ByRachel Wallach

Proteins must fold themselves up into specific three-dimensional shapes to perform tasks required by the cell for function and survival. But when proteins fold into the wrong shape, they aggregate or clump togetherthink of the way eggs transform from liquid to solid during the cooking process.

Misfolded proteins can disrupt the normal functioning of the cell, and are associated with a wide range of diseases. When the proteins inside neurons aggregate, the toxic structures they create cause neurodegenerative diseases like Alzheimer's and Parkinson's. "Over billions of years of evolution, cells were challenged with the task to get their proteins to fold up correctly and stay that way," says Stephen Fried, assistant professor in the Department of Chemistry in the Krieger School of Arts and Sciences. "But we humans live for a pretty long time, and as the proteins in our brains get older, there seems to be a slow process where they forget what shape they're supposed to be in. They form structures that stick to each other, ultimately leading to the death of neurons, dementia, and other maladies associated with age."

Fried has received an NIH Director's New Innovator Award from the National Institutes of Health's High-Risk, High-Reward Research program to continue his studies into both the normal process of protein folding and what happens on a molecular level when the process goes awry. The award, in the amount of $1.5 million over five years, supports unusually innovative research from early career investigators.

Francis S. Collins

Director, National Institutes of Health

"The breadth of innovative science put forth by the 2020 cohort of early career and seasoned investigators is impressive and inspiring," said NIH Director Francis S. Collins. "I am confident that their work will propel biomedical and behavioral research and lead to improvements in human health."

Scientists have been trying to understand the folding process for some time by studying purified proteins in test tubes. What sets Fried's research apart is that he and his team are studying the normal and abnormal folding of proteins in their native contextin this case, within rodent brains.

"We think that the tools we're developing on the front part of our project will give us a new view into why cells are so good at getting their proteins to assemble into such complicated and intricate architectures," Fried says. "We will then apply the tools to take a look at what's going on inside rats' brains at the molecular level when they age. Specifically, we want to know what's different in cognitively healthy versus cognitively impaired rats."

Fried has been collaborating with Michela Gallagher, Krieger-Eisenhower Professor of Psychology and Neuroscience, whose long-term research on Alzheimer's-related changes within the brain has produced experimental drugs now in clinical trials. While Gallagher's team focuses on the brain's structures, Fried's team complements the research by working at the molecular level. "Our collaboration will zoom in on the proteins forming incorrect shapes inside the brain; what they are interacting with, and what shapes they are forming," Fried says.

It is the opportunity for such interdisciplinary work that made the NIH award possible, Fried says, pointing to the preliminary data he and Gallagher were able to produce that he believes convinced the reviewers to take a chance on this uncharted territory.

Jotham Suez, a postdoctoral fellow at the Weizmann Institute of Science and is expected to join the Johns Hopkins Bloomberg School of Public Health as an assistant professor in the Department of Molecular Microbiology and Immunology in January, was also awarded an Early Independence Award from the High-Risk, High-Reward Research program. A microbiologist, Suez focuses on how non-nutritive sweeteners affect microbiomes. His early research indicated that non-caloric sweeteners can negatively affect health through the disruption of gut bacteria, and his research at JHU will focus on deciphering the underlying mechanisms.

The High-Risk, High-Reward Research program catalyzes scientific discovery by supporting research proposals that, due to their inherent risk, may struggle in the traditional peer-review process despite their transformative potential. Program applicants are encouraged to think "outside the box" and to pursue trailblazing ideas in any area of research relevant to the NIH's mission to advance knowledge and enhance health.

Read more here:
Hopkins chemist awarded New Innovator Award from National Institutes of Health - The Hub at Johns Hopkins

Abstract

It is generally accepted that prokaryotes can tune gene expression noise independently of protein mean abundance by varying the relative levels of transcription and translation. Here, we address this question quantitatively, using a custom-made library of 40 Bacillus subtilis strains expressing a fluorescent protein under the control of different transcription and translation control elements. We quantify noise and mean protein abundance by fluorescence microscopy and show that for most of the natural transcription range of B. subtilis, expression noise is equally sensitive to variations in the transcription or translation rate because of the prevalence of extrinsic noise. In agreement, analysis of whole-genome transcriptomic and proteomic datasets suggests that noise optimization through transcription and translation tuning during evolution may only occur in a regime of weak transcription. Therefore, independent control of mean abundance and noise can rarely be achieved, which has strong implications for both genome evolution and biological engineering.

Understanding the sources of diversity among individuals in a population has been a long-standing problem in biology. Genetic variability and environment account for most of this diversity. However, genetically identical individuals sharing the same environment still exhibit some phenotypic variability. This variability has been observed for more than half a century (13), and its mechanistic origins and evolutionary consequences have been intensively studied in the past decades (46).

Phenotypic variability stems from the stochastic nature of intracellular biochemical processes, in particular, gene expression. Gene expression involves many molecular events requiring the random encounter of chemical species that are present in small numbers inside the cell, leading to stochastic births and deaths of mRNAs and proteins (intrinsic noise) (4, 7). In addition, gene expression relies on many molecules such as polymerases, ribosomes, nucleotides, or amino acids, whose concentration can fluctuate inside the cell, creating a stochastic environment for the protein production process (extrinsic noise) (4, 7). The intrinsic and extrinsic components of noise can be assessed using the dual-reporter method developed by Elowitz et al. (4), who found that both extrinsic and intrinsic sources can substantially contribute to noise in prokaryotic gene expression.

Gene expression can be divided into two main steps, namely, transcription and translation. The relative contribution of these two processes to noise generation has been investigated both theoretically and experimentally (811). The classical two-stage model of gene expression, which describes the temporal evolution of the number of mRNA molecules and the number of proteins as two Markovian birth and death processes (12), predicts a different impact of transcription and translation on gene expression noise (6, 8, 12, 13). In particular, in this model, the Fano factor of the protein copy number distribution, i.e., the variance divided by the mean, increases linearly with the rate of translation but is independent of the rate of transcription (8). This differential effect of translation and transcription on noise reflects the importance of mRNA fluctuations in protein expression noise. mRNAs are present in small numbers in the cells and are therefore subject to strong fluctuations. mRNA fluctuations generate fluctuations in protein abundance, whose amplitude depends on the efficiency of translation, a phenomenon called translational bursting (6, 13, 14). The translational bursting mechanism was experimentally tested by Ozbudak et al. (9), who constructed four Bacillus subtilis strains expressing the green fluorescent protein (GFP) under an inducible promoter but four different translation control elements. Measuring GFP abundance in single cells by flow cytometry, for the four different strains under different induction conditions, Ozbudak et al. (9) concluded that the Fano factor (variance divided by the mean), also called the noise strength, linearly depends on the translation rate but is largely independent of the transcription rate, confirming the translational bursting mechanism predicted by the two-stage model. As a result, the mean abundance of a protein and the expression noise have been deemed to be independently controllable through combinations of transcription and translation control elements.

Noise in intracellular processes can limit the performance of the cell by driving it away from the optimal concentration of its molecular components (1518). In contrast, it can also be used to create diversity in a clonal population. This diversity can be the basis for bet-hedging strategies in case of fluctuating environments (1922), and it allows division of labor (23). Consequently, noise optimization can lead to substantial selective forces acting on genome evolution (5). It has, for instance, been shown that some regulatory motifs, such as negative feedback loops, can decrease the level of noise (24). These motifs can therefore be selected for during evolution on the basis of their noise reduction property. Likewise, the position of the gene in the genome can affect its expression noise (25, 26), and noise optimization has thus been proposed to exert a selective force on genome organization. The independent control of mean abundance and noise by translation and transcription control elements, such as that described by Ozbudak et al. (9), offers a particularly simple way to modulate the level of noise in the expression of a given gene. In other words, a given mean expression level can then be achieved through different strategies leading to different noise levels: with a strong transcription and weak translation, leading to low noise levels, or with a weak transcription and strong translation, leading to high noise levels (6, 9). This would have important implications both for genome evolution and for synthetic biology and biological engineering, where the genetic elements controlling transcription and translation could be tuned to reduce noise and optimize a bioproduction process (27).

The translational bursting mechanism predicted by the two-stage model and evidenced in B. subtilis (9) is in agreement with later system-wide analysis in yeasts. A large number of naturally expressed proteins in Saccharomyces cerevisiae showed a scaling between protein abundance and noise, where the squared coefficient of variation is proportional to the inverse of the mean (28, 29). This scaling was interpreted as the result of mRNA fluctuations (28, 29). Similar system-wide analysis in the model bacterium Escherichia coli revealed that a similar scaling exists for very weakly expressed genes (30). However, it does not hold for most of the proteome (30), questioning the generality of translational bursting. Although translational bursting is generally assumed to be the main mechanism underlying noise generation in prokaryotes, the experimental evidence is still scarce. To our knowledge, the study of Ozbudak et al. (9) is the only one where the effects of transcription and translation on noise were independently measured. Although the results of this study are coherent with theoretical predictions, this simple picture is clouded by several issues. First, the two-stage model is based on several questionable assumptions, such as the Poissonian production of mRNAs (31, 32), and it only describes intrinsic noise. Second, the experimental data of Ozbudak et al. (9) are based on only four strains with different translation control elements, and transcription is varied using an inducible promoter, whereas noise at intermediate induction levels are known to be strongly affected by extrinsic fluctuations in the activity of the regulatory protein mediating induction (4).

Translational bursting and the associated differential effect of transcription and translation rates on noise are often evoked as the basis for noise optimization strategies. Given the discrepancy between the importance of the result and the scarcity of experimental evidence, we decided to revisit the relative contributions of transcription and translation in prokaryotic gene expression noise. To that end, we implemented a strategy similar to the one developed by Ozbudak et al. (9), allowing us to test independently the effect of translation and transcription. We designed a library of 40 strains of B. subtilis, where the chromosomally inserted gene of GFPmut3 is expressed under the control of a combination of different translation and transcription control elements. As a result, the fluorescence of the strains covers a wide range of expression that is representative of the entire natural range of expression in B. subtilis. For each strain, the fluorescence was quantified at the single-cell level using fluorescence microscopy and flow cytometry. We showed that in contrast to the prediction of the two-stage model and to previous experimental findings in B. subtilis (9), the noise strength (or Fano factor) increases linearly with both transcription and translation rates. Using the dual-reporter method designed by Elowitz et al. (4), we showed that this unexpected result can be explained by extrinsic noise.

We designed a library of 40 B. subtilis strains in which the gene of the GFPmut3 protein is inserted into the chromosome and expressed under the control of a combination of eight different transcription control elements (transcription modules) and five different translation control elements (translation modules) (Fig. 1A). The different strains and their control elements are listed in table S1. The translation modules consist of natural (fbaA, gtlX, and tufA) or synthetic (fbaAhs and fbaAshort) translation initiation regions (TIRs), defined as the 5 untranslated region deprived of the first eight nucleotides. Our transcription modules contain natural promoters, defined as the 50 base pairs (bp) preceding the first transcribed nucleotide and including the 35 and 10 boxes. The transcription start site (TSS), i.e., the first transcribed nucleotide, is known to affect the efficiency of initiation, and the site of initiation can vary by a few bases between several initiation events (33). Therefore, we decided to extend our transcription module beyond the promoter and include the extended TSS (eTSS), defined as the first eight transcribed nucleotides (34). The different promoters and TIRs were chosen to ensure a wide range of expression on the basis of data from Nicolas et al. (35) and Borkowski et al. (36). We constructed 37 of the 40 designed strains. For the three remaining strains, repeated failures in the construction suggest that for some uncharacterized reasons, the designed sequences impose a strong burden to the cells. Further details on the design and construction of the library can be found in the Supplementary Materials.

