlr23b had CxCx24Cx18C at their LRRCT domain with 27 and 14 LRRs, respectively.

Protein domain structure of teleost-specific Tlrs in Atlantic cod. Graphical representation of Atlantic cod Tlr21, Tlr22 and Tlr23 protein structure predicted by ScanProsite. LRR ectodomain, transmembrane domain and TIR domain are represented by blue, grey and green colored shapes, respectively. Scale bar indicates 100 aa.

Synteny and phylogenetic analysis of teleost-specific tlrs in cod

Most cod tlrs were mapped to single contigs (Table  1 ). Tlr22a, tlr22b and tlr22e were present in the same chromosomal region (GeneScaffold_1177), which was syntenic in stickleback, tiger pufferfish and green-spotted pufferfish tlr22 (Figure  3 ). Tlr22c and tlr22d were found in GeneScaffold_1176 and tlr22k and tlr22l were both in GeneScaffold_351 along with other genes, but there was no identifiable synteny in these regions across other teleost genomes (Figure  3 ).

Partial synteny map of the genomic region surrounding teleost-specific Atlantic cod tlrgenes. A. Partial map of the genomic regions surrounding the Atlantic cod tlr21, tlr22 and tlr23 paralogues. Their genomic location based on the current draft genomic sequence of Atlantic cod (gadMor1 v67.1) is also indicated. B. Partial synteny map between cod tlr22a, tlr22b and tlr22e and tlr22 of stickleback (G. aculeatus), green-spotted pufferfish (T. nigroviridis) and tiger pufferfish (T. rubripes). Tlr22 paralogues are connected by black lines while genes in their vicinity are connected by grey lines to show synteny amongst these four teleosts. Genes are not represented to scale.

Bayesian inference from 41 tlr21, tlr22 and tlr23 sequences from 15 teleost species generated a consensus phylogenetic tree that was identical to the maximum likelihood one (Figure  4 ). All tlr21 genes were grouped under a single clade, while tlr22 and tlr23 formed a separate cluster. Stickleback tlr21a clustered with other teleost tlr21 genes, while tlr21b seemed to have arisen from a recent duplication and was more closely related to teleost tlr22. It is noteworthy that all tlr22 from cod clustered under a single clade, while the two tlr23 paralogues clustered along with their homologues from Tetraodontidae. As expected, the tlr22 paralogues encoded by salmonids, such as Atlantic salmon and rainbow trout, were grouped together and corresponded to closely related paralogues, which have probably arisen from the salmonid tetraploidisation. Amongst the cod tlr22 paralogues that are adjacent in the genome (Figure  3 ), only tlr22k and tlr22l clustered together, whereas tlr22a, tlr22b and tlr22e or tlr22c and tlr22d did not. Tlr22 encoded by basal teleosts belonging to the Ostariophysi superorder clustered as a separate clade followed by Salmonidae and higher teleosts from the Acanthopterygii superorder. Unexpectedly, cod tlr22 paralogues were more distant from the ancestral tlr22 sequence than their Acanthopterygii orthologues.

Phylogeny of teleost-specific tlrs. Unrooted phylogenetic tree of teleost-specific tlrs – tlr21, tlr22 and tlr23. Numbers at the nodes indicate posterior probability values from Bayesian inference. Posterior probability values were calculated for each node by Bayesian analysis based on 250,000 generations. Samples were collected every 100 generation and a consensus tree was built after burning the initial 1,250 trees. Only probability values above 0.8 are indicated: 0.95 to 1 shaded in red, 0.9 to 0.94 in blue and 0.8 to 0.89 in green, respectively. Atlantic cod genes are highlighted within red boxes.

Expression profiles of teleost-specific tlrs in adult cod tissues and during early ontogeny

Tlr21, tlr22 and tlr23 paralogues were widely expressed across many tissues, including immune-related organs (head-kidney, kidney spleen and gills), liver and gonads (Figure  5A ). All tissues examined, except ovary, had detectable levels of tlr21 transcripts with high levels in kidney, liver, gills, testis and blood. A differential expression pattern across adult fish tissues was observed for tlr22 paralogues. Tlr22k transcripts were detected in all tested tissues. Tlr22e had the lowest expression in kidney, liver and gills, while it was not detected in other tissues. All tlr22 paralogues, except tlr22e, were detected in head-kidney, kidney, spleen, liver and gills at varied levels. Six out of 12 tlr22 paralogues, tlr22a, tlr22c, tlr22d, tlr22h, tlr22j and tlr22k, were found to be expressed in stomach, while muscle and skin expressed only tlr22k. Testis had transcripts of most tlr22 paralogues but tlr22a, tlr22h and tlr22k were the only genes to be detected in ovary. Within tlr23 paralogues, expression of tlr23b was lower than that of tlr23a but they were both expressed in head-kidney, kidney, spleen, gills, blood and testis. Tlr23a transcripts were also found in liver, heart and brain.

Expression profile of cod teleost-specific tlrs in adult tissues and during early development. A. Tissue specific expression of Atlantic cod tlr21, tlr22 and tlr23 genes. Tlrs are mainly expressed in immune-related tissues such as head-kidney, kidney, spleen, liver and gills. Transcripts of most paralogues were also found in high levels in blood and testis. Eef1a was used as an internal reference for RT-PCR. Minus reverse transcriptase (−RT) and no template (NTC) controls were included to ascertain the specificity of PCR primers. Amplicon sizes in bp are indicated on the right hand side of the figure. B. Expression analysis of tlrs during embryonic development. Low expression of tlr21 was detected at later stages from hatching until first feeding, while tlr23a and tlr23b were not detected at any of the examined developmental stages. Tlr22c, tlr22, tlr22j and tlr22k transcripts were found in unfertilised eggs (UFE), while tlr22k was expressed at most developmental stages examined. Luciferase was used as an external reference for RT-PCR.

Tlr22c, tlr22h, tlr22j and tlr22k transcripts were found in unfertilised eggs (Figure  5B ). Tlr22k was the only tlr22 paralogue to be expressed throughout early development and its transcripts were detected at epiboly, somite stage, golden eye, hatching, bladder and hindgut stages. Low expression of tlr21 and tlr22a was detected at later stages from hatching until first feeding, while tlr23a and tlr23b were not present in any of the developmental stages examined.

Differential expression following pathogen challenge

Teleost-specific tlrs in cod were differentially regulated following a bath challenge with V. anguillarum (Figure  6 ). A significant decrease of tlr21 expression was recorded after 48 h in gills (2.3-fold) and spleen (2.2-fold) compared to the initial control. In head-kidney, the highest change in expression was observed at 4 hpc in tlr22c (3.3-fold decrease) and tlr22l (4.2-fold increase), albeit not significant, while most of the other paralogues remained at basal levels. Following a 2-fold significant decrease in expression of tlr22a and tlr22b at 4 hpc in head-kidney, tlr22a transcripts reached a 2-fold higher expression at 48 hpc, which was also significant compared to the initial control levels. Several significant changes in expression of tlr22 paralogues were also observed in gills and spleen following the bath challenge. In gills, tlr22d transcript levels were significantly reduced by 3.5-fold and this level was maintained through to 48 hpc. In the same tissue, a decrease of up to 2-fold in tlr22k expression was observed at 4 and 48 hpc. A significant decrease in tlr22f and tlr22i transcript levels was also observed at 48 hpc in gills. In spleen, tlr22d (2.4-fold), tlr22h (2.4-fold) and tlr22k (1.2-fold) were down-regulated at 4 hpc and an increase in expression of tlr22f, tlr22h and tlr22k (2.1-fold) was observed at 48 hpc compared to the initial control. Both tlr23a and tlr23b followed a similar pattern with significant reduction in the expression of tlr23a in gills (2.8-fold) and spleen (2.3-fold).

Quantification of teleost-specific Atlantic cod tlrs in response to bath challenge with V. anguillarum. Heatmap representing the expression of Atlantic cod tlrs in head-kidney, gills and spleen in response to bath challenge with V. anguillarum. After collecting initial control samples, fish were subjected to bath challenge with V. anguillarum strain H610 at a concentration of 2.6·10 7 �u·ml -1 . Samples were collected at 4 (4 hpc) and 48 (48 hpc) h post-challenge. Relative expression of tlr21, tlr22 and tlr23 was determined by qPCR and expressed as ratios between each sample and the respective initial control. Significance levels were set at P < 0.05 and statistically different expression values are enclosed in red boxes. Eef1a and ubi were used as internal controls.

Response to temperature stress

Following thermal shock, a significant down-regulation of tlr21 and tlr22 paralogues was observed both in head-kidney and spleen, and most of the transcripts returned to initial levels or were up-regulated at 72 hps (Figure  7 ). In both organs, up to 3-fold significant reduction in tlr21, tlr22f, tlr22g, tlr22i and tlr22k mRNA levels was observed at 4 hps. Tlr22a transcript levels did not show much change to stress, but had a 3.1-fold increase at 72 hps in head-kidney. Tlr22l expression in head-kidney increased by 3-fold following thermal stress and then up to 4-fold at 72 hps, albeit not significant. The highest change in transcript levels was recorded for tlr22d, with a 5.5-fold decrease in spleen at 4 hps. No significant change was observed in tlr23 expression with temperature stress.

Quantification of teleost-specific Atlantic cod tlrs in response to temperature stress. Heatmap representing the expression of Atlantic cod tlrs in head-kidney and spleen in response to temperature stress. Adult fish were maintained at 4ଌ. After collecting initial control samples, the water temperature was gradually increased to 12ଌ in 4 h (4 hps) and the fish were maintained at this temperature for 72 h (72 hps). Relative expression of tlr21, tlr22 and tlr23 paralogues was quantified by qPCR as ratios between each sample and the initial control. Significance levels were set at P < 0.05 and statistically different expression values are enclosed in red boxes. Eef1a and ubi were used as internal controls.

Molecular evolution of the cod tlr22 family

Tests of selection and relative rate tests

A pairwise codon based Z-test revealed that cod tlr22 paralogues are evolving at different rates (Table  3 ). The highest dN-dS values were observed between tlr22c and tlr22i (2.852, P = 0.003) or tlr22l (2.787, P = 0.003). Even tlr22c and tlr22d, which are encoded by adjacent genes in the cod genome, were found to be evolving at different rates (dN-dS = 2.157, P = 0.016). Tajima’s relative rate test further confirmed the evolution of cod Tlr22 paralogues through pairwise comparison of these protein sequences with Tlr22b as outgroup. The test revealed that Tlr22d has undergone relatively high divergence compared to all other Tlr22 paralogues (Additional file 3).

Table 3

Codon based Z-test of positive selection analysis between Atlantic cod tlr22paralogues

Atlantic cod paraloguestlr22btlr22ctlr22dtlr22ftlr22gtlr22htlr22itlr22jtlr22l
tlr22b  𢄠.436 𢄡.789 0.002 0.241 0.135 0.833 0.779 0.449
tlr22c1.000   2.157 1.554 1.186 2.072 2.852 2.656 2.787
tlr22d1.000 0.016  0.264 1.265 1.722 2.465 1.907 1.577
tlr22f0.499 0.061 0.396   1.817 1.968 2.345 2.020 2.389
tlr22g0.405 0.119 0.104 0.036   0.800 2.314 1.131 2.126
tlr22h0.446 0.0200.0440.0260.213   0.074 𢄠.427 0.306
tlr22i0.203 0.0030.0080.0100.0110.471   1.632 1.893
tlr22j0.219 0.0040.0290.0230.130 1.000 0.053   0.901
tlr22l0.3270.0030.0590.0090.0180.3800.0300.185 

A modified Nei-Gojobori method with Jukes-Cantor correction was used. The test statistic (dN-dS) is shown above the diagonal and the corresponding P-value is indicated below the diagonal. P-values less than 0.05 are highlighted in bold. Positions containing gaps were eliminated for this analysis and in total 708 codons were included in the final dataset.

Positive selection

A sliding window analysis of the complete coding sequence of nine tlr22 paralogues performed with SNAP revealed that the occurrence of non-synonymous mutations is not uniform throughout the coding sequence (Figure  8A ). The average dN/dS ratio for the complete coding sequence was 0.748 (dS = 0.223, dN = 0.167), while the ratio for the LRR region was much higher (dN/dS = 0.815) than for the TIR region (dN/dS = 0.313). These differences in substitution rates confirm that the TIR domain within teleost-specific Tlrs in cod is more conserved than the LRR region. Thus, the site-specific positive selection analysis focused on the latter. Likelihood ratio tests (LRTs) revealed that PAML models that allowed for adaptive positive selection fitted the data better than those which did not (M3 versus M0, p = 0 M2 versus M1, p = 0 M8 versus M7, p = 0) (Table  4 ). In total, 24 positively selected codons (PSCs) were identified by all three models, M2, M3 and M8, with ω values of 4.08, 4.36 and 4.06, respectively. SLAC and FEL analyses found 2 and 28 codons evolving under positive selection with p-value less than 0.1 (data not shown) and REL identified 19 sites PSCs with Bayes factor greater than 50 (Table  4 ). In total, the Datamonkey server analysis indicated 37 codons to be under selection pressure. The 24 sites indicated by the Bayesian approach using PAML were also selected by Datamonkey. All codons under positive selection were found within the N-terminal LRR domain, which recognises pathogens and 19 of these sites were present on the convex surface (Figure  8B , ​ ,8C). 8C ). Fifteen of the 24 PSCs were found within the LRR repeats. Only five of the 24 sites were found in beta sheets within the concave surface of the horseshoe-shaped domain, while most of the amino acids under selection pressure were on the structural components of the LRRs, the coils.

Codons under positive selection in Atlantic cod Tlr22 paralogues and their location within Tlr22b. A. Cumulative non-synonymous (green) and synonymous (red) substitutions for all pairwise comparisons between nine Atlantic cod tlr22 paralogues. The ratio of non-synonymous (dN) over synonymous (dS) substitution is greater in the LRR region than in the TIR domain. B. Multiple sequence alignment of cod Tlr22. Amino acid residues identical to Atlantic cod Tlr22b are represented by a dot and alignment gaps are indicated by a dash. LRR regions are shaded in grey and positively selected sites are boxed in red. The cysteine cluster within the LRRCT domain is marked in green. C. Predicted structure of Atlantic cod Tlr22b. LRR region with the positively selected sites highlighted in black. Their amino acid position is indicated by arrows.

Table 4

Identification of positively selected sites in Atlantic cod tlr22paralogues by maximum likelihood analysis

ModelsParameter estimatesLn likelihoodModel comparisonPositively Selected sites
M0: neutral ω = 1.12 �.27   None
M1: nearly neutral ω0 = 0.081, ω1 = 1 �.92   Not allowed
p0 = 0.39, p1 = 0.61
M2: positive selection ω0 = 0.05, p0 = 0.28 �.74 M2 vs M1 4, 6, 30, 41, 73, 126, 170, 224, 250, 274, 279, 318, 326, 333, 369, 371, 427, 443, 452, 455, 458, 478, 484, 501, 503, 505, 507, 509, 528, 531, 553, 577, 674
ω1 = 1, p1 = 0.54 2ΔlnL = 192.35,
ω2= 4.08, p2 = 0.18 df = 2, p = 0
M3: discrete ω0 = 0.18, p0 = 0.35 �.56 M3 vs M0 1, 3, 4, 6, 9, 11, 12, 13, 16, 18, 23, 25, 27, 28, 30, 33, 37, 40, 41, 43, 44, 49, 51, 52, 54, 56, 57, 59, 68, 70, 71, 73, 75, 76, 78, 80, 81, 82, 83, 94, 95, 97, 108, 110, 112, 115, 116, 122, 124, 126, 127, 129, 132, 134, 147, 152, 154, 156, 157, 159, 163, 169, 170, 172, 174, 175, 177, 178, 179, 180, 181, 189, 194, 196, 197, 210, 211, 212, 215, 224, 227, 230, 231, 233, 234, 236, 237, 240, 241, 245, 246, 247, 250, 251, 252, 253, 257, 260, 262, 267, 268, 271, 273, 274, 276, 277, 279, 281, 284, 287, 288, 294, 295, 297, 298, 301, 302, 303, 305, 307, 311, 313, 315, 316, 318, 320, 326, 330, 331, 333, 334, 335, 336, 338, 339, 340, 341, 342, 344, 345, 347, 349, 350, 352, 354, 355, 357, 358, 368, 369, 371, 375, 376, 378, 379, 382, 383, 387, 388, 392, 393, 395, 397, 400, 402, 403, 406, 410, 413, 414, 416, 417, 419, 421, 426, 427, 428, 429, 430, 432, 434, 438, 440, 441, 443, 445, 448, 452, 453, 455, 457, 458, 460, 471, 472, 474, 475, 476, 478, 480, 481, 484, 493, 495, 498, 499, 501, 503, 504, 505, 506, 507, 508, 509, 510, 517, 524, 526, 528, 529, 530, 531, 532, 533, 535, 538, 539, 548, 549, 550, 551, 552, 553, 555, 556, 557, 572, 574, 577, 578, 579, 595, 596, 598, 600, 601, 603, 613, 616, 619, 620, 622, 624, 639, 642, 643, 645, 650, 662, 666, 670, 673, 674, 680, 681, 691, 702, 712, 719
ω1 = 1.19 , p1 = 0.49 2ΔlnL = 413.43,
ω2= 4.36, p2 = 0.16 df = 4, p = 0
M7: β p = 0.02, q = 0.01 �.60   Not allowed
M8: β + ωSϡ p = 0.1, q = 0.05 �.86 M8 vs M7 1, 4, 6, 30, 41, 56, 73, 126, 170, 174, 224, 245, 250, 274, 279, 287, 295, 318, 326, 333, 342, 344, 355, 369, 371, 378, 393, 400, 427, 443, 452, 455, 457, 458, 460, 471, 474, 478, 484, 501, 503, 505, 506, 507, 509, 528, 529, 530, 531, 553, 577, 619, 674
ω = 4.062ΔlnL = 201.48,
p0 = 0.81, p1 = 0.19 df = 2, p = 0
REL   4, 6, 30, 41, 49, 73, 76, 126, 147, 157, 170, 224, 246, 274, 279, 295, 318, 320, 326, 333, 342, 369, 371, 397, 400, 427, 452, 455, 478, 484, 501, 503, 507, 509, 553, 578, 613