(A) Synthetic sequences are made of a combination of eight transcription modules (promoters and eTSS) exhibiting different transcription strengths (yellow intensity) and five translation modules (TIRs) exhibiting different translation efficiencies (blue intensity). Combined modules are cloned upstream of the GFPmut3 coding sequence, resulting in a library of 40 synthetic sequences, which allow a wide range of GFPmut3 expression, that is representative of the natural range of protein expression in B. subtilis (fig. S2). (B and C) Mean protein abundance (B) and protein concentration noise strength (C) of all the strains of the library. To facilitate the interpretation, the protein concentration is expressed in number of proteins in 1 fl, which is the average cell volume. Therefore, the mean concentration corresponds to the mean number of proteins per cell (mean protein abundance). The noise strength is defined as the variance of the single-cell protein concentration divided by the mean. For each strain, at least two replicate experiments were performed. Each dot represents a single experiment. Experiments using the same strains are represented with vertically aligned dots of identical color. (D and E) The strains are ordered in a two-dimensional map according to their transcription (x axis) and translation (y axis) modules. Translation modules (1, fbaAhs; 2, fbaA; 3, fbaAshort; 4, gtlX; and 5, tufA) and transcription modules (1, ykwB; 2, yufK; 3, yqzM; 4, zwf; 5, ykpA; 6, fbaA; 7, rrnJP2; and 8, ylxM) are ordered according to their strength. The color of the pixels represents the log-transformed mean protein abundance (D) and log-transformed noise strength (E). White pixels correspond to the strains that could not be constructed or measured. Crossed-out pixels correspond to strains with an unexpected mean fluorescence, suggesting specific interactions between the transcription and translation modules.

For all the strains in the library, we quantified the fluorescence at the single-cell level using both epifluorescence microscopy and flow cytometry. Flow cytometry allows fast, high-throughput data acquisition but is less accurate and sensitive than fluorescence microscopy. In consequence, only 21 of the 37 strains of the library produced a quantifiable signal in cytometry. In contrast, the fluorescence of all the strains was quantified using microscopy, except the S27 strain, which had an unexpectedly low fluorescence that was indistinguishable from the natural autofluorescence of B. subtilis. In addition, for our analysis, the fluorescence signal has to be normalized by cell size to eliminate the variability coming from the cell cycle. Cell size can be directly measured from microscopy images, whereas it can only be coarsely estimated from cytometry measurements on the basis of the forward scatter signal (FSC). Therefore, we focused here on microscopy measurements and used flow cytometry as a control, ensuring that our conclusions are supported by data obtained using two independent measurement methods. The mean fluorescence and noise strength of the strains measured using cytometry are in agreement with microscopy measurements (fig. S1), and all the conclusions presented thereafter are supported by both cytometry and microscopy measurements.

Translation and transcription rates can vary substantially with the rate of growth, in a way that is dependent on the sequences controlling expression (35, 36). As a consequence, to characterize gene expression noise in our library, the growth rate has to be reproducibly controlled between experiments. We therefore performed fluorescence measurements on cells that are in a steady state of balanced growth (37). More precisely, we plated diluted cell precultures on agarose pads and let single cells grow into microcolonies. We monitored microcolony growth, waited six to eight generations, allowing the growth rate to reach its steady-state value, and imaged ca. 30 microcolonies, in phase contrast and fluorescence. Analyzing microcolony growth rates, we found that their variations were limited (coefficient of variation, ~14%), were mainly due to interexperiment variability, and did not significantly affect the fluorescence measurements (text S1). Single cells within microcolonies were segmented from phase contrast images, and their fluorescence was measured and normalized by the segmented cell area. Fluorescence values were then normalized to actual protein concentrations based on fluorescence measurements performed on strains with known protein abundances (see Materials and Methods).

For each strain, we performed at least two replicate experiments. Figure 1 (B and C) shows that fluorescence measurements were reproducible between replicate experiments. This can be more quantitatively addressed using a partition of variance such as that performed in a one-way analysis of variance (ANOVA). This analysis shows that for both mean fluorescence and noise strength, >95% of the variance observed between experiments is explained by the different strains used, whereas the residual variance corresponding to replicate experiments is <5% (see text S1).

As shown in Fig. 1B, the library covers a 200-fold range of expression levels, which is representative of natural expression levels in B. subtilis (see fig. S2). Figure 1D shows the mean fluorescence of all the strains, ordered along the x axis according to the strength of their transcription module and along the y axis according to the strength of their translation module. As expected, the mean expression strongly depends on both the transcription and translation modules. Figure 1D also shows that, except for three strains that exhibit unexpected behaviors (S04, S07, and S27; crossed-out pixels in Fig. 1D), the transcription modules can be ranked according to their strength independently of the translation module and reciprocally, suggesting that transcription and translation modules generally have independent effects on mean expression. For S04, S07, and S27, the mean fluorescence is not coherent with the rankings of the modules, suggesting a specific interaction, such as an effect of the eTSS on mRNA folding. In the simple two-stage model of gene expression, the mean expression of a gene is proportional to the product of the transcription rate and the translation rate. Therefore, according to this model, transcription and translation modules are expected to have independent effects on the log-transformed mean expression. This assumption can be tested using a partition of variance, such as that performed in a two-way ANOVA. Using two-way ANOVA, the variance can be partitioned between the independent effects of the two factors, as well as an interaction term and a residual unexplained variance. The underlying model implies additive effects of the two factors, so here, we performed the ANOVA on the log-transformed mean fluorescence, using transcription and translation modules as factors. This analysis demonstrated that >90% of the total variance is explained by the independent effects of the transcription and translation modules (text S2). Our microscopy dataset contains only two replicate experiments per strain, which limits the precision of the ANOVA. Therefore, to further check the independence of the effects of the transcription and translation modules, we also measured the fluorescence of all the strains at the population level during exponential growth in 96-well microplates, performing five independent measurements for each strain. A two-way ANOVA confirmed the results obtained with our microscopy data (text S2). Therefore, the effects of transcription and translation modules on mean expression are mostly independent, except on some rare instances where substantial interaction can occur, such as for the strains S04, S07, and S27, which were not used in the following analyses. These results are in agreement with previous results obtained in E. coli with a similar approach by Mutalik et al. (38) and with a larger library by Kosuri et al. (39).

We then analyzed how expression variability depends on the transcription and translation modules. Here, we used the Fano factor or noise strength, i.e., the variance divided by the mean, as a measure of expression variability. In the work of Ozbudak et al. (9), the noise strength was found to vary substantially with the translation rate, whereas the effect of the transcription rate was much weaker, as predicted by the two-stage model. In contrast, Fig. 1E shows that in our library, the noise strength depends substantially on both the transcription and translation modules.

We analyzed the dependence of the noise strength on the translation module for each transcription module, as shown in Fig. 2 and fig. S3. For each transcription module, increases linearly with the mean expression when the translation module changes, which is in agreement with previous work in B. subtilis (9). The two-stage model predicts that increases linearly with the rate of translation and therefore increases linearly with ( ab, with a being the transcription rate and b being the translation rate) with a slope that depends on the strength of the transcription modules, i.e., the slope should be smaller for modules eliciting a higher transcription rate. We performed linear regressions of versus when the translation module is changed for each transcription module. The estimated slopes are given in table S3. As predicted by the model, the slope decreases with the strength of the transcription module.

Each subplot corresponds to a group of strains with the same transcription module: (A) fbaA, strains S1 to S3 and S5; (B) rrnJP2, strains S7 to S9; (C) ykpA, strains S11 to S15; (D) ykwB, strains S16 to S20; (E) ylxM, strains S21 to S24; (F) yqzM, strains S26 and S30; (G) yufK, strains S31 to S35; and (H) zwf, strains S36 to S40. In each subplot, the different colors correspond to different translation modules (blue, fbaAhs; cyan, fbaA; green, fbaAshort; magenta, gtlX; and red, tufA). Black lines are linear regressions (parameters are given in table S3). To facilitate the interpretation, the protein concentration is expressed in number of proteins in 1 fl, which is the average cell volume. Therefore, the mean concentration corresponds to the mean number of proteins per cell (mean abundance).

Likewise, we analyzed the dependence of on the transcription module for each translation module. In contrast to previous experimental work in B. subtilis (9) and model predictions, we found that for all the translation modules, increases linearly with when the transcription rate changes. This is shown in Fig. 3 and fig. S4. We performed linear regressions and found that the slope is quite similar for all the translation modules (see table S4). The slopes are on the same order of magnitude than the slopes obtained when translation is modulated and transcription is constant (see tables S3 and S4). Consequently, for many strains in the library, increasing the mean expression by changing transcription or translation modules leads to similar noise strength (Fig. 4A).

Each subplot corresponds to a group of strains with the same translation module: (A) fbaA, (B) fbaAhs, (C) fbaAshort, (D) gtlX, and (E) tufA. In each subplot, the different colors correspond to different transcription modules (blue, yufK; cyan, yqzM; green, ykpA; yellow, zwf; magenta, ykwB; orange, fbaA; red, rrnJP2; and brown, ylxM). Black lines are linear regressions (parameters are given in table S4). To facilitate the interpretation, the protein concentration is expressed in number of proteins in 1 fl, which is the average cell volume. Therefore, the mean concentration corresponds to the mean number of proteins per cell (mean abundance).

To facilitate the interpretation, the protein concentration is expressed in number of proteins in 1 fl, which is the average cell volume. Therefore, the mean concentration corresponds to the mean number of proteins per cell (mean abundance). (A) The mean protein abundance is modulated by changing the transcription (red) or the translation (green) module. The green dots correspond to the strains with the ylxM transcription module (and different translation modules, strains S21 to S24), and the red diamonds corresponds to the strains with the fbaAshort translation module (and different transcription modules, strains S03, S08, S13, S18, S23, S33, S38, A1 to A7, and B1 to B7). The superimposed green dot and red diamond correspond to the S23 strain (transcription module, ylxM and translation module, fbaAshort). Straight lines are linear regressions. (B) The mean protein abundance is modulated by changing only the promoter. The red squares correspond to different strains with the same eTSS and translation module (strains S03 and A1 to A7), and the black straight line is a linear regression. (C) The mean protein abundance is modulated by changing either the promoter [red squares, strains S03 and A1 to A7 as in (B)], the eTSS (blue circles, strains S8 and B1 to B7), or both (green diamonds, strains S13, S18, S23, S33, and S38).

When the translation rate increases, increases with , with a slope that depends on the strength of the transcription module (table S3). In contrast, when the transcription rate increases, increases linearly with with a slope that is independent of the translation module, but the intercept depends on the strength of the translation module (table S4). These relations impose a mathematical relationship between and the rate of transcription (a) and translation (b) of the form = C1 + C2b + C3ab (Eq. 1) (see text S4 for details), with C1, C2, and C3 constants. In previous works, relations between and the rate of translation (b) were derived from a modeling approach on the basis of assumptions on the underlying biological mechanisms (8, 12, 13). In contrast, here, Eq. 1 is derived directly from the data, with no modeling assumptions. Equation 1 can be rewritten to show the dependence of on : = C1 + C2/a + C3. This equation shows that when the mean abundance is varied through the translation rate, the slope of versus (Stranslation) is the sum of a transcription-dependent term (C2/a) and a constant term (C3). This constant C3 is the slope of versus when the transcription rate varies (Stranscription). Therefore, if C2/a is small compared to C3, then modulating transcription or translation has a similar effect on noise (Stranslation ~ Stranscription). In contrast, if C2/a is large compared to C3, then translational bursting dominates and translation has a stronger impact than transcription on noise (Stranslation >> Stranscription). Thus, comparing C2/a and C3 allows defining a regime of weak transcription where translational bursting dominates noise production.

Comparing the slopes of Figs. 2 and 3 (see tables S3 and S4), we see that only the three weakest transcription modules of our library (ykwB, yufK, and yqzM) belong to this translational bursting regime. For these modules, C2/a is approximately twice as large as C3, i.e., Stranslation ~ 3.Stranscription. We analyzed genome-wide transcriptomic data from the work of Nicolas et al. (35) and found that only ca. 30% of B. subtilis proteome corresponds to a transcription rate weaker than the one of yqzM (text S5 and fig. S6) and should therefore belong to the translational bursting regime. On the basis of Eq. 1 and the genome-wide transcriptomic data, we can also compute a theoretical value for Stranslation for the whole proteome (text S5 and fig. S7). Although this approach is unlikely to give precise predictions at the single-gene level, it allows estimating the fraction of the proteome that is in the translational bursting regime. For instance, we estimated that Stranslation is 10-fold (respectively 2-fold) higher than Stranscription for only 1% (respectively 35%) of B. subtilis native promoters.