Only positively selected sites with Bayesian posterior probabilities above 95% are indicated and the ones greater than 99% are highlighted in bold.

In the REL analysis, positively selected sites with a Bayes factor greater than 50 are highlighted in bold.


3. Chemical Probes in Action: Applications in Imaging of Bacteria

In the following we will highlight some of the most relevant application areas of chemical probe-based imaging. These applications are summarized in Figure 2 and the probes are listed in Table 1 .

Applications of chemical probes in bacterial imaging. The images demonstrates the utility in difficult probes for visualizing and potential for purification, isolation and downstream analysis by cell-sorting. Exemplary imaging applications are illustrated with images from primary publications: (i) Uniform labeling of PG in live B. subtilis cells using FDAAs (HADA) [112]. The figure is reproduced with permission from [112]. (ii) The subcellular localization of PBPs during cell division and elongation in Lactococcus lactis is depicted by Bocillin-FL labeling [130]. The figure is reproduced from [130] under a Creative Commons Attribution (CC BY) license. (iii) Live� labeling in E. coli demonstrates FDAAs (HADA) labeling in live cells (blue) but not dead cells (red) [112]. The figure is reproduced with permission from [112]. (iv) Internalization of ciprofloxacin fluorophore derivatives in live E. coli with (green, left) and without (red, right) efflux pump inhibitor [126]. The figure is reproduced under a Creative Commons Attribution 3.0 Unported License from [126]—Published by The Royal Society of Chemistry. (v) Absence of acticity-based probes (ABP) labeling (purple) in some GFP expressing S. aureus cells (green) during exponential phase indicates phenotypically distinct subpopulations [7]. The figure is reproduced with permission from [7]. (vi) Vibrios are selectively labeled by the vibroferrin-derived fluorescent siderophore conjugate vibrioferrin-fluorescein (VF-FL) (top) while other species are not labeled (bottom) [60]. The figure is adapted with permission from [60]. Copyright (2017) American Chemical Society. (vii) Non-invasive optical in vivo imaging of a mouse with E. coli and S. aureus-induced myositis in the limb using fluorescently labeled vancomycin [146].

Table 1

Chemical probes and their application in visualizing bacterial structure and physiology.

Probe NameProbe TypeTargeted SpeciesMolecular TargetDetection Tag(s)ApplicationReferences
Vibrioferrin-FLNon-covalent targeted conjugateV. parahaemolyticus,
V. cholerae, and V. vulnificus
Siderophore uptake pathwayFluorophore/Bio-orthogonal handleVisualization of vibrioferrin uptake and selective detection of Vibrios under iron-limited conditions[60]
DOTAM𠄿LNon-covalent targeted conjugateP. aeruginosa and E. coliSiderophore uptake pathwayFluorophoreVisualization of iron transport and detection of bacterial infections[25]
MDPsNon-covalent targeted conjugateE. coli, P. aeruginosa, B. subtilis, and S. aureusMaltodextrin uptake pathwayFluorophore/RadiolabelVisualization of maltodextrin uptake and high-sensitivity detection of bacteria in vivo[27,73,124]
Neo𠄼y5Non-covalent targeted conjugateP. aeruginosa, A.
baumannii, K. pneumoniae, S. typhimurium,
and S. aureus
Aminoglycoside antibiotics uptake pathwayFluorophore/Bio-orthogonal handleVisualization of aminoglycoside uptake and mode of action[125]
Cipro-azideNon-covalent targeted conjugateE. coli and S. aureusAntibiotics uptake pathwayFluorophore/Bio-orthogonal handleUnderstanding the bacterial penetration and efflux pump mechanisms[126]
Van-FLNon-covalent targeted conjugateB. subtilis, S. pneumoniae, S. coelicolor and C. glutamicumPG stem peptide (D-Ala-D-ALA)FluorophoreVisualize nascent PG biosynthesis in live cells[64,127]
BOCILLIN-FLActivity-based probeE. coli, P. aeruginosa, and S. pneumoniaeActive PBPs (broad spectrum)FluorophoreBroad-spectrum detection of PBP activities in live cells.[128,129,130]
Ceph C-TActivity-based probeB. subtilis and S. pneumoniaePBPs 1a/1b, 2b, 2c, and 4 (B. subtilis) and PBP1b and 3b (S. pneumoniae)FluorophoreVisualize involvement of different
PBP subsets in live cells
[97]
β-lactone probesActivity-based probe S. pneumoniaePBP1a, PBP1b, PBP2x, and PBP2aFluorophoreVisualize the catalytic activity of PBP subsets
in live cells
[131]
Meropenem-derived probe MEM-FLActivity-based probe B. subtilisPBP3 and 5Fluorophore/Bio-orthogonal handleVisualize
PBP3 activity in single cells during cell division
[132]
Fluoro-phosphonates (FP-TMR)Activity-based probe S. aureusSerine hydrolasesFluorophore/BiotinIdentification of serine hydrolase activities[7,89]
JCP251-bTActivity-based probe S. aureusFluorophos-phonate-binding serine hydrolase B (FphB)FluorophoreVisualize subcellular FphB localization and distribution across cell population[7]
Triazole urea probesActivity-based probe S. aureusFluorophos-phonate-binding serine hydrolases and lipasesFluorophore/Bio-orthogonal handleAssessment of specific cellular serine hydrolase activity levels[84]
GlcA-ABPActivity-based probeMouse gastrointestinal
microbes
β-glucuronidaseFluorophore/Bio-orthogonal handleDetection, isolation and identification of microbial subpopulations in the gut microbiome[133]
CSL174Substrate probe M. tuberculosisHydrolase-important for pathogenesis 1 (Hip1)FluorophoreSpecific detection of Hip1 protease activity[12]
FLASHSubstrate probe M. tuberculosisHydrolase-important for pathogenesis 1 (Hip1)ChemiluminescentDetection of live M. tuberculosis[45]
Calcein- AMSubstrate probeM. tuberculosis and Mycobacterium smegmatisEsterasesFluorophoreSingle-cell assessment of esterase activity and probe uptake[103]
Redox Sensor Green (RSG) Substrate probe E. coliBacterial reductaseFluorophoreAssessment of cellular redox activity [104,105]
LPETG-derived peptides Metabolic labeling S. aureusSortase A�pendent cell wall anchoringFluorophore/Bio-orthogonal handleImaging of cellular Sortase A levels, cell wall re-engineering[134,135]
CLSP and CLLPSubstrate probeSalmonella spp. and L. monocytogenesEsterase and phosphatidylinositol-specific phospholipase C (PI-PLC)LuminophoreSelective detection of Salmonella spp. and L.monocytogenes from food samples[44]
Nitro-aryl fluorogenSubstrate probe B. subtilisNitroreductase activityFluorophoreVisualization subcellular localization of nitroreductases [106]
CDG-OMeSubstrate probe M. tuberculosisβ-lactamase (Bla) CFluorophoreDetection of live M. tuberculosis[136,137]
Cy5. 5-TTSubstrate probe S. aureusMicrococcal nuclease (MN)Quenched fluorophoreNoninvasive detection of S. aureus infections in mouse pyomyositis model[138]
D-alanine analoguesMetabolic labeling L. monocytogenesPeptidoglycanBio-orthogonal handleVisualization of PG dynamics[16]
Propargyl-cholineMetabolic labeling S. pneumoniaeTeichoic acidBio-orthogonal handleVisualization of pneumococcal teichoic acid biosynthesis.[139]
FDAAMetabolic labelingB. subtilis, E. coli, S. aureus, S. pneumoniae, Agrobacterium tumefaciens
and C. crescentus
PG stem peptideFluorophore/Bio-orthogonal handleVisualization of PG biosynthesis and illustration of bacterial growth and division[61,112,140,141,142]
KDOMetabolic labelingE. coli and Salmonella typhimuriumLPSFluorophore/Bio-orthogonal handleVisualization of LPS structure and location[143]
Homopropargylglycine (HPG)Metabolic labelingSulfate-reducing bacteria,
uncultured microbes
Protein synthesisBio-orthogonal handleSingle-cell assessment of translational activity[144,145]
L-azidohomo-alanine (AHA)Metabolic labelingE. coli, single environmental bacterial strains and complex samplesProtein synthesisBio-orthogonal handleSingle-cell assessment of translational activity[19]
Azido-modified trehaloseMetabolic labeling M. tuberculosisCell surface glycolipidsBio-orthogonal handleDetection and visualization of cell-surface glycolipids[15]
Trehalose analogsMetabolic labelingMycobacterium sppMyco-membraneFluorophore/Bio-orthogonal handleDetermination of the envelope structure of Mycobacterium[113]
Thioflavin T (ThT)Non-specific fluorescent dye B. subtilisMembraneFluorophoreQuantification of membrane potential[114]
DMN-TreEnvironmental sensor/Metabolic labeling M. tuberculosisMyco-membraneFluorophoreDetection of M. tuberculosis[63]

3.1. The Cell Wall

The bacterial cell wall is a complex macromolecular heteropolymer that provides stability to the cell, that controls uptake and release of molecules and that serves as a template for interactions with the environment. Studying the bacterial cell wall is not only relevant to understand cellular morphology, cell division and growth. As interference with cell wall structure and biosynthesis usually has detrimental consequences for the cell, a comprehensive understanding of mechanistic features of the PG structure, function, and biosynthesis is also crucial for the development of novel antibiotics.

The bacterial cell wall contains a rigid layer of peptidoglycan (PG). PG is composed of a glycan chain of repetitive disaccharide units of N-acetylglucosamine and N-acetylmuramic acid that are crosslinked via peptide bridges most commonly containing L-Ala, D-Ala, D-Glu, Gly, L-Lys, mesodiaminopimelic acid (DAP), or other amino acids. The exact composition and cross-linking architecture of the peptide bridges varies across different families of bacteria (e.g., Gram-positive, Gram-negative and mycobacteria), but also depending on the exact species and based on culture conditions [147,148].

The biosynthesis of peptidoglycan starts in the cytoplasm where the precursors of the disaccharide building blocks UDP-N-acetylmuramyl (UDP-MurNAc)-pentapetide and UDP-N-acetylglucosamine (UDP-GlcNAc) are synthesized, attached and combined to a membrane-localized isoprenoid carrier to give the central PG-precursor molecule Lipid II (reviewed in [149]). Lipid II is then flipped to the outer side of the cytoplasmic membrane (in Gram-positive bacteria), which is equivalent to the periplasmic side of the inner membrane of Gram-negative bacteria. Subsequently, the disaccharide units are incorporated into the existing, highly crosslinked peptidoglycan structure through the glycosyltransferase or transpeptidase activity of penicillin-binding proteins (PBPs) (reviewed by [150,151]). DD-transpeptidase activity of PBPs provides peptide cross-linking between D-Ala and DAP, whereas LD-transpeptidases perform cross-link two DAP residues [152]. Additionally, PBPs may also have DD-carboxypeptidase and/or endopeptidase activity [153,154]. Cell wall biosynthesis can be probed and visualized by two main strategies: First, by targeting the enzymatic activities, that shape the cell wall and second, by metabolic labeling of cell wall polymers.

3.1.1. Metabolic Labeling of Peptidoglycan

PG synthesis pathways and cell wall growth and structure can be studied using metabolic labeling with functionalized D-amino acids. Among the most commonly used D-amino acid (DAA) probes are clickable analogs of D-Ala and fluorescent D-amino acid (FDAA) analogs of D-Ala and D-Lys [112] each of which have different incorporation characteristics in different bacterial species. In order to facilitate sequential labeling protocols and ensure compatibility with other staining procedures, FDAAs are available such as blue light emitting (HCC-amino-d-alanine, HADA), green (NBD-amino-d-alanine, NADA, and fluorescein-d-lysine, FDL) or red (TAMRA-d-lysine, TDL) for PG labeling of live bacteria [140]. The use of FDAA has enabled, e.g., the detection of sites of new PG synthesis in cells live bacterial cells, thus distinguishing ‘old’ from ‘new’ PG and also differentiating metabolically active from inactive cells [140]. It has also helped to identify cell division inhibitors, to validate the role of cell division/elongation factors and other factors involved in PG synthesis using mutant strains [140]. Another breakthrough discovery was enabled through clickable D-Ala probes: They have solved the riddle of the 𠆌hlamydia anomaly’ (i.e., the fact that C. trachomatis is sensitive to PG-targeting antibiotics while no PG could be detected), by visualizing functional PG in C. trachomatis for the first time [141].

The incorporation of DAAs into PG can follow three different routes, depending on the species studied: Incorporation can occur into D-Ala-D-Ala through activity of cytoplasmic D-alanine D-alanine ligase (Ddl) or through extracytoplasmatic (e.g., periplasm in Gram-negative bacteria) L,D- or D,D transpeptidases (reviewed in detail in [150,151]). Interestingly, FDAAs were shown to be incorporated into PG of E. coli and B. subtilis by the extracytoplasmic pathways and not via the intracellular precursor D-Ala-D-Ala [142]. Importantly, the interplay of different routes of PG incorporation is of high relevance for antibiotic activity and resistance as it has been reported that the capability of LD-transpeptidases to bypass DD-transpeptidase activity results in higher level of resistance β-lactam antibiotics in both E. coli and E. faecium [155,156]. For a more detailed overview on research on how DAA-probes have contributed to our understanding of the dynamics of bacterial growth and division the reader is referred to this excellent review article [157]. A notable chemical innovation for D-amino acid probes was the development of fluorogenic D-amino acids, which provide lower background and are suitable for real-time imaging of peptidoglycan synthesis [8].

3.1.2. Dissecting the Activity of Penicillin-Binding Proteins

Elucidating the specific roles of different PBPs in PG synthesis is important in understanding cell growth and cell wall homeostasis as well as in understanding efficacy and resistance of PBP-targeting antibiotics. Fluorescent penicillin-derivatives (BOCILLIN-FL) have been the first PBP-targeting ABPs. They have been and still are useful tools for detection of PBPs in membrane fractions of labeled cells [129]. BOCILLIN-FL does not discriminate between different members of the PBP family, whereas fluorescently labeled analogs of the beta-lactam antibiotics cephalosporin C and meroponem, as well as beta-lactones have been used successfully to visualize the activity of individual PBPs in the context of living cells [91,131,132].