In the work of Ozbudak et al. (9), the transcription rate was modulated by using an inducible promoter. In contrast, we used different transcription modules, all leading to constitutive gene expression. As explained in the first section, we decided to consider the first eight transcribed nucleotides, namely, the eTSS, as part of our transcription modules because of its substantial effect on the transcription rate. However, these nucleotides are part of the mRNA sequence and therefore may also have an impact on its folding, degradation, and/or translation (40). Therefore, our unexpected results could stem from the design of the library, the different transcription modules potentially having an artifactual impact on translation through their different eTSS (40). To rule out any bias due to the effect of the eTSS on mRNA translation and degradation, we constructed seven new strains where only the promoter varies, while the eTSS and TIRs are identical (strains A1 to A7; see table S1). Figure 4B shows that in these strains, the noise strength also increases with the mean expression level. Therefore, the effect of the transcription modules on noise strength in the whole library is not due to a bias caused by eTSS modifications. We also constructed six strains that have identical TIRs and promoters but different eTSS regions (strains B1 to B7; see table S1). We found that changing the eTSS, the promoter, or both gives rise to a similar effect on noise strength. This is illustrated in Fig. 4C.

Equation 1 ( = C1 + C2b + C3ab), which is derived directly from the linear relations observed in Figs. 2 and 3 and describes the dependence of the noise strength on the transcription and translation rates, is reminiscent of the formula established by Taniguchi et al. (30) to take into account extrinsic noise. In the work of Taniguchi et al. (30), the two-stage model is generalized by introducing temporal fluctuations of the translation and transcription rates. The formula obtained for the noise strength is of the form of Eq. 1, with a and b being the average rates of transcription and translation and C1, C2, and C3 depending on the level of extrinsic noise. This suggests that our unexpected results could be due to a strong extrinsic noise component. In E. coli, the noise was shown to scale with protein abundance for very low expression levels and to reach a plateau when the mean abundance increases above ~10 proteins per cell (30). This plateau was suggested to be the consequence of extrinsic noise (30). Our data also show a plateau for the noise, which is reached when the mean abundance increases above ~100 proteins per average cell volume (Fig. 5A). This global analysis is therefore also in agreement with a strong extrinsic noise.

(A) The noise (squared coefficient of variation: CV2, y) of the protein concentration as a function of the mean protein abundance (x) for all the strains. Each blue circle corresponds to a single experiment with a single strain. The red line corresponds to a fit y = C/x for all the experiments for which x < 50 (left part of the graph). (B) The total noise (blue), extrinsic noise (green), and intrinsic noise (red; y) as a function of the mean (x), for the two-colored strains (same eTSS and translation module and different promoters). The red line is a fit y = k1/x + k2, as in (4). (C) The total (blue dots), extrinsic (green dots), and intrinsic (red dots) noise strength as a function of the mean, for the two-colored strains. Straight lines are linear regressions. To facilitate the interpretation, the protein concentration is expressed in number of proteins in 1 fl, which is the average cell volume. Therefore, the mean concentration corresponds to the mean number of proteins per cell (mean abundance).

To further assess the role of extrinsic noise, we used the dual-reporter method developed by Elowitz et al. (4). For the eight strains that have identical eTSS and translation module but variable promoters (strains S03 and A1 to A7; see table S1), we introduced the gene of the mKate2 red fluorescent protein into the genome, with the same control elements as for the GFPmut3 (table S2). The mKate2 gene was introduced directly downstream of the GFPmut3 gene, thus limiting difference in gene copy number during the cell cycle. Quantification of both red and green fluorescence in single cells showed that the expression of mKate2 and GFPmut3 are strongly correlated in all strains (Spearmans rank correlation between 0.6 and 0.9; P < 1010; see fig. S5). The noise and noise strength can be decomposed into their extrinsic and intrinsic components, as explicated by Elowitz et al. (4). The decomposition of noise shown in Fig. 5B shows that it is dominated by the extrinsic component, which accounts for ca. 60% of the noise at the lowest expression levels and up to ca. 90% at the highest expression levels. Figure 5C shows that the increase of noise strength when transcription increases can be fully explained by the strong extrinsic component, which increases with transcription rate.

Genome-wide analysis of transcription and translation levels in the yeast S. cerevisiae revealed that essential genes are more transcribed and less translated than nonessential genes with the same protein expression level (17). This was interpreted as the signature of a selection pressure toward noise reduction, which is likely to be stronger for essential genes. This conclusion relies on the assumption that tuning the relative levels of transcription and translation allows tuning the expression noise. In this work, we show that this strategy is less effective than previously thought in bacteria and only concerns very low expression levels for which intrinsic noise is stronger. Therefore, we investigated whether the different expression strategies observed for essential and nonessential genes in yeast also exist in B. subtilis. To that end, we performed a genome-wide analysis that allows comparing the levels of transcription and translation of essential and nonessential genes.

We used the transcriptomic and proteomic data presented by Borkowski et al. (36) and Goelzer et al. (41) and the list of essential genes from SubtiWiki (42). Protein abundance is, on average, higher for essential than nonessential genes. Therefore, to control for this effect, we grouped the genes according to their protein abundances. Then, for each group of similarly expressed genes, we divided the genes into three subgroups of identical size, according to their transcription rate: the third of the genes that have the highest transcription rate, the third that has the lowest transcription rate, and the remaining third. We then computed the number of essential genes in the two extreme subgroups (lowest and highest transcription rates), as performed by Fraser et al. (17). These subgroups a priori contain essential and nonessential genes, and if essential and nonessential genes have similar expression strategies, then the number of essential genes in the different subgroups should be similar. In S. cerevisiae, the number of essential genes was shown to be 2- to 10-fold higher in the high-transcription subgroups for all the protein expression levels (17). In contrast, Fig. 6 shows that in B. subtilis, the number of essential genes in the highly transcribed (red) and weakly transcribed (blue) subgroups are not markedly different. Thus, B. subtilis does not use markedly different expression strategies for essential and nonessential genes. However, note that there is a significant enrichment of essential genes in the high-transcription subgroups for genes with low expression levels (typically <300 proteins per cell). Note that the genome-wide data used here contain genes that are transcriptionally regulated, and low expression levels may correspond to transcriptional repression. However, removing genes that are likely to be transcriptionally regulated does not change the results shown in Fig. 6A (as Fig. 6A, where all genes are included, is similar to fig. S8, where regulated genes are excluded), suggesting that the different expression strategies of essential and nonessential genes at low expression levels are not due to different transcriptional regulation. In contrast, it may reflect a selection pressure for noise reduction of poorly expressed essential genes.

(A to C) Genes are grouped according to the protein abundance, and each group is divided into three subgroups of identical size according to the transcription rate. The subgroups are formed with the third of the genes that have the highest transcription rate, the third that has the lowest transcription rate, and the remaining third. Then, the number of essential genes in each subgroup is computed. Red circles, number of essential genes in the high-transcription subgroup; blue circles, number of essential genes in the low-transcription subgroup. The filled circles indicate significant differences based on Fishers exact test (P < 0.05). (A) The analysis is performed on all genes in the genome. (B) The analysis is performed on a subset of genes that are weakly transcribed (less transcribed than yqzM). (C) The analysis is performed on the rest of the genes (i.e., those more transcribed than yqzM). In (A) to (C), the procedure to group the genes of identical protein abundance is not a simple binning and creates groups of genes whose levels of expression are not significantly based on an ANOVA (see Materials and Methods for details). The different groups therefore do not contain the same number of genes, and the number of groups is different in (A) to (C).

As we presented above, translational bursting dominates noise production only in a regime of weak transcription, which represents only a small fraction of the natural proteome. For instance, we showed that only ca. 30% of natural promoters should lead to Stranslation > 3 Stranscription. Restricting the analysis shown in Fig. 6A to this group of weakly transcribed genes gives identical results, as shown in Fig. 6B. In contrast, if we use the 70% most transcribed genes, then the effect at low expression levels disappears and no difference can be detected between essential and nonessential genes (Fig. 6C).

It is generally assumed that translational bursting is the dominant source of noise in prokaryotic gene expression and that translation therefore has a stronger impact on noise than transcription. In this work, we show that translational bursting dominates noise production only in a regime of weak transcription, which corresponds to a small fraction of the natural transcription range of bacteria. In contrast, for most of the natural expression range, translation and transcription modulations have similar effects on noise. We show here that this phenomenon can be explained by the prevalence of extrinsic noise.

As previously demonstrated, very weak promoters associated with strong translation control elements can promote noisy expression (43). Such an expression strategy could therefore be selected for by evolution or implemented in synthetic biology approaches to increase population diversity and/or implement bet-hedging strategies. However, our results show that for most of B. subtilis natural transcription range, noise cannot be tuned independently of mean abundance by varying the ratio of transcription and translation rates. This strategy is therefore less general than previously thought (6, 8, 9), which has important implications both for synthetic biology and engineering and for genome evolution. In bioengineering, the control of gene expression noise is an essential component of system design. Until now, strong promoters and weak RBS (ribosome binding site) sequences were favored when assembling robust, i.e., low-noise gene circuits (27). Our results indicate that the future of bioengineering will require the elaboration of a novel framework for engineering noise in various living systems.

Our analysis of genome-wide transcriptomic and proteomic data in B. subtilis shows that at low expression levels, essential genes are transcribed more and translated less than nonessential genes of identical protein abundance. As previously proposed for yeasts, this difference may reflect a selection pressure for noise reduction, which is assumed to be stronger for essential genes. Notably, the difference in expression strategies between essential and nonessential genes is restricted to a fraction of the genome, which corresponds to weakly transcribed genes. Therefore, our experimental results and our genome-wide analysis offer a coherent picture. In the weak transcription regime, noise can be tuned independently of mean abundance by varying the ratio of transcription and translation, leading to a selection force acting on genome evolution. However, this force is negligible in the evolution of most of the genome.

Translational bursting is expected to have a different impact on noise for different functional categories of genes. In particular, transcription factors are known to be present at low copy number in the cell compared to enzymes or structural proteins (44). In addition, among transcription factors, those that act specifically on a few genes, such as E. coli Lac repressor, are usually present at lower concentrations than global regulators that act on many genes. Low copy number transcription factors are therefore expected to be in the weak transcription regime, where noise can be tuned independently of mean expression. This noise tuning can lead to strong phenotypic effects and provide a basis for specific bet-hedging strategies (22, 43). In contrast, for enzymes that are present in high copy number, expression noise cannot be tuned by varying the ratio of transcription and translation. The cell therefore often implements alternative strategies to minimize the fluctuations in biochemical pathways, such as the negative regulation of a biosynthesis pathway by its end product.

The existence of two regimes of noise production, dominated either by translational bursting or extrinsic noise depending on the strength of transcription, is likely to hold for other organisms. Different organisms may be in different regimes depending on their natural transcription range and the source and intensity of the extrinsic noise. In yeasts, markedly different expression strategies between essential and nonessential genes suggest that noise can generally be tuned by varying the ratio of transcription and translation, thus suggesting that at the whole-genome scale, noise production is mainly in the regime where translational bursting prevails. This pattern may be related to the level of extrinsic noise, which was reported to be lower in yeasts than in bacteria (28, 29). Note that in the case of transcriptional bursting, i.e., when promoters can stochastically switch between inactive and active states, different regimes of noise production can also be defined, by comparing the transcription rate to the activation and inactivation rates of the promoter (11). Therefore, both extrinsic noise and transcriptional bursting can prevail over translational bursting, restricting the regime in which noise can be tuned independently of the mean abundance by varying the ratio of transcription and translation.

E. coli Mach1T1 and TG1 were used for plasmid construction and amplification, respectively, using standard techniques (45). B. subtilis strains were obtained by integration of the plasmid by single crossing-over in a tryptophan prototrophic 168 strain (BSB168) (46), using standard procedures.

When required, DNA fragments were purified using the QIAquick PCR Purification Kit or QIAquick Gel Extraction Kit (QIAGEN, Hilden, Germany). Plasmids were purified from E. coli cultures using the QIAprep Spin Miniprep Kit (QIAGEN).

The vectors used to generate the strain collection were made as follows: The vector pBaSysBioII (46) was linearized by Eco RV and recircularized by ligation of a 714-bp PCR (polymerase chain reaction) product to obtain the plasmid PL1. The PCR fragment was obtained by amplification of B. subtilis chromosomal DNA between the coordinates 213.017 and 213.757 according to the version AL009126 of the complete genome of B. subtilis deposited in GenBank.

The synthetic sequences used to control expression of GFPmut3 in the strains S01 to S40 (table S1) have been chemically synthesized by GeneArt. Briefly, each of the synthetic sequence is made of the association of a given promoter, an eTSS, and a TIR. These DNA sequences are preceded by a 29-bp sequence identical to the 29 bp upstream of the promoter PfbaA in the original PL1 and followed by 29 bp identical to the first 29 bp of the GFPmut3 coding sequence.

Plasmids PL1S01 to PL1S40 had been built as follows: The plasmid PL1 was PCR-amplified using primers P-PS-AM and P-PS-AV, resulting in a linear DNA sequence of 5243 bp made of the whole PL1 plasmid devoid of any promoter and RBS upstream of the GFPmut3 coding sequence (CDS). Synthetic sequences were PCR-amplified using the universal primers PS-F and PS-R, purified, and cloned in the plasmid by Gibson assembly using a NEBuilder HiFi DNA Assembly kit according to the manufacturers instructions (New England Biolabs, Ipswich, MA, USA). Each Gibson assembly mix has been used to transform chemically competent Mach1T1 E. coli cells. Once sequenced (GATC Biotech, Cologne, Germany), recombinant plasmids were transformed and multiplied in chemically competent TG1 cells before transformation in BSB168.

The following protocol is used to ensure a steady state of balanced growth. All incubation steps are performed at 37C under agitation. Cultures are inoculated in LB supplemented with spectinomycin (100 g/ml) and incubated overnight. They are then diluted 100-fold in LB, incubated for 2 hours, and then diluted 50-fold in S Medium [0.2% (NH4)2SO4, 1.4% K2HPO4, 0.6% KH2PO4, 0.1% sodium citrate, 0.0096% MgSO4, 104% MnSO4, 0.5% glucose, and 0.00135% FeCl3] and incubated for 2 hours. The culture is then diluted 8-fold in S medium, incubated for 3 hours, diluted again 70,000-fold in S medium, and incubated until the optical density at 600 nm reaches 0.2. The culture is then analyzed by flow cytometry and/or fluorescence microscopy.

Single-cell fluorescence, FSC, and side scatter measurements were carried out on a Becton Dickinson FACSCalibur flow cytometer, equipped with a 488-nm excitation laser and a 530/30-nm emission filter, and controlled by the CellQuest software. For all the strains, measurements were performed with the same laser power and voltage settings. The exponentially growing cultures were diluted 40-fold, and measurements were performed on 104 to 105 cells.

Microcolony growth monitoring and single-cell fluorescence measurements were performed using an inverted DeltaVision Elite microscope equipped with the Ultimate Focus system for automatic focalization, a 100 oil immersion objective (numerical aperture 1.4), a temperature-controlled chamber (37C), and the DV Elite sCMOS Camera. Bright-field illumination was provided by a white light-emitting diode (LED), and fluorescence illumination was provided by the DV Light Solid State Illuminator 7 Colors (475-nm LED for GFP and 575-nm LED for mKate2). Our microscope can perform two different illumination techniques: Khler illumination and critical illumination. We used critical illumination to improve evenness of illumination.

A liquid solution of 1.5% high-resolution low-gelling temperature agarose (Sigma-Aldrich) in S medium is prepared. To that end, agarose is first dissolved in water, heated, and allowed to cool down to 50C. The components of the S medium are then added to the agarose solution. A Gene Frame (125 l, 1.7 cm by 2.8 cm; Thermo Fisher Scientific) is stuck on a clean glass slide (Knittel Glass; 76 mm by 26 mm); the resulting cavity is filled with S-agarose, covered with a microscope slide, and cooled for 1 hour at 4C. Then, the microscope slide is removed, and stripes of S-agarose are removed using a surgical scalpel to leave three small stripes of agarose (~4 mm wide, with ~4 mm spacing), separated by air cavities ensuring oxygenation. Three different strains are then loaded on the three agarose stripes. To that end, the exponentially growing cultures are diluted 300-fold, and cca. 2 ml is deposited on each agarose stripe. Once the liquid is absorbed, the cavity is sealed with a clean coverslip (Knittel Glass Cover Slips; 24 mm by 60 mm), and the slide is placed in the temperature-controlled chamber set at 37C for 1 hour before acquisition begins.

We first follow the growth of microcolonies from single cells using phase contrast microscopy. Images are acquired using 50-ms exposure with 32% of the maximum intensity of the white LED. For each strain, we image ~30 microcolonies, every 5 or 10 min, for cca. 4 hours. After 4 hours of growth, the cells are in a steady state of growth, and the microcolonies are still in monolayers. We then image ca. 30 microcolonies, using both phase contrast and fluorescence. Depending on their fluorescence levels, the strains are imaged with different illumination intensities and/or exposure times.

To convert the fluorescence levels into protein concentrations, we quantified the fluorescence of two B. subtilis strains that express GFPmut3 and for which the concentration of proteins was previously quantified by two-photon fluorescence fluctuation microscopy (47). More precisely, we used two strains where GFPmut3 is under the control of the gapB or the cggR promoter, and we measured the fluorescence during exponential growth in 96-well microplates in S medium with glucose or malate as carbon sources, leading to different induction levels of the gapB or cggR promoters [see (47)]. We simultaneously measured the fluorescence of the S5, S9, and S13 strains in glucose-S medium, to allow determining the average concentration of proteins for those strains. The single-cell fluorescence data are then normalized accordingly for the whole library.

The fluorescence images are first corrected for inhomogeneous illumination. To estimate the illumination profile [b(x,y): the illumination intensity at (x,y) coordinate], we averaged ~40 images of agarose pads supplemented with fluorescein. For an image I0(x,y), we perform the following normalization to get the corrected image I1(x,y): I1(x,y) = I0(x,y) /b(x,y), where is the mean intensity averaged over every pixel. We also correct for the autofluorescence of the agarose gel by subtracting to the fluorescence image the average background intensity (pixels outside of the microcolony). We also normalize the fluorescence signal by the excitation energy to take into account the different illumination settings used for different strains. The corrected images are then analyzed using Schnitzcells software (48). Bacteria are segmented using the phase contrast images, and their fluorescence intensity is measured, i.e., the total fluorescence of the cell normalized by the cell area.

All data analysis is performed using MATLAB. One-way and two-way ANOVAs are performed using MATLABs functions anova1 and anova2.

For both microscopy and flow cytometry data, autofluorescence was estimated from measurements of the wild-type BSB168 strain, which does not contain any fluorescent protein. The single-cell fluorescence of cells expressing GFP and/or mKate2 is the sum of the contribution from the fluorescent proteins and the autofluorescence. Therefore, to reflect only the number of fluorescent proteins, the mean fluorescence is corrected by subtracting the mean autofluorescence. The single-cell autofluorescence is assumed to be independent of the number of fluorescent proteins in cells expressing GFP and/or mKate2. Therefore, the variance of the fluorescence can be corrected by subtracting the variance of the autofluorescence.

Analysis of the microscopy data shows that the autofluorescence is Gaussian. In the flow cytometry data, the distribution of autofluorescence is truncated on the left of a threshold that corresponds to the sensitivity of the cytometer. We therefore reconstruct the whole distribution as follows: The sensitivity threshold is lower than the mode of the distribution (i.e., the maximum of the density). Therefore, the right half of the distribution can be estimated. The whole Gaussian distribution is then reconstructed by symmetry, and the average and variance can be estimated.

For single-cell fluorescence measurements with the flow cytometer, we eliminated all the strains for which the fluorescence distribution was truncated by the sensitivity threshold. To reduce the fluctuations originating from cell size variations in the cytometry data, we kept only the cells whose FSC signal was within 3% of the mode of the FSC signal distribution.

The transcriptomic and proteomic data are taken from the works of Borkowski et al. (36) and Goelzer et al. (41), respectively, and the list of essential genes is taken from SubtiWiki (42). For each gene, the dataset contains several independent proteomic measures (up to nine replicates) and several independent transcriptomic measures (up to four replicates). Genes were binned according to their protein expression as follows: First, protein expression was estimated for each gene as the average of the proteomic replicates, and the genes were ranked according to this averaged measure. Then, we use all the replicates to take into account the level of confidence of the proteomic measure for each gene and to group the genes whose levels of expression are not significantly different. Starting with the first gene, we add the next genes one by one, performing a one-way ANOVA at each step. If the P value of the ANOVA is larger than a fixed threshold (0.05), then the gene is added to the group. Otherwise, it is used to start a new group, where genes are added one by one similarly. In contrast to a simple binning, this procedure takes into account the level of confidence of the measurements and produces groups of genes whose levels of expression are not significantly different.

Acknowledgments: We thank M. Calabre (Micalis, Jouy-en-Josas, France) for technical assistance in constructing the library. We thank A. Amir and J. Lin for useful comments on the manuscript. Funding: This work was supported by the French National Research Agency (ANR-18-CE44-0003) and the European Commission (FP7-244093). A.D. acknowledges a 3-year Ph.D. grant from the Interface Pour le Vivant(IPV) doctoral program of Sorbonne Universit. Author contributions: L.R., M.J., J.R., and S.A. conceived the project and designed the experimental plan. L.R. and J.R. designed the experimental setup. A.D. performed the experiments. V.S., M.J., and S.A. designed the strain library. V.S. constructed the library. A.D. and L.R. analyzed the data. L.R. wrote the manuscript with contributions from all authors. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors.

Excerpt from:
Extrinsic noise prevents the independent tuning of gene expression noise and protein mean abundance in bacteria - Science Advances

Photo: Red poppy, Auckland Botanic Gardens, Auckland, New Zealand, by Sandy Millar via Unsplash.

As John West noted here last week, the Journal of Theoretical Biology has published an explicitly pro-intelligent design article, Using statistical methods to model the fine-tuning of molecular machines and systems. Lets take a closer look at the contents. The paper is math-heavy, discussing statistical models of making inferences, but it is also groundbreaking for this crucial reason: it considers and proposes intelligent design, by name, as a viable explanation for the origin of fine-tuning in biology. This is a major breakthrough for science, but also for freedom of speech. If the paper is any indication, appearing as it does in a prominent peer-reviewed journal, some of the suffocating constraints on ID advocacy may be coming off.

The authors are Steinar Thorvaldsen, a professor of information science at the University of Troms in Norway, and Ola Hssjer, a professor of mathematical statistics at Stockholm University. The paper, which is open access, begins by noting that while fine-tuning is widely discussed in physics, it needs to be considered more in the context of biology:

Fine-tuning has received much attention in physics, and it states that the fundamental constants of physics are finely tuned to precise values for a rich chemistry and life permittance. It has not yet been applied in a broad manner to molecular biology.

The authors explain the papers main thrust:

However, in this paper we argue that biological systems present fine-tuning at different levels, e.g. functional proteins, complex biochemical machines in living cells, and cellular networks. This paper describes molecular fine-tuning, how it can be used in biology, and how it challenges conventional Darwinian thinking. We also discuss the statistical methods underpinning finetuning and present a framework for such analysis.