3.1.3. Targeting Cell Wall Precursors

Another strategy to interrogate PG structure and biosynthesis is by targeting the Lipid II precursor with fluorescent conjugates of vancomycin (a glycopeptide) and ramoplanin (a glycolipodepsipeptide) [64]. In non-invasive in vivo imaging studies, near-infrared fluorescent vancomycin-conjugates have been used to specifically detect murine infections caused by Gram-positive bacteria [146]. In their ability to differentiate Gram-positive from Gram-negative pathogens in complex living animals in real time fluorescent vancomycin-conjugates can be regarded as the ‘Gram stain’ of the 21st century.

Other fluorescent conjugates have been used efficiently to study their mode of action, such as dansylated polymyxins that reveal its interaction with LPS in the outer membrane of Gram-negative bacteria [65,158].

3.1.4. Targeting Other Components of the Cell Envelope

The PG layer also represents anchor sites for the attachment of further macromolecules, including cell wall anchored proteins, anionic polymers such as wall teichoic acid and polysaccharide capsules. In Gram-negative bacteria, surface-exposed polymers such as lipopolysaccharide can be anchored in the outer membrane. 3-deoxy-D-manno-octulosonic acid (KDO) is an essential component of LPS inner core and thus became an attractive candidate for the metabolic probe labeling approach [143]. Another good reason that KDO has been selected as a probe target because the 𠆜lickable’ probe analogue of KDO, such as 8-azido-8-deoxy-KDO is well-tolerated by KDO pathway in a wide range of Gram-negative bacteria [143]. Metabolic incorporation of KDO analogue into bacterial lipopolysaccharides has been shown to be independent of genetic modifications resulting in an efficient tool to investigate LPS structure and their role in the pathophysiological process [143]. In contrast, no broadly applicable technique is available for labeling of teichoic acids in Gram-positive bacteria. However, the unique feature of S. pneumoniae to incorporate choline into teichoic acid has enabled the use of propargyl-choline as a bio-orthogonal probe for metabolic labeling of teichoic acids that can be detected via CuAAC [139].

Secreted proteins can be anchored to the bacterial cell wall through the action of transpeptidases that recognize specific peptidic recognition motifs. In S. aureus, Sortase A recognizes its substrates based on an N-terminal LPXTG-sequence, cleaves between Thr and Gly and transfers the N-terminal part of the protein onto the free NH2-termini in Lipid II [134]. This Sortase-targeting anchorage motif has been exploited for the development of fluorescent and bio-orthogonal chemical probes that are decorated on the bacterial cell wall, enabling the subcellular localization of Sortase A substrates and allows for other cell wall engineering strategies [134,135]. Other cell surface structures such as flagella and pili play a crucial role in the bacterial pathogenesis involving cell motility, adhesion, chemotaxis, and conjugation. To the best of our knowledge, no specific chemical probes have been developed to investigate their function, however, some commercial non-specific fluorescent dyes have been used to capture flagella and pili in action in several bacterial species [9,159,160,161,162].

3.1.5. Trehalose and the Unique Cell Envelope of Mycobacteria

Mycobacteria, which include M. tuberculosis (Mtb), have a cell envelope that is very distinct from both Gram-positive and Gram-negative and which includes a unique mycomembrane layer comprising arabinogalactan and long-chain mycolic acids enriched with trehalose-containing glycolipids (reviewed by [163,164]). The application of fluorescent trehalose-conjugates revealed labeling of the mycobacterial membrane and poles and allowed the selective detection of Mtb within macrophages [113]. Other studies employed azide-modified trehalose analogues to visualize cell-surface glycolipids revealing mechanistic insights into the molecular pathways of trehalose modifications and recycling [15]. Trehalose-metabolism has also been exploited to develop an environmentally sensitive fluorescent trehalose probe that lights up within the hydrophobic environment of the mycobacterial cell envelope and can be used to rapidly and sensitively detect Mtb [63]. Intriguingly, a recent study using fluorescent aminoglycoside antibiotics revealed subpopulations of E. coli with different probe labeling properties. Weak labeling of a subpopulation was attributed to non-specific membrane-binding of the probe, while cells with high probe-labeling take up the antibiotic in an energy-dependent process, suggesting these two subpopulations possess different susceptibility to aminoglycoside antibiotics [125].

3.2. Dissecting Antibiotic Susceptibility and Resistance

Antibiotic resistance can be based on different mechanisms that may be studied using chemical probes, including target bypass mechanisms, antibiotic-modifying enzymes or altered cellular penetration/efflux properties. For PBP, in S. aureus or E. faecium individual PBP-s have been identified that are associated with low-affinity. With an expanding toolset of ABPs that selectively target PBPs [97,131,132], the visualization of resistance properties using chemical probes appears within reach.

Another reason for β-lactam-resistance may be β-lactamase enzymes that hydrolyse and inactivate β-lactam antibiotics [137]. Fluorogenic umbelliferone-cephalosporin conjugates that serve as a useful tool to study the naturally occurring β-lactamase of Mtb (Bla) [136,137]. It has been demonstrated that cephalosporin based fluorogenic probe is highly selective for Bla and thus applicable for point-of-care diagnostic purposes [136].

Another important mechanism of antibiotic resistance is altered antibiotic uptake and efflux properties due to the reduced expression of porins or increased expression of efflux pumps (detail reviewed [165]). These mechanisms can be studied using fluorescent antibiotic conjugates. Linezolid is a bacterial protein synthesis inhibitor that belongs to the class of oxazolidinone antibiotics and azide-functionalized linezolid probes can be used to visualize AB-uptake into Gram-positive bacteria [72], while the activity of efflux pumps prevents the labeling of Gram-negative cells with this probe [166]. Fluoroquinolone-derived and trimethoprim-derived probes have also been used to study penetration and efflux of bacterial cells and mutant and chemical inhibition studies showed that their intracellular accumulation is dependent on the activity of efflux systems [126,167]. In addition to studying the accumulation of antibiotics in bacterial cells, fluorescent conjugates have also been employed to study the tissue distribution of antibiotics [72].

3.3. Visualizing Specific Metabolic Uptake Pathways

3.3.1. Siderophores

Iron is an essential element for bacterial growth and survival. Siderophores secreted and utilized by microbes, engage in scavenging iron from their surroundings, which is crucial for bacterial survival under iron-limiting conditions. In the quest for new and improved drugs, AB-conjugates are an emerging strategy that promises to deliver ABs to cells that are otherwise not AB-sensitive [25,168,169]. These efforts have led to FDA-approval of the siderophore-cephalosporin conjugate cefiderocol which is highly effective against Gram-negative bacteria [170]. As these AB-conjugates turn the pathogen’s own uptake machinery against itself, the approach has been termed Trojan Horse strategy. Siderophore-uptake in E. coli is an active, energy-dependent process initiated through binding to receptor proteins on the outer membranes (OM) [171]. Fluorescent and radiolabeled catechol-based siderophore conjugates are efficiently taken up by a variety of bacterial pathogens and have been used for non-invasive optical in vivo imaging of a mouse model of P. aeruginosa infection [25]. The authors note that in many instances, they observed in microscopic and flow cytometry experiments that𠅏or unknown molecular reasons—only a subpopulation of cells were able to take up these probes [25], which highlights the utility of chemical probe-based in dissecting functional heterogeneity among cellular populations.

Other siderophore-probes rely on very specific uptake pathways and can only be taken up by a small group of bacteria, allowing for differentiation of bacterial species. One such example is Vibrioferrin (VF), fluorescent conjugates of which fluorescent VF-conjugates can selectively label Vibrio species under iron-limiting conditions, allowing their discrimination from S. aureus or E. coli [60].

3.3.2. Sugar Uptake

Another group of biomolecules whose cellular uptake is driven through a specific molecular machinery are sugars such as maltodextrin. Active maltodextrin uptake involves translocation through the OM via maltoporins, followed by periplasmic binding to maltose-binding protein and translocation through the inner membrane via maltodextrin transporters [124]. Due to a lack of maltodextrin transporters in mammals, fluorescent maltodextrin conjugates can serve as selective tools for non-invasive optical in vivo imaging of bacterial infections [73]. The same group demonstrated in a follow-up study that maltodextrin uptake can also be targeted by radiolabeled probes for visualization of sites of bacterial infections in mice through PET-imaging [26].