They explain how fine-tuning is defined. The definition is essentially equivalent to specified complexity:

We define fine-tuning as an object with two properties: it must a) be unlikely to have occurred by chance, under the relevant probability distribution (i.e. complex), and b) conform to an independent or detached specification (i.e. specific).

They then introduce the concept of design, and explain how humans are innately able to recognize it:

A design is a specification or plan for the construction of an object or system, or the result of that specification or plan in the form of a product. The very term design is from the Medieval Latin word designare (denoting mark out, point out, choose); from de (out) and signum (identifying mark, sign). Hence, a public notice that advertises something or gives information. The design usually has to satisfy certain goals and constraints. It is also expected to interact with a certain environment, and thus be realized in the physical world. Humans have a powerful intuitive understanding of design that precedes modern science. Our common intuitions invariably begin with recognizing a pattern as a mark of design. The problem has been that our intuitions about design have been unrefined and pre-theoretical. For this reason, it is relevant to ask ourselves whether it is possible to turn the tables on this disparity and place those rough and pre-theoretical intuitions on a firm scientific foundation.

That last sentence is key: the purpose is to understand if there is a scientific method by which design can be inferred. They propose that design can be identified by uncovering fine-tuning. The paper explicates statistical methods for understanding fine-tuning, which they argue reflects design:

Fine-tuning and design are related entities. Fine-tuning is a bottom-up method, while design is more like a top-down approach. Hence, we focus on the topic of fine-tuning in the present paper and address the following questions: Is it possible to recognize fine-tuning in biological systems at the levels of functional proteins, protein groups and cellular networks? Can fine-tuning in molecular biology be formulated using state of the art statistical methods, or are the arguments just in the eyes of the beholder?

They cite the work of multiple leading theorists in the ID research community.

They return to physics and the anthropic principle, the idea that the laws of nature are precisely suited for life:

Suppose the laws of physics had been a bit different from what they actually are, what would the consequences be? (Davies, 2006). The chances that the universe should be life permitting are so infinitesimal as to be incomprehensible and incalculable. The finely tuned universe is like a panel that controls the parameters of the universe with about 100 knobs that can be set to certain values. If you turn any knob just a little to the right or to the left, the result is either a universe that is inhospitable to life or no universe at all. If the Big Bang had been just slightly stronger or weaker, matter would not have condensed, and life never would have existed. The odds against our universe developing were enormous and yet here we are, a point that equates with religious implications

However, rather than getting into religion, they apply statistics to consider the possibility of design as an explanation for the fine-tuning of the universe. They cite ID theorist William Dembski:

William Dembski regards the fine-tuning argument as suggestive, as pointers to underlying design. We may describe this inference as abductive reasoning or inference to the best explanation. This reasoning yields a plausible conclusion that is relatively likely to be true, compared to competing hypotheses, given our background knowledge. In the case of fine-tuning of our cosmos, design is considered to be a better explanation than a set of multi-universes that lacks any empirical or historical evidence.

The article offers additional reasons why the multiverse is an unsatisfying explanation for fine-tuning namely that multiverse hypotheses do not predict fine-tuning for this particular universe any better than a single universe hypothesis and we should prefer those theories which best predict (for this or any universe) the phenomena we observe in our universe.

The paper reviews the lines of evidence for fine-tuning in biology, including information, irreducible complexity, protein evolution, and the waiting-timeproblem. Along the way it considers the arguments of many ID theorists, starting with a short review showing how the literature uses words such as sequence code, information, and machine to describe lifes complexity:

One of the surprising discoveries of modern biology has been that the cell operates in a manner similar to modern technology, while biological information is organized in a manner similar to plain text. Words and terms like sequence code, and information, and machine have proven very useful in describing and understanding molecular biology (Wills, 2016). The basic building blocks of life are proteins, long chain-like molecules consisting of varied combinations of 20 different amino acids. Complex biochemical machines are usually composed of many proteins, each folded together and configured in a unique 3D structure dependent upon the exact sequence of the amino acids within the chain. Proteins employ a wide variety of folds to perform their biological function, and each protein has a highly specified shape with some minor variations.

The paper cites and reviews the work of Michael Behe, Douglas Axe, Stephen Meyer, and Gnter Bechly. Some of these discussions are quite long and extensive. First, the article contains a lucid explanation of irreducible complexity and the work of Michael Behe:

Michael Behe and others presented ideas of design in molecular biology, and published evidence of irreducibly complex biochemical machines in living cells. In his argument, some parts of the complex systems found in biology are exceedingly important and do affect the overall function of their mechanism. The fine-tuning can be outlined through the vital and interacting parts of living organisms. In Darwins Black Box (Behe, 1996), Behe exemplified systems, like the flagellum bacteria use to swim and the blood-clotting cascade, that he called irreducibly complex, configured as a remarkable teamwork of several (often dozen or more) interacting proteins. Is it possible on an incremental model that such a system could evolve for something that does not yet exist? Many biological systems do not appear to have a functional viable predecessor from which they could have evolved stepwise, and the occurrence in one leap by chance is extremely small. To rephrase the first man on the moon: Thats no small steps of proteins, no giant leap for biology.

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A Behe-system of irreducible complexity was mentioned in Section 3. It is composed of several well-matched, interacting modules that contribute to the basic function, wherein the removal of any one of the modules causes the system to effectively cease functioning. Behe does not ignore the role of the laws of nature. Biology allows for changes and evolutionary modifications. Evolution is there, irreducible design is there, and they are both observed. The laws of nature can organize matter and force it to change. Behes point is that there are some irreducibly complex systems that cannot be produced by the laws of nature:

If a biological structure can be explained in terms of those natural laws [reproduction, mutation and natural selection] then we cannot conclude that it was designed. . . however, I have shown why many biochemical systems cannot be built up by natural selection working on mutations: no direct, gradual route exist to these irreducible complex systems, and the laws of chemistry work strongly against the undirected development of the biochemical systems that make molecules such as AMP1 (Behe, 1996, p. 203).

Then, even if the natural laws work against the development of these irreducible complexities, they still exist. The strong synergy within the protein complex makes it irreducible to an incremental process. They are rather to be acknowledged as finetuned initial conditions of the constituting protein sequences. These structures are biological examples of nano-engineering that surpass anything human engineers have created. Such systems pose a serious challenge to a Darwinian account of evolution, since irreducibly complex systems have no direct series of selectable intermediates, and in addition, as we saw in Section 4.1, each module (protein) is of low probability by itself.

The article also reviews the peer-reviewed research of protein scientist Douglas Axe, as well as his 2016 book Undeniable, on the evolvability of protein folds:

An important goal is to obtain an estimate of the overall prevalence of sequences adopting functional protein folds, i.e. the right folded structure, with the correct dynamics and a precise active site for its specific function. Douglas Axe worked on this question at the Medical Research Council Centre in Cambridge. The experiments he performed showed a prevalence between 1 in 1050 to 1 in 1074 of protein sequences forming a working domain-sized fold of 150 amino acids (Axe, 2004). Hence, functional proteins require highly organised sequences, as illustrated in Fig. 2. Though proteins tolerate a range of possible amino acids at some positions in the sequence, a random process producing amino-acid chains of this length would stumble onto a functional protein only about one in every 1050 to 1074 attempts due to genetic variation. This empirical result is quite analog to the inference from fine-tuned physics.

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The search space turns out to be too impossibly vast for blind selection to have even a slight chance of success. The contrasting view is innovations based on ingenuity, cleverness and intelligence. An element of this is what Axe calls functional coherence, which always involves hierarchical planning, hence is a product of finetuning. He concludes: Functional coherence makes accidental invention fantastically improbable and therefore physically impossible (Axe, 2016, p. 160).

They conclude that the literature shows the probability of finding a functional protein in sequence space can vary broadly, but commonly remains far beyond the reach of Darwinian processes (Axe, 2010a).

Citing the work of Gnter Bechly and Stephen Meyer, the paper also reviews the question of whether sufficient time is allowed by the fossil record for complex systems to arise via Darwinian mechanisms. This is known as the waiting-time problem:

Achieving fine-tuning in a conventional Darwinian model: The waiting time problem

In this section we will elaborate further on the connection between the probability of an event and the time available for that event to happen. In the context of living systems, we need to ask the question whether conventional Darwinian mechanisms have the ability to achieve fine-tuning during a prescribed period of time. This is of interest in order to correctly interpret the fossil record, which is often interpreted as having long periods of stasis interrupted by very sudden abrupt changes (Bechly and Meyer, 2017). Examples of such sudden changes include the origin of photosynthesis, the Cambrian explosions, the evolution of complex eyes and the evolution of animal flight. The accompanying genetic changes are believed to have happen very rapidly, at least on a macroevolutionary timescale, during a time period of length t. In order to test whether this is possible, a mathematical model is needed in order to estimate the prevalence P(A) of the event A that the required genetic changes in a species take place within a time window of length t.

Throughout the discussions are multiple citations of BIO-Complexity, a journal dedicated to investigating the scientific evidence for intelligent design.

Lastly, the authors consider intelligent design as a possible explanation of biological fine-tuning, citing heavily the work of William Dembski, Winston Ewert, Robert J. Marks, and other ID theorists:

Intelligent Design (ID) has gained a lot of interest and attention in recent years, mainly in USA, by creating public attention as well as triggering vivid discussions in the scientific and public world. ID aims to adhere to the same standards of rational investigation as other scientific and philosophical enterprises, and it is subject to the same methods of evaluation and critique. ID has been criticized, both for its underlying logic and for its various formulations (Olofsson, 2008; Sarkar, 2011).

William Dembski originally proposed what he called an explanatory filter for distinguishing between events due to chance, lawful regularity or design (Dembski, 1998). Viewed on a sufficiently abstract level, its logics is based on well-established principles and techniques from the theory of statistical hypothesis testing. However, it is hard to apply to many interesting biological applications or contexts, because a huge number of potential but unknown scenarios may exist, which makes it difficult to phrase a null hypothesis for a statistical test (Wilkins and Elsberry, 2001; Olofsson, 2008).

The re-formulated version of a complexity measure published by Dembski and his coworkers is named Algorithmic Specified Complexity (ASC) (Ewert et al., 2013; 2014). ACS incorporates both Shannon and Kolmogorov complexity measures, and it quantifies the degree to which an event is improbable and follows a pattern. Kolmogorov complexity is related to compression of data (and hence patterns), but suffers from the property of being unknowable as there is no general method to compute it. However, it is possible to give upper bounds for the Kolmogorov complexity, and consequently ASC can be bounded without being computed exactly. ASC is based on context and is measured in bits. The same authors have applied this method to natural language, random noise, folding of proteins, images etc (Marks et al., 2017).

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The laws, constants, and primordial initial conditions of nature present the flow of nature. These purely natural objects discovered in recent years show the appearance of being deliberately fine-tuned. Functional proteins, molecular machines and cellular networks are both unlikely when viewed as outcomes of a stochastic model, with a relevant probability distribution (having a small P(A)), and at the same time they conform to an independent or detached specification (the set A being defined in terms of specificity). These results are important and deduced from central phenomena of basic science. In both physics and molecular biology, fine-tuning emerges as a uniting principle and synthesis an interesting observation by itself.

In this paper we have argued that a statistical analysis of fine-tuning is a useful and consistent approach to model some of the categories of design: irreducible complexity (Michael Behe), and specified complexity (William Dembski). As mentioned in Section 1, this approach requires a) that a probability distribution for the set of possible outcomes is introduced, and b) that a set A of fine-tuned events or more generally a specificity function f is defined. Here b) requires some apriori understanding of what fine-tuning means, for each type of application, whereas a) requires a naturalistic model for how the observed structures would have been produced by chance. The mathematical properties of such a model depend on the type of data that is analyzed. Typically a stochastic process should be used that models a dynamic feature such as stellar, chemical or biological (Darwinian) evolution. In the simplest case the state space of such a stochastic process is a scalar (one nucleotide or amino acid), a vector (a DNA or amino acid string) or a graph (protein complexes or cellular networks).

A major conclusion of our work is that fine-tuning is a clear feature of biological systems. Indeed, fine-tuning is even more extreme in biological systems than in inorganic systems. It is detectable within the realm of scientific methodology. Biology is inherently more complicated than the large-scale universe and so fine-tuning is even more a feature. Still more work remains in order to analyze more complicated data structures, using more sophisticated empirical criteria. Typically, such criteria correspond to a specificity function f that not only is a helpful abstraction of an underlying pattern, such as biological fitness. One rather needs a specificity function that, although of non-physical origin, can be quantified and measured empirically in terms of physical properties such as functionality. In the long term, these criteria are necessary to make the explanations both scientifically and philosophically legitimate. However, we have enough evidence to demonstrate that fine-tuning and design deserve attention in the scientific community as a conceptual tool for investigating and understanding the natural world. The main agenda is to explore some fascinating possibilities for science and create room for new ideas and explorations. Biologists need richer conceptual resources than the physical sciences until now have been able to initiate, in terms of complex structures having non-physical information as input (Ratzsch, 2010). Yet researchers have more work to do in order to establish fine-tuning as a sustainable and fully testable scientific hypothesis, and ultimately a Design Science.

This is a significant development. The article gives the arguments of intelligent design theorists a major hearing in a mainstream scientific journal. And dont miss the purpose of the article, which is stated in its final sentence to work towards establish[ing] fine-tuning as a sustainable and fully testable scientific hypothesis, and ultimately a Design Science. The authors present compelling arguments that biological fine-tuning cannot arise via unguided Darwinian mechanisms. Some explanation is needed to account for why biological systems show the appearance of being deliberately fine-tuned. Despite the noise that often surrounds this debate, for ID arguments to receive such a thoughtful and positive treatment in a prominent journal is itself convincing evidence that ID has intellectual merit. Claims of IDs critics notwithstanding, design science is being taken seriously by scientists.

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Breakout Paper in Journal of Theoretical Biology Explicitly Supports Intelligent Design - Discovery Institute

Abstract

RNAs form critical components of biological processes implicated in human diseases, making them attractive for small-molecule therapeutics. Expanding the sites accessible to nuclear magnetic resonance (NMR) spectroscopy will provide atomic-level insights into RNA interactions. Here, we present an efficient strategy to introduce 19F-13C spin pairs into RNA by using a 5-fluorouridine-5-triphosphate and T7 RNA polymerasebased in vitro transcription. Incorporating the 19F-13C label in two model RNAs produces linewidths that are twice as sharp as the commonly used 1H-13C spin pair. Furthermore, the high sensitivity of the 19F nucleus allows for clear delineation of helical and nonhelical regions as well as GU wobble and Watson-Crick base pairs. Last, the 19F-13C label enables rapid identification of a small-molecule binding pocket within human hepatitis B virus encapsidation signal epsilon (hHBV ) RNA. We anticipate that the methods described herein will expand the size limitations of RNA NMR and aid with RNA-drug discovery efforts.

RNAs form essential regulators of biological processes and are implicated in human diseases, making them attractive therapeutic targets (1, 2). This extensive functional diversity of RNA derives from its ability to fold into complex three-dimensional (3D) structures. Yet, the number of noncoding RNA sequences far outstrips the number of solved RNA structures deposited in the Protein Data Bank (PDB) necessary for understanding RNA function (3, 4). In comparison to x-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy provides high-resolution structural and dynamic information in solution, making it an ideal biophysical technique to characterize the interactions between target RNAs and small drug-like molecules. Nonetheless, NMR studies of RNA suffer from poor spectral resolution and sensitivity, both of which worsen with increasing molecular weight. In contrast with proteins, which are made up of 20 unique amino acid building blocks, RNAs are composed of only four aromatic residues. These four resonate over a very narrow chemical shift region. At high magnetic field strengths, sizable transverse relaxation rates (R2) cause line broadening and thereby decrease both sensitivity and resolution. These problems are further exacerbated with increasing molecular weight. To overcome these limitations of RNA, novel labeling strategies that expand the number of NMR probes beyond the traditional nonradioactive and stable isotope labels such as hydrogen-1 (1H), phosphorus-31 (31P), carbon-13 (13C), hydrogen-2 (2H), and nitrogen-15 (15N) are needed.

Solution NMR of the magnetically active fluorine-19 (19F) isotope offers clear advantages in the study of RNA structure and conformational changes, which occur upon ligand binding. 19F has high NMR sensitivity (0.83 of 1H) due to a large gyromagnetic ratio that is comparable to 1H (0.94 of 1H), a 100% natural abundance, and ~6 wider chemical shift dispersion than 1H (5, 6). In addition, 19F is also sensitive to changes in its local chemical environment (5, 6). In contrast with other commonly used NMR nuclei (1H/31P/13C/15N), 19F is virtually absent in biological systems, thereby rendering 19F NMR background free. Together, 19F is an attractive probe for incorporation into nucleic acids to study their structure, interactions, and dynamics in solution.

Given its attractive spectroscopic properties, 19F was incorporated into RNA for NMR studies in the 1970s (79). Since then, 19F has been successfully incorporated into DNA and RNA oligonucleotides for NMR analysis and used to probe RNA and DNA structure, conformational exchange, and macromolecular interactions (10, 11). Most of these studies were conducted on short oligonucleotides [~30 nucleotides (nt)] prepared by solid-phase synthesis with only a few residues 19F labeled. Even when 2-fluoroadenine (2FA) was incorporated into a 73-nt (~22 kDa) guanine-sensing riboswitch, only 4 of the 16 signals could be assigned. This 2FA study hinted at the limitations of 19F NMR for large RNAs (12). Despite its attractiveness, the application of 19F NMR to study RNA has remained limited because the large 19F chemical shift anisotropy (CSA) contributes substantially to line broadening as a function of increasing molecular weight and polarizing magnetic fields.

To circumvent this limitation, Boeszoermenyi et al. (13) recently showed that direct coupling of 19F to 13C allowed for cancelation of CSA and dipole-dipole (DD) interactions. By incorporating this 19F-13C spin pair into aromatic moieties of proteins and a 16-nt DNA, they showed that a transverse relaxation optimized spectroscopy (TROSY) version of a 19F-13C heteronuclear single-quantum coherence (HSQC) (13) provided improved spectroscopic properties. These exciting results hinted that installing 13C-19F pairs in RNA nucleobases should also lead to improved spectroscopic features.

However, there were no facile methods to readily incorporate 19F-13C spin pairs into RNA. To overcome this technical obstacle of incorporating fluorinated aromatic moieties into RNA, we provide here a straightforward chemoenzymatic synthesis of [5-19F, 5-13C]-uridine 5-triphosphate (5FUTP) for incorporation into RNA (Fig. 1) using phage T7 RNA polymerasebased in vitro transcription. To showcase its versatility, we transcribed two model RNAs using these labels: the 30-nt (~10-kDa) human immunodeficiency type 2 transactivation response (HIV-2 TAR) element (6, 14) and the 61-nt (~20-kDa) human hepatitis B virus encapsidation signal epsilon (hHBV ) element (Fig. 1) (15, 16).

(A) Model RNA systems: HIV-2 TAR (30 nt, 10 kDA) and hHBV (61 nt, 20 kDa). Residues highlighted in green are labeled with 19F-13C5-fluorouridine (5FU) shown in the box. Green circle, 19F; brown circle, 13C; blue circle, 2H. (B) Theoretical 19F,13C spectrum showing the four observable magnetization components of the 19F-13C spin pair as well as the decoupled resonance that has the average chemical shift and linewidths of all four components.

With our new labels, we demonstrate several advantages for RNA NMR studies, including improved resolution and increased sensitivity to ligand binding. We show that a 19F substitution is structurally nonperturbing and has an optimal TROSY effect at readily available magnetic field strength of 600 MHz (1H frequency), in agreement with previous studies (13). Unlike C-H spectra, the resolving power of 19F allows for easy identification of RNA structural elements in helical and nonhelical regions, as well as in wobble GU base-paired regions. With protons substituted with deuterium and depending on the molecular weight of the RNA, the TROSY effect in the 19F-13C pair can reduce the 13C linewidth by a factor >2, compared to a 13C-1H pair, and the 19F-13C label enables detection of a small-molecule binding to a 20-kDa RNA. Thus, our 19F-13C label overcomes several of the limitations in sensitivity and resolution facing RNA NMR studies with the potential to extend the application of solution NMR measurements to largermolecular weight systems in vivo.

Given the potential utility but unavailability of 19F-13C spin pairs in aromatic moieties of RNA, we first sought to develop a reliable and scalable method that combined chemical synthesis with enzymatic coupling in almost quantitative yields. This chemoenzymatic approach is a versatile method that combines chemical synthesis of atom-specific labeled nucleobases with commercially available selectively labeled ribose using enzymes from the pentose phosphate pathways (PPPs) (3, 4). To this end, we adapted the method of Santalucia et al. (17) and Kreutz and co-workers (18) and first synthesized the uracil base (U) specifically labeled with 13C at the aromatic C5 and 15N at the N1 and N3 positions (Fig. 1). This synthesis is readily accomplished using unlabeled potassium cyanide, 13C-labeled bromoacetic acid, and 15N-labeled urea. The resulting U was converted to 5-fluorouracil (5FU) by direct fluorination with Selectfluor (19, 20). This strategy allows for efficient and cost-effective synthesis of the 5FU base with high yield of ~63%. In addition, to remove unwanted scalar coupling interactions (14), we selectively deuterated H6 (~95%) using well-established methods (21). Next, using enzymes from the PPP, we coupled 5FU to D-ribose labeled at the C1 position to give 5FUTP (Fig. 1) (3, 22) with an overall yield of ~50%. This site-specifically labeled 5FUTP was then used for DNA templatedirected T7 RNA polymerasebased in vitro transcription with overall yields comparable to those obtained with unmodified nucleotides.

Fluorine substitution at uridine C5 is thought to reduce the imino N3 pKa values by about 1.7 to 1.8 units with respect to their protonated analogs (23), leading to extensive line broadening of imino protons in 5FU RNAs (24). To determine if incorporation of 5-fluorouridine alters the folding thermodynamics of our RNAs (Fig. 1), we recorded ultraviolet (UV) thermal melting profiles for both wild-type (WT) and 5FU HIV-2 TAR and hHBV (table S1). Both WT and 5FU RNAs showed a single transition in their melting profiles, consistent with unimolecular folding (25). WT and 5FU HIV-2 TAR had melting temperatures within ~1 K of each other (WT: Tm = 355.6 0.5 K; 5FU: Tm = 357.4 0.4 K). Similarly, 5FU hHBV had a melting temperature of 327.1 0.1 K, which is within the error of the melting temperature of WT. Together, these results suggest that 5FU does not markedly alter the thermodynamic stability of HIV-2 TAR and hHBV , in accordance with previous studies of 5FU RNAs (6, 7, 24).

The linewidth for aromatic 19F-13C spin pair (Fig. 1B) is expected to become dominated by the CSA mechanism with increasing polarizing magnetic fields (13). To estimate this effect for 5FU, we calculated the chemical shielding tensor (CST) for 19F-13C spin pairs using density functional theory (DFT) methods (tables S2 and S3) (2629). Using these CST parameters and relaxation theory implemented in the Spinach library (30), we computed the TROSY R2 relaxation rates for the 19F-13C pair of 5FU (13CF and 19FC) and the 13C-1H pair of U (13CH and 1HC) (Fig. 2) assuming isotropic tumbling. The R2 of fluorinated carbon (13CF) TROSY resonance is ~2 times smaller than that of the protonated carbon (13CH) at their respective minima of ~600 and ~950 MHz, respectively, for all molecular weights greater than 5 ns (Fig. 2A). Compared with the decoupled resonance, the R2 of the 13CF TROSY resonance is ~3 times smaller than that of protonated carbon for all molecular weights greater than 5 ns (fig. S1). Although the TROSY effect is quite small for 19F nuclei bonded to 13C (19FC) and for 1H nuclei bonded to 13C (1HC), the R2 of 19FC is three times bigger than that of 1HC (fig. S2). Thus, sensitive, high-resolution NMR spectra for the 19F-13C pair of 5FU in RNAs can be obtained by selective detection of the 13CF TROSY resonance as demonstrated for the 19F-13C pair in aromatic amino acids (13).