3.4. Visualizing Virulence-Associated Enzymes

Enzymes can be important virulence factors and chemical probe-based imaging can provide important insight into the subcellular localization and dynamic activity patterns of virulence-associated enzymes. Our recent work on the previously uncharacterized S. aureus fluorophosphonate-binding hydrolases (Fph) illustrates how ABPs can be instrumental both in the identification of new enzymatic activities, as well as in their functional validation [7,84]. Fluorescent conjugates of newly identified FphB-specific inhibitors revealed selective labeling of the target enzyme at specific sites of the cell envelope and growth-condition dependent labeling of the septal cross wall of dividing cells, suggesting its physiological role may be modification of substrates located in the cell wall [7,84]. We also demonstrated that FACS-analysis of ABP-labeled bacterial populations is suitable to dissect the dynamic distribution of enzymatic activity levels across cells in different growth environments [7,84].

In other instances, virulence factors have been targeted to enable probe-based pathogen-specific detection, which is the case for the cell envelope-associated M. tuberculosis protease Hydrolase-important for pathogenesis 1 (Hip 1) [12]. Elucidation of the proteolytic substrate selectivity profile of Hip1 has enabled the generation of highly specific hybrid canonical/non-canonical peptide fluorogenic substrates that are turned over by Mtb in a Hip1-dependent manner and promise the development of Mtb-specific imaging agents [12]. This Hip1-targeting peptide sequence has recently been introduced into the luminogenic probe FLASH that detects Hip1 activity with extremely high sensitivity [45].

Secreted nucleases are important virulence factors of S. aureus that are involved in degrading extracellular DNA of S. aureus biofilms. Quenched fluorescent oligonucleotide substrates with chemical modifications to prevent cleavage by mammalian nucleases provide a S. aureus nuclease-specific signal that has enabled non-invasive in vivo imaging of S. aureus infections in a mouse pyomyositis model [138]. Similar FRET-based nuclease probes were also useful to image surface-localization of the nuclease Nuc2 activity and identified peak activity during early logarithmic growth [172].

3.5. Biofilms and Other Microbial Communities

Biofilms are heterogeneous bacterial communities that are embedded in a self-produced extracellular matrix of polysaccharides, proteins and DNA. Biofilms are most often surface-associated and in the context of medical device/implant-associated infections, they are of great clinical concern as they are difficult to eradicate. For biofilm settings, the use of chemical probes is helpful visualize and quantify functionally different cells within the population. The most simple and common functional differentiation of cells within biofilms is between live and dead cells using a combination of different non-specific dyes with different cellular permeability in live and dead cells [173,174]. It should be noted, however, that factors other than cell death can affect cellular permeability read-outs [5,6].

Another clinically relevant cellular subpopulation of bacteria are persister cells. Persister cells are phenotypic variants within isogenic bacterial populations that can survive antibiotic treatment without developing genetic antibiotic-resistance [175]. These cells are morphologically indistinguishable from the bulk of antibiotic-susceptible cells. Brynildsen and coworkers applied fluorogenic substrates that are activated by reductases as markers for metabolic activity in combination to fluorescent-activated cell-sorting (FACS) and persister cell assays. These elegant studies revealed that, in E. coli, spontaneously occurring persisters in exponential phase were mostly derived from metabolically-dormant cells (with low fluorogenic probe labeling) [104], whereas the formation of triggered persisters in stationary phase required high redox-activity (high probe labeling) [105].

Another study employed the non-specific esterase probe calcein-AM in mycobacteria, followed by FACS-sorting, and antibiotic susceptibility testing [103]. The authors continued with a FACS-based transposon mutant screen that identified factors that affect calcein-AM staining in cells. This included obvious candidates such as esterases and efflux pumps, but also identified a mycobacterial divisome factor responsible for heterogeneity in polar growth. [103]

Target-selective probes can also serve as reporters for phenotypic heterogeneity in bacterial population as our already mentioned studies on the distribution of the enzymatic activities of the S. aureus serine virulence factors FphB and FphE across growth conditions revealed [7,84]. While the physiological relevance and molecular mechanisms behind this observation remain unclear, it showcases how the use of chemical probes in cellular imaging can uncover new biology𠅋oth by design and by serendipity.

While the previous examples illustrate the power of chemical probes in dissecting the physiological diversity within populations of single bacterial species, they have also been applied to deconstruct even more complex samples taken from environmental communities or the human microbiome. Whidbey and coworkers used beta-glucuronidase-specific activity-based probes to isolate bacterial subpopulations from mouse fecal sample and determine beta-glucuronidase-active taxa within the gut microbiome [133].

One emerging metabolic labeling strategy that has been used to differentiate single cells of environmental bacterial samples based on their translational activity is 𠆛io-orthogonal non-canonical amino acid tagging’ (BONCAT [19,144]). BONCAT makes use of azide-/or alkyne-functionalized analogs of L-methionine, which are incorporated into newly translated proteins and can be visualized by conjugation fluorescent dyes in a bio-orthogonal reaction post-labeling [19] and allows the separation of cells based on their anabolic activity by fluorescence-activated cell sorting [62,144].


Biodegradation by Members of the Genus Rhodococcus: Biochemistry, Physiology, and Genetic Adaptation

This chapter focuses on biochemical and genetic versatility of members of the genus Rhodococcus and further emphasizes the importance of these bacteria in environmental applications. The genus Rhodococcus includes a diverse grouping within the wider group of nocardioform actinomycetes and is common in many environmental niches from soils to fresh water, seawater, plants, and animals. The remarkable ability of members of the genus Rhodococcus to degrade many organic compounds, their ability to produce surfactants, and their environmental persistence make them ideal candidates for enhancing the bioremediation of contaminated sites. With respect to their environmental significance, metabolic versatility, and potential for biotechnological applications, rhodococci are in some respects similar to the pseudomonads and related bacteria. Its genetic diversity is immense and the selection of a representative strain is difficult. A feature that can influence segregation of genetic elements, and which is often not considered, is their cellular pleomorphism.


RESULTS

Epidermal Cells of V. aestivalis and V. vinifera Respond Differently to Conidiospores

To compare the characteristics of PM-induced symptoms in the two grapevine genotypes, we conducted a microscopy study of conidiospore germination and hyphal development during a 6-d time period. Microscopic images of 24, 48, and 120 hpi are presented in Figure 1 . Conidiospores produced appressoria and secondary hyphae on both V. vinifera and V. aestivalis leaves at 24 hpi. In V. aestivalis leaves, most epidermal cells invaded by the conidiospores exhibited brown coloration, which was visible after tissue was cleared of chlorophyll ( Fig. 1 ). This browning appeared more intense in the region of the cell wall. Brown-colored cells were also observed beneath appressoria that developed from secondary hyphae on V. aestivalis leaves at 120 hpi. The infection led to the formation of colonies with dense secondary hyphae on V. vinifera leaves but only small colonies with sparse hyphae on V. aestivalis leaves by 120 hpi.

Progression of PM on V. vinifera and V. aestivalis leaves. Shown are representative images taken at 24, 48, and 120 hpi with conidiospores. Spores and hyphae were stained with 0.05% aniline blue. Images were taken at 100× magnification under transmission light. Scale bar = 65 μm. Insets inside the top two photos are images of 600× magnification. Scale bar = 25 μm. bc, Brown cell hp, secondary hyphae sp, conidiospore.

SA Is Present at Elevated Levels in PM-Infected V. vinifera and in Mock-Inoculated V. aestivalis

It is known that SA is a signal molecule in the induction of host defense responses, including hypersensitive response and systemic acquired resistance, and that the increase of endogenous SA levels is associated with the activation of PR gene expression (Shah, 2003). To assess if SA levels change during PM infection in V. aestivalis and V. vinifera, we measured the total SA content in PM-infected leaf tissue of V. aestivalis and V. vinifera in comparison with mock-inoculated samples by HPLC. Changes of SA content in PM-inoculated V. aestivalis at 24, 48, and 120 hpi were not statistically significant in comparison with mock-inoculated leaf tissue ( Fig. 2A ). In contrast, we found that SA levels increased in PM-infected leaf tissue of V. vinifera at 120 hpi, but there was no significant difference in SA levels between PM-inoculated and mock-inoculated samples at 24 and 48 hpi ( Fig. 2B ).

Endogenous levels of total SA in V. aestivalis and V. vinifera. A, Accumulation of SA in the PM-infected V. aestivalis leaf tissue (I) in comparison with mock-inoculated samples (M) at 24, 48, and 120 hpi. B, Accumulation of SA in the PM-infected V. vinifera leaf tissue (I) in comparison with mock-inoculated samples (M) at 24, 48, and 120 hpi. Values are the average of three biological samples for each time point. Error bars represent sd , n = 3.

Our earlier results from the analysis of genotype-specific transcriptome changes demonstrated that representative PR genes including PR-2 and PR-3 were transcribed constitutively at higher levels in V. aestivalis than in V. vinifera (Fung et al., 2007). It is possible that higher expression of PR genes is a result of elevated SA levels in V. aestivalis. To test this possibility, we compared the levels of endogenous SA between the two grapevine genotypes under mock-inoculation conditions. We found that the endogenous SA content in V. aestivalis was significantly higher than in V. vinifera in the absence of the fungus ( Fig. 2 ).

PM-Responsive Transcript Profiles Are Distinct in the Two Grapevine Genotypes

In a previous study, we found that transcriptome changes can be reliably measured in both V. aestivalis and V. vinifera by using the Vitis GeneChip (Fung et al., 2007). This finding allowed us to compare transcript abundance between the PM-inoculated and mock-inoculated plants in the two grapevine genotypes at 0, 4, 8, 12, 24, and 48 hpi. The data of three independent biological replicates were collected and analyzed. We conducted two independent F tests (one for each genotype) to determine whether the expression level of a transcript in the PM-inoculated plant was different from the mock-inoculated plant at any time point. We also conducted an additional F test with a model that incorporated data from both genotypes and included an effect of the genotypes to test the same null hypothesis. To account for heteroscedasticity of error variances between the two grapevine genotypes, the distribution of the residuals ɛijkm was assumed normal with error variance σ 2 N for residuals associated with V. aestivalis observations and σ 2 C for residuals associated with V. vinifera. If the null hypothesis was rejected, this indicated that the level of a transcript between PM-inoculated and mock-inoculated samples differed for at least one time point, and that the transcript was deemed to be PM-responsive for that genotype. To declare statistical significance and account for multiple tests, we used a false discovery rate (FDR) level of 0.05 approximated using the approach of Benjamini-Hochberg (Benjamini and Hochberg, 1995). We identified 626 transcripts on the Vitis GeneChip that were differentially expressed in V. vinifera. In contrast, only four transcripts were considered to be differentially expressed in V. aestivalis at a 0.05 FDR level three of them were also found in the 626 PM-responsive transcripts of V. vinifera. In a serendipitous discovery, one of the PM-responsive transcripts (Affy ID 1615715_at) aligned with an EST of the fungus E. necator, and its predicted amino acid sequence is homologous to a hypothetical protein MG09900.4 from Magnaporthe grisea (e = 2e-15). This fungal transcript was consistently present in PM-inoculated V. aestivalis and V. vinifera but absent in the mock-inoculated samples across all six time points. This transcript fortuitously served as a control probe set confirming the presence and absence of conidia in the PM- and mock-inoculated samples, respectively. Thus, three and 625 plant-specific transcripts, respectively, were found. Gene names based on sequence homology to other plant species, UniGene ID, log-transformed expression value, fold-change, nominal P value, and FDR-corrected P value for the 625 PM-responsive transcripts in V. vinifera and the three PM-responsive transcripts in V. aestivalis are provided (Supplemental Table S1).

For the genes that were significantly different between PM- and mock-inoculated samples for at least one time point, we classified the differences at each time point as up-regulated, down-regulated, or the same based upon the nominal P value for the contrast between PM- and mock-inoculated samples at that time point and the direction of difference (Supplemental Table S1). We observed that the number of PM-responsive transcripts increased as PM developed in V. vinifera ( Fig. 3 ). The total number of up- and down-regulated transcripts during the early infection stage (0𠄸 hpi) was around 100 to 150 and then increased to over 250 at 12 hpi and 350 at 48 hpi ( Fig. 3 ). Further analysis of the 625 PM-responsive transcripts indicated that they represented 598 genes (510 UniGenes and 88 singletons) based on the UniGene assignment in the National Center for Biotechnology Information (NCBI). Twenty of the 510 UniGenes were represented by more than one probe set. In total, 240 genes (175 UniGenes and 65 singletons) were up-regulated and 345 genes (323 UniGenes and 22 singletons) were down-regulated in at least one time point, while four genes (UniGenes) were both up- and down-regulated during some of the time points. Expression of 12 of the genes (11 UniGenes and one singleton) was significant only for the initial test but not in the individual time-point test.

Number of transcripts (probe sets) that are differentially expressed in response to PM inoculations relative to mock inoculations of V. vinifera at each of the six time points.

We hypothesized that a possible reason for the low number of PM-responsive transcripts in V. aestivalis was that many of the 625 PM-responsive transcripts were constitutively expressed at a higher or lower level in V. aestivalis than in V. vinifera even prior to PM infection. To test this hypothesis, we compared the transcript levels of the two genotypes for the 625 PM-responsive transcripts of V. vinifera. We first tested whether the two genotypes were different at any time point at an FDR of 0.05. The nominal P value for the contrast at an individual time point, together with the direction of the observed difference, was used to classify the difference between genotypes at that time point as higher, lower, or the same. We found that 508 out of 625 PM-responsive transcripts showed higher or lower expression in V. aestivalis in comparison with V. vinifera for at least one individual time point. Of these 508 transcripts, 83 transcripts were expressed at a higher level and 219 were expressed at a lower level in V. aestivalis in all six time points under the mock-inoculation condition. We also tested whether our findings were consistent with differential treatment response directly by testing the interaction of treatment and variety across all time points. Of the 625 transcripts identified, 533 showed evidence for an interaction between treatment and variety (FDR 0.20).

Representative Genes Are Verified by Quantitative PCR

We performed quantitative real-time PCR (qRT-PCR) assays on a subset of genes to verify differential expression measured in the microarray analysis. Thirteen genes were selected from the 598 PM-responsive genes in V. vinifera (FDR threshold P value < 0.05). Two of the three that were differentially regulated in V. aestivalis in response to PM infection were also analyzed by qRT-PCR. The degree of change in transcript abundance of each gene determined by the microarray and by the qRT-PCR assay was compared by using the difference in natural log values between PM- and mock-inoculated samples for each of the six time points (Supplemental Table S2). For 13 of the 15 genes, the results between the qRT-PCR and microarray were in agreement. The correlation between the microarray and qRT-PCR estimates was positive in all cases and significantly different from zero (Supplemental Table S2 Supplemental Fig. S1). The lowest observed correlation was 0.61 at 0 hpi and the highest was 0.90 at 24 and 48 hpi. For two of the 15 genes (1611550_at and 1611058_at), concordance at the 0 and 12 hpi time points was poor. It appeared that these two genes showed absolute differences in expression levels between the two platforms and, on this basis, were eliminated from comparisons for the remaining time points.

Expression Profiles of PM-Responsive Transcripts Are Distinct across the Six Time Points

To acquire a global overview of the PM-responsive transcriptome, we performed a nonlinear cluster analysis on the difference between PM- and mock-inoculated samples of the 14,571 informative transcripts in V. vinifera that were detected in at least one sample (Qu and Xu, 2006). This approach clusters gene expression profiles based on the pattern of the mean differences between the expression values of PM- and mock-inoculated samples over the six time points. In total, 25 clusters were identified (Supplemental Fig. S2 Supplemental Table S3). We found that many of the genes in clusters 5, 16, and 18 were among the 625 statistically significant PM-responsive transcripts (25.4%, 100%, and 23.3%, respectively). Figure 4A shows the distribution of 625 PM-responsive transcripts in each cluster. Interestingly, these three clusters showed distinct expression patterns that seem to reflect a progression of PM infection ( Fig. 4B ). Furthermore, most PM-responsive transcripts from clusters 5, 16, and 18 were known to respond to plant pathogens ( Fig. 4B ). PM-responsive PR-1, PR-2, PR-3, PR-4, PR-5, and PR-8 belonged to cluster 5 together with a bZIP transcription factor and a dirigent-like protein oxidase. Cluster 18 contained three PR-10s and five stilbene synthase genes, a PR-2, a PR-3, a WRKY, and also a gene encoding a dirigent-like protein ( Fig. 4B ). Cluster 5 was characterized by a steady increase in transcript abundance starting from 12 hpi, while clusters 16 and 18 represented a pattern whose expression peaked at 12 hpi and then decreased at 24 hpi ( Fig. 4B ).