(A) Theoretical curves showing the expected R2 values for the TROSY component of 13CF (cyan) and 13CH (magenta) as a function of magnetic field strength (relative to 1H Larmor frequency) for c = 6 ns (dashed line), 25 ns (solid line), and 100 ns (dotted line) at 25C. (B) Theoretical R2 values taken at the commercially available magnetic field strength closest to the maximum TROSY effect (13CH = 950 MHz; 13CF = 600 MHz) for c = 6, 25, and 100 ns at 25C.

To validate these theoretical TROSY predictions experimentally, we adapted the 1H-15N TROSY experiment (3, 31, 32) to perform a 19F-13C TROSY experiment on a ~10-kDa 5FU HIV-2 TAR and on a ~20-kDa 5FU hHBV RNAs (Fig. 3). Because of hardware limitations, we could only run experiments that start with and end on the magnetization of 19F, with the 13C frequency encoded in the indirect dimension. That is, we used the so-called 19F-detected out-and-back method, rather than the more sensitive 19F-excited out-and-stay 13C-detected experiment (13). We collected spectra for each of the four components (Fig. 1B) of the 19F-13C (1H-13C) correlations for both 5FU (WT) HIV-2 TAR and hHBV (figs. S3 to S6).

(A) 19F-13C TROSY of 5FU HIV-2 TAR. (B) 1H-13C TROSY of WT HIV-2 TAR. (C) 19F-13C TROSY of 5FU hHBV . (D) 1H-13C TROSY of WT hHBV . The assignments of 5FU and WT TAR-2 are indicated, as well as the arbitrary peak numbers for 5FU and WT hHBV . The same window size was used in all four spectra to aid in comparison. Gray dashed boxes indicate signals from helical, GU, and nonhelical regions. For (D), the black box indicates a zoom-in view of poorly resolved signals.

Both HIV-2 TAR and hHBV show ~6-fold improvement in chemical shift dispersion of 19F compared with 1H and similar dispersion in 13C (Fig. 3). All six correlations of HIV-2 TAR are well resolved for both 1H-13C and 19F-13C correlations and are in agreement with previously published 1H-19F and 1H-13C RNA spectra (6, 24, 33). Nonetheless, even for this small RNA, the 19F-13C spin pair markedly improves the spectral resolution. 5FU HIV-2 TAR shows a chemical shift dispersion of 2.6 parts per million (ppm) in the 19F dimension and only 0.5 ppm in the 1H dimension for WT (Fig. 3, A and B). Replacing 1H with 19F at C5 results in a slight reduction in chemical shift dispersion along the 13C dimension from 2.1 to 1.5 ppm, although this effect is much smaller than the gain in resolution for 19F over 1H (Fig. 3, A and B). Similarly, the 19F resonances of 5FU hHBV are spread over 4.5 ppm, whereas the WT 1H signals resonate over a narrow 0.8-ppm window. This represents 5.7 times better dispersion (Fig. 3, C and D). Again, substitution of 1H with 19F at C5 results in a reduction in chemical shift dispersion of 2.3 to 1.7 ppm along the 13C dimension for hHBV (Fig. 3, C and D). Of the anticipated 18 signals for hHBV , 16 are resolved for WT and 17 for 5FU. Together, these results demonstrate the marked gain in resolution afforded by the 19F-13C spin pair in 5FU RNAs compared with the 1H-13C spin pair in WT.

In addition to this considerable gain in resolution, 19F-13C labeling confers favorable 13CF TROSY linewidths. We compared the relative linewidths for both RNAs, which we assume to be Lorentzian (Figs. 4 and 5). For 5FU HIV-2 TAR, the 13CF TROSY linewidths were 1.5 times sharper on average than the anti-TROSY components, with a range of 1.3 to 1.7 (Fig. 4A). For WT HIV-2 TAR, the 13CH TROSY component was 3.7-fold narrower than the anti-TROSY component (range, 1.6 to 8.7) (Fig. 4B). Similarly, for 5FU HBV , the 13CF TROSY linewidths were 2.2-fold narrower than the anti-TROSY ones over a range of 1.5 to 3.3 (Fig. 4C). For WT HBV , only 5 of the 16 13CH anti-TROSY signals were observed and were 2.6 times broader than the TROSY resonances (range, 2.0 to 3.3) (Fig. 4D). As predicted from our simulations (Fig. 2), the 13CF TROSY component relaxes ~2 times slower than the 13CH TROSY component in both HIV-2 TAR and hHBV . The 19FC TROSY linewidths for 5FU HIV-2 TAR and 5FU HBV were 1.4 (range, 1.3 to 1.6) and 1.6 (range, 1.1 to 2.5) times narrower than the anti-TROSY components, respectively (Fig. 5, A and C). For both WT HIV-2 and WT HBV , the 1HC TROSY and anti-TROSY linewidths were comparable (Fig. 5, B and D). Consistent with our simulations, the 19FC TROSY linewidth is ~2-fold larger than that of the 1HC component for both RNAs (fig. S3). Again, this is in line with the poor performance of 19F NMR experiments due to the large CSA-induced relaxation. Thus, the incorporation of the 13C label mitigates the deleterious relaxation of the 19F nuclei within a 19F-13C spin pair. However, even for medium-sized RNAs ~20 kDa, 19F TROSY detection of the 19F-13C spin pair still outperforms that for a 1H-13C spin pair. Therefore, to reap the maximum benefits of this label, it is advantageous to monitor the 13C nuclei rather than the 19F nuclei. We anticipate that the 19F-13C TROSY effect will continue to scale with molecular weight for RNAs as was seen recently with proteins (13) and our simulations.

Quantification of TROSY (black) and anti-TROSY (gray) (A) 13CF and (B) 13CH linewidths for HIV-2 TAR. Note that U40 was not observed in the anti-TROSY spectrum of WT HIV-2 TAR (B). In addition, the anti-TROSY component of U38 in (B) was 97 Hz and truncated to fit in the plot. Quantification of TROSY (black) and anti-TROSY (gray) (C) 13CF and (D) 13CH linewidths for hHBV . Note that peaks 1 through 11 in WT hHBV were not observed in the anti-TROSY spectrum (D). The average SD in Hz is shown for the TROSY and anti-TROSY components in each plot. Peak numbers and assignments are given in Fig. 3.

Quantification of TROSY (black) and anti-TROSY (gray) (A) 19FC and (B) 1HC linewidths for HIV-2 TAR. Quantification of TROSY (black) and anti-TROSY (gray) (C) 19FC and (D) 1HC linewidths for hHBV . The average SD in Hz is shown for the TROSY and anti-TROSY components in each plot. Peak numbers and assignments are given in Fig. 3.

In addition to these gains in resolution and favorable linewidths, previous work suggested the 19F chemical shifts serve as sensitive markers of RNA secondary structure (10, 11). For example, GU wobble base pairs are deshielded and shifted by ~4.5 ppm to lower fields compared with AUs within Watson-Crick geometries (34). On the basis of these earlier observations, we hypothesized that 19F-13C correlations of HIV-2 TAR and hHBV can be grouped on the basis of whether or not they are in helical, nonhelical, or GU base-paired regions of the RNA. As a positive control, we note that nonhelical U23, U25, and U31 in 5FU HIV-2 TAR resonate around ~165.5 ppm in 19F and ~142.5 ppm in 13C (Fig. 3A). On the other hand, the helical residues U38, U40, and U42 of 5FU HIV-2 TAR are centered around ~167.5 ppm in 19F and ~141.5 ppm in 13C in line with previous observations for 19F-1H samples of HIV-2 TAR (6) and tRNA (34). Comparison of the equivalent 1H-13C spectra shown in Fig. 3B indicates that even though helical residues cannot be distinguished from nonhelical residues in the 1H dimension, nonhelical residues can be differentiated from helical base pairs in the 13C dimension for a 1H-13C spin pair.

The 17 19F-13C resolved correlations of 5FU hHBV show similar clustering as 5FU HIV-2 TAR (Fig. 3C). For instance, the six most intense signals are centered around ~165.5 ppm in 19F and ~142.5 ppm in 13C where the nonhelical signals of HIV-2 TAR are located. On the basis of the secondary structure of hHBV (Fig. 1A), these six intense peaks belong to the six nonhelical uridines (U15, U17, U18, U32, U34, and U43) (Fig. 3C). A seventh peak is also seen in this region, most likely due to U48 or U49, both of which flank the bulge region. The weaker peaks are from the helical portions of hHBV because these signals located at ~167.5 ppm in 19F and ~141.5 ppm in 13C resonate in the same region as the helical signals from 5FU HIV-2 TAR (6). HIV-2 TAR contains only Watson-Crick base pairs, and so, signals in this region of the hHBV spectrum correspond to AUs (U3, U7, U38, U39, U47, U48, U49, and U56). Of the eight anticipated peaks belonging to helical residues, only seven are observed, further suggesting that U48 or U49 may fray and resonate within the nonhelical region. Unlike HIV-2 TAR, hHBV has four noncanonical GU wobble base pairs embedded within helical regions. The three signals resonating in a distinct region centered at ~163.5 ppm in 19F and ~142.0 ppm in 13C are from the four GUs (U4, U9, U12, and U25). This is in line with previous observations of GU base pairs in tRNA (34). Peak 5 (Fig. 3C) is most likely two GUs that are overlapped. Again, comparison of the equivalent 1H-13C spectra shown in Fig. 3D indicates that even though helical residues can be distinguished from nonhelical residues, nonhelical residues cannot be differentiated from GU base pairs for a 1H-13C spin pair. Thus, the spectroscopic discrimination of helical and nonhelical regions as well as GU wobble and Watson-Crick base pairs in RNA structures becomes possible with the high sensitivity of 19F to the local chemical environment of a 19F-13C spin pair. This distinguishing feature is not readily available for a 1H-13C spin pair.

Ligand-based (35) and protein-observed (36) 19F NMR screening methods are important for identifying small drug-like molecules that act as protein inhibitors. Although most work to date has focused on proteins, recent work suggests that RNAs also contain specific binding pockets that could be easily distinguished and targeted with small molecules (1, 2). hHBV is at the center of the viral replication cycle since the first two residues in its internal bulge are used by the virus to initiate synthesis of the minus-strand DNA. Thus, targeting this RNA structure will notably expand the repertoire of HBV drug targets beyond the current focus on viral proteins (37). Given 19F chemical shifts serve as sensitive markers of RNA secondary structure, we reasoned that 19F-13C spectroscopy will likely pinpoint loop over helical region binders. Rather satisfyingly, we found a small molecule that specifically binds a subset of nonhelical residues in 5FU hHBV (Fig. 6). Overlay of the full spectra of 5FU hHBV with and without the small-molecule shows chemical shift perturbations (CSPs) (38) predominantly confined to nonhelical regions (Fig. 6). Within the nonhelical residues, only four of the seven signals shift with the addition of the small molecule, which suggests selectivity for certain nonhelical residues over others (Fig. 6). We propose a model whereby our small molecule binds hHBV in the 6-nt bulge formed between C14 and C19, but not anywhere else in the RNA. The minor CSPs seen in the helical portion of the 5FU hHBV spectra are from U residues flanking the 6-nt bulge, specifically U47, U48, and U49. Last, the CSP seen in the GU portion is from U12, which also flanks our proposed binding pocket.