Cluster analysis of the PM-responsive transcripts from V. vinifera. A, Percentage of entire probe sets on the whole Vitis GeneChip that was distributed in each cluster (dashed line) and proportion of the 625 significantly expressed transcripts (Si-probe set) in each of the 25 clusters (solid line). B, Distinct expression patterns of genes in clusters 5, 16, and 18. On the right is a list of genes among the 625 PM-responsive transcripts that are grouped together in that cluster.

Key Defense Genes Change in PM-Inoculated V. vinifera

We found that the expression level of genes encoding PR-2 (β-1,3-glucanases), PR-3 (chitinases), and PR-5 (thaumatin-like protein) increased upon the PM infection across the course of the infection process (Supplemental Table S1), confirming previous reports that these genes are associated with grapevine defense against pathogens (Derckel et al., 1996 Busam et al., 1997 Salzman et al., 1998 Jacobs et al., 1999 Renault et al., 2000 Tattersall et al., 2001 Ferreira et al., 2004). We also identified many defense/PR genes that have not been well characterized in the interactions between PM and grapevine. These genes are potentially involved in defense signal perception and MAPK-mediated signal transduction, transcriptional regulation, phytoalexin and lignin biosynthesis, cell wall modification, and metabolism of reactive oxygen species (ROS Table I ). We found at least eight receptor-like kinase (RLK) genes ( Table I ). Three of them were homologous to the Avr9/Cf-9 rapidly elicited (ACRE) 256 gene in the tobacco (Nicotiana tabacum) plant. The expression profile of one RLK gene is presented in Figure 5 . Two genes were homologous to Arabidopsis (Arabidopsis thaliana) AtMEK1 and AtMPK3 encoding a MAPKK and a MAPK, respectively ( Table I ). The MAPKK gene was induced as early as 12 hpi ( Fig. 5 ). For the two EDS1 genes that were identified ( Table I ), one was induced at 24 and 48 hpi ( Fig. 5 ), while the other was up-regulated at 4 and 48 hpi. Seven members of the WRKY family were up-regulated ( Table I ). The closest Arabidopsis orthologs of these WRKYs are AtWRKY11, 15, 33, 40, 53, and 75. The expression profile of the AtWRKY53 homolog representing an example of the WRKY gene expression pattern is shown in Figure 5 . One PR-1 was induced in V. vinifera after 8 hpi ( Fig. 5 ) and its closest Arabidopsis homolog At2g14610 is the only PR-1 that was induced by SA or pathogen infection (Uknes et al., 1992 van Loon et al., 2006). PR-1 is a marker gene that indicates the onset of local defense and systemic acquired resistance, although its precise enzymatic activity and its function have not been defined yet (van Loon et al., 2006). Five PR-10 genes were identified four of them were up-regulated, while one was slightly repressed. One PR-10 was induced at 8 hpi and continued to increase from 12 to 48 hpi ( Fig. 5 ). Thus, it is likely that the induction of PR-1 and PR-10 is indicative of the defense response during the grapevine-PM interaction as in other plant-microbe pathosystems (van Loon et al., 2006). We identified four different PR-9 genes involved in ROS metabolism that encode peroxidases two of them were induced ( Fig. 5 ), and the other two were repressed by PM in V. vinifera. Three NADH dehydrogenase genes were repressed in PM-inoculated V. vinifera leaves and also highly expressed in V. vinifera in comparison to V. aestivalis ( Fig. 5 ). Three glutathione S-transferase (GST) genes were also induced by PM (Supplemental Table S1). Differential expression of these defense-related genes indicates the activation of defense pathways even in compatible interactions between grapevine and the fungus.

Table I.

Representative defense- and secondary metabolism-related transcripts that were differentially expressed at one of the six time points after V. vinifera was infected with PM


Access options

Get full journal access for 1 year

All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.

Get time limited or full article access on ReadCube.

All prices are NET prices.


Perspectives

The divergence of balanced complexes presents a problem that has yet to be fully explored experimentally or theoretically. Namely, if a balanced set of interacting genes is resistant to changes in stoichiometry, as the broad evolutionary evidence suggests, how is it possible for them to diverge? One possibility is that selection operating on one member of a complex will present conflict with the interacting members and place selection pressure on the interaction properties ( Birchler et al., 2007 ). An example of selection on one member of a complex affecting interacting partners has been described in the case of the male-specific lethal complex in Drosophila ( Levine et al., 2007 Rodriquez et al., 2007 ). Another potential means for a new balance to evolve is if there are changes among the target loci that occur in cis that alter the level of expression ( Birchler et al., 2007 ). There is some evidence for increasing cis regulatory divergence with increasing genetic distance ( Lemos et al., 2008 Wittkopp et al., 2008 ). Over time such changes could accumulate among the critical target genes controlled by a regulatory complex and allow subtle variation in the balanced interactors to shift the stoichiometric relationships. We are not aware of whether any evidence for such a scenario has been sought. Further, a completely unexplored concept is the role of microRNA modulation of translation of proteins that are involved in balanced complexes. MicroRNAs could potentially modulate the expression of genes subtly to either maintain a protein–protein balance or cause it to diverge. Retained duplicates during diploidization may find themselves duplicated again in turn during a subsequent tetraploidization event. With now eight copies of a gene present, changes in the amount of the gene product might be tolerated and a copy of the gene can be lost or diverge.

One of the future directions that deserves attention is experimental work on the basis of how changing the stoichiometry of subunits of multi-molecular complexes affects the action of the complex. One possibility involves the ordered assembly of the complex in such a manner that nonfunctional subcomplexes form regularly and diminish the amount of the full complex that can be assembled ( Veitia, 2002, 2003 Veitia et al., 2008 ). Another possibility is that with varied amounts of subunits, the multisubunit complex will form and the unassociated subunits are degraded ( Veitia et al., 2008 ).

Another issue of interest is how the regulatory balance is manifested. The genetic and evolutionary evidence indicates that regulatory systems that operate in complexes are tightly conserved whereas the target loci can be deleted back to a diploid level following tetraploidization or can be varied regularly in copy number variants. However, the effect of the regulatory balance must ultimately be realized through the expression of target loci that perform cellular metabolism and developmental processes. The dynamics of the regulators and their target loci has not been explored in the context of balance.

Another topic worthy of theoretical exploration in the context of regulatory balance is the fate of duplicate genes and their evolution of new functions. Duplicate genes have the potential to divide their activities via a process referred to as subfunctionalization. Alternatively, one of the copies could evolve a new property, which is referred to as neofunctionalization ( Lynch & Conery, 2000 ). Either process would hold the duplicate pair in the evolutionary lineage. Clearly, we know that neofunctionalization has given rise to new regulatory and housekeeping genes. However, we now know from repeated tetraploidization events followed by diploidization in plants and Paramecium that duplicate genes can be held in a lineage because of stoichiometric constraints involved with molecular complexes. Other classes of genes are regularly deleted back to the diploid level and are thus rarely maintained by subfunctionalization or neofunctionalization. However, exceptions exist such as disease resistance genes that are in a constant ‘arms race’ with pathogens. Nevertheless, it is important to note that the maintenance of duplicate regulatory genes via balance constraints will hold them in an evolutionary lineage for a longer time-frame than other classes of genes. This scenario would provide a longer time-frame for cases of neofunctionalization to occur. Also, the degree to which these factors hold duplicate genes in a lineage deserves further study. In plants and Paramecium, as the tetraploidization events recede into the distant past, fewer of the duplicate pairs persist, suggesting that subfunctionalization and neofunctionalization, while important for evolutionary novelty, are not pervasive ( Freeling, 2008 ).

The gene balance hypothesis suggests that quantitative traits will be controlled by a large number of genes that can contribute variation. This variation is constrained because of the interacting balance of regulators to a very narrow window around the population norm under most conditions. However, because of the large number of loci involved with any one trait, subtle progressive changes to large extremes are possible under intense selection. Subtle regulatory variation of small magnitude, but present at many loci, can be neutral for long periods of evolutionary time, providing the opportunity for novelty to evolve with a change of environment. Thus, the gene balance hypothesis has the potential to explain the apparently contradictory observations that the status quo can be maintained for eons but extreme novelty can evolve within a few generations.