(A) Overlay of 19F-13C-TROSY spectra for hHBV without (black) and with small molecule (SM, magenta). (B) Zoom-in of nonhelical residues showing chemical shift perturbations (CSPs) upon addition of SM. (C) Quantification of the CSPs upon addition of SM. The average (Ave) CSP is shown as a dashed line.

19F is an attractive spectroscopic probe to study biomolecular structure, interactions, and dynamics in solution. Nonetheless, a number of obstacles must be overcome for it to become widely useful. First, we must be able to easily install the label into any biopolymer. While incorporation of fluorinated aromatic amino acids and nucleobases into proteins and nucleic acids is usually not a technical challenge, until now, synthesis of carbon-labeled and fluorinated nucleobase to create a 19F-13C spin pair has been problematic for RNA. Here, we present a facile strategy to incorporate 19F-13C 5-fluorouridine into RNA using in vitro transcription for characterization of small-molecule binding interactions by NMR. Our protocol to prepare 19F-13C 5-fluorouridine-5-triphosphate (5FUTP) involves chemically synthesizing 5FU and then enzymatically coupling it to 13C-labeled D-ribose. Our synthetic strategy can be generalized to selectively place labels in the pyrimidine nucleobase at either 15N1, 15N3, 13C2, 13C4, 13C5, or 13C6 or any combinations thereof, and then enzymatically couple ribose labeled at either 13C1, 13C2, 13C3, 13C4, or 13C5 or any of the preceding ribose combinations to the base. The resulting isotopically enriched 5FUTP is then readily incorporated into any desired RNA using DNA templatedirected T7 RNA polymerasebased in vitro transcription. This enzymatic approach, unlike solid-phase RNA synthesis, is not limited to RNAs less than 70 nt or to nucleotides made of labeled nucleobase coupled to unlabeled ribose. Although fluorine substitution at C5 in pyrimidines strongly affects the shielding of the nearby H6, it has little effect on the anomeric H1 chemical shifts (24). We therefore anticipate that our unique strategy that combines ribose 13C1 label with 19F-13C uracil should allow the transfer of assignments from unmodified RNAs to 5-fluoropyrimidinesubstituted RNAs made with our labels.

Second, because of van der Waals radii comparable to that of 1H, 19F is considered minimally perturbing when incorporated into biopolymers (24). Although fluorine substitution in 5FU RNAs leads to sizeable line broadening of the imino protons, thermal melting analysis indicates that the 5FU RNAs are thermodynamically equivalent to the nonfluorinated RNAs (6, 7, 24). In future work, it will be important to systematically investigate the effect of fluorine substitution not only on thermodynamic stability but also on folding kinetics of RNAs. Insights derived from solving, at high-resolution, the 3D structures of fluorinated and nonfluorinated RNA could potentially guide the use of these spin pairs to spy on the biological processes within the cell.

Third, despite its huge potential, nucleic acid observed 19F (NOF) NMR has remained underused because the large 19F CSA induces severe line broadening at high molecular weights and magnetic fields. Using DFT calculations of CST parameters, we show that an optimal 19F-13C TROSY enhancement occurs at 600-MHz 1H frequency to enable slow relaxation of 13C bonded to 19F. Our RNAs show an enhanced 19F-13C TROSY effect with increasing molecular weight and 13C linewidths that are twice as sharp as seen with traditional 1H-13C spin pairs. Thus, nucleobase 19F -13C TROSY will expand the applicability of RNA NMR beyond the ~30-nt (~10-kDa) average.

Fourth, the RNA secondary structure is made up of segments of nucleotides that are either base paired or not. The arrangements of base-paired with unpaired regions can leave distinct NMR chemical shift signatures that can provide low-resolution structural information with minimum expenditure of time and cost. For example, the H5 of a pyrimidine is sensitive to the nature of the residue that comes before it within a triplet of canonical Watson-Crick AU and GC base pairs. When the A in a central UA base pair is substituted by a G, the H5 resonance shifts downfield because of the formation of the GU base pair. Yet, an analysis of the commonly used 1H-13C probes fails to unambiguously separate nonhelical residues from helical ones (39). In contrast, the 19F-13C labels resonate in distinct chemical shift regions based on their secondary structure. For instance, nonhelical residues resonate in spectral regions distinct from helical ones, which are further separated into GU wobble and AU Watson-Crick base-paired regions. The ability to differentiate between different structural features in an RNA simply based on chemical shifts removes the need for the time-consuming and laborious process of resonance assignment.

Given the ubiquity and functional importance of GU wobble base pairs (40) in all kingdoms of life (41), the ability to easily distinguish GU from canonical GC and AU base pairs has several important implications. For instance, in the minor groove, a GU base pair presents a distinctive exocyclic amino group that is unpaired and the Us C1 atom rotates counterclockwise compared with the Cs C1 atom in a canonical GC base pair. This region serves as an important site for protein-RNA interactions. Similarly, in the major groove, G N7 and O6 together with U O4 create an area of intense negative electrostatic potential conducive for binding divalent metal ions. Furthermore, all canonical Watson-Crick base pairs are circumscribed by ~10.6- diameters formed by a line connecting their C1-C1 centers. These ribose-connected centers are superimposable with almost perfect alignment. In contrast, a GU base pair is misaligned counterclockwise by a residual twist of +14, and an UG base pair is misaligned clockwise by a residual twist of 11 (42). That is, the GU base pair is not isosteric with canonical Watson-Crick pairs. Rather, these wobble base pairs either overtwist or undertwist the RNA double helix. 19F-13C labels might aid in elucidating the structural and dynamic basis of these twists depending on the identity of the base pairs neighboring the wobble pair. We, therefore, anticipate that our new label could potentially open up avenues for probing GU wobble pairs in various structural contexts outlined above, such as 19F-13Clabeled RNA-protein interactions and metalloribozyme-ion interactions.

In summary, the labeling technologies presented here open the door for characterizing the structure, dynamics, and interactions of RNA, RNA-RNA, RNA-DNA, RNA-protein, and RNA-drug complexes in vitro and in vivo for complexes as large as 100 kDa or higher with the appropriate fluorine NMR hardware. This 19F -13C labeling approach will also enable correlating chemical shiftstructure relationships to aid chemical shiftcentered probing of RNA structure, dynamics, and interactions. We envision that the 19F-13C spin pair, by providing a clear demarcation of RNA structural elements, may facilitate the discovery and identification of small drug-like molecules that target RNA binding pockets in vitro and in vivo.

The full description of Materials and Methods can be found in the Supplementary Materials. A brief summary is provided here.

[5-19F, 5-13C, 6-2H] and [5-19F, 5-13C, 6-2H, 1,3-15 N2]5FU were synthesized from unlabeled potassium cyanide, 13C-labeled bromoacetic acid, and 15N-labeled urea as described elsewhere (3, 17, 18, 24). The resulting uracil was converted to 5FU by direct fluorination with Selectfluor and deuteration (1921). [1,5-13C2, 5-19F, 6-2H]5FUTP and [1,5-13C2, 5-19F, 6-2H, 1,3-15 N2]5FUTP were synthesized using PPP enzymes (3, 4, 6, 22, 24, 43).

All RNAs were prepared by in vitro transcription and purified as previously described (3, 4). RNA concentrations were approximated by UV absorbance using extinction coefficients of 387.5 mM1 cm1 for HIV-2 TAR and 768.3 mM1 cm1 for hHBV . All RNA concentrations were >0.5 mM (~0.3 ml) in Shigemi NMR tubes.

We collected thermal melting profiles for both WT and 5FU-substituted HIV-2 TAR and hHBV as previously described (24, 25).

Calculations were carried out on 1-methyl-uracil and 5-fluoro-1-methyl uracil using optimized geometries (2628). All calculations used the Gaussian-16 program (29). Details are provided in the Supplementary Materials.

All 19F-13C TROSY spectra were collected at 298 K using a Bruker 600 MHz Avance III spectrometer equipped with TXI (triple resonance inverse) and BBI (broad band inverse) probes. All data were processed with Brukers Topspin 4.0.7 software. 1H chemical shifts were internally referenced to DSS (0.00 ppm), with the 13C chemical shifts referenced indirectly using the gyromagnetic ratios of 13C/1H (44). The 19F chemical shifts were internally referenced to trifluoroacetic acid (75.51 ppm) (45). Experiments showing each component of the 1H/19F-13C correlations were adapted from a sensitivity- and gradient-enhanced 1H-15N TROSY used for proteins (31).

M. J. Frisch, G. W. Trucks, H. B. Schlegel, G. E. Scuseria, M. A. Robb, J. R. Cheeseman, G. Scalmani, V. Barone, G. A. Petersson, H. Nakatsuji, X. Li, M. Caricato, A. V. Marenich, J. Bloino, B. G. Janesko, R. Gomperts, B. Mennucci, H. P. Hratchian, J. V. Ortiz, A. F. Izmaylov, J. L. Sonnenberg, D. Williams-Young, F. Ding, F. Lipparini, F. Egidi, J. Goings, B. Peng, A. Petrone, T. Henderson, D. Ranasinghe, V. G. Zakrzewski, J. Gao, N. Rega, G. Zheng, W. Liang, M. Hada, M. Ehara, K. Toyota, R. Fukuda, J. Hasegawa, M. Ishida, T. Nakajima, Y. Honda, O. Kitao, H. Nakai, T. Vreven, K. Throssell, J. Montgomery, J. A., J. E. Peralta, F. Ogliaro, M. J. Bearpark, J. J. Heyd, E. N. Brothers, K. N. Kudin, V. N. Staroverov, T. A. Keith, R. Kobayashi, J. Normand, K. Raghavachari, A. P. Rendell, J. C. Burant, S. S. Iyengar, J. Tomasi, M. Cossi, J. M. Millam, M. Klene, C. Adamo, R. Cammi, J. W. Ochterski, R. L. Martin, K. Morokuma, O. Farkas, J. B. Foresman, D. J. Fox, Gaussian 16, Revision A.03 (2016).

Acknowledgments: We thank P. Deshong, J. Kahn, L.-X. Wang, and P. Y. Zavalij (University of Maryland) and H. Arthanari (Harvard University) for the helpful comments. We thank S. Bentz and D. Oh for help in preparing samples for thermal melt analysis, and M. Svirydava for help in analyzing samples by mass spectrometry. Funding: We thank the National Science Foundation (DBI1040158 to T.K.D. for NMR instrumentation) and the NIH (U54AI50470 to T.K.D. and D.A.C.) for support. Author contributions: T.K.D.: conceptualization. T.K.D. and O.B.B.: implementation of the project and manuscript preparation. G.Z., B.C., K.M.T., and T.K.D.: synthesis of 5FU. O.B.: synthesis of 5FUTP, RNA synthesis, and thermal melt analysis. T.K.D., K.M.T., B.C., and O.B.B.: TROSY measurements. O.B.B.: small-molecule titration. D.A.C.: DFT calculations. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors

Originally posted here:
Solution NMR readily reveals distinct structural folds and interactions in doubly 13C- and 19F-labeled RNAs - Science Advances

Until recently High Performance Computing (HPC) was largely the preserve of the automotive, aerospace and financial services industries but, increasingly, the need for HPC within the life sciences sector has predominated.

Never has this been more evident than in the last six months of the pandemic, which has seen global HPC resources pooled in an unprecedented effort to halt the progress of COVID-19.

HPC refers to the practice of aggregating super-computing power in a way that delivers much higher performance than the typical desktop computer or workstation.

It enables researchers to analyse vast datasets, run extraordinarily large numbers of calculations in epidemiology, bioinformatics and molecular modelling and solve highly complex scientific problems.

Most significantly, it enables scientists and researchers to do in hours and days what would have, otherwise, taken months and years via slower, traditional computing platforms.

According to HPC expert Adrian Wander over the next few years, we will start seeing an ever-increasing democratisation of HPC which will be central to super-computing becoming a routine part of life sciences and pharmaceutical research.

A former research student with a PhD in theoretical solid-state physics, Adrian was a chemistry lecturer at Cambridge University, working under Professor Sir David King (later chief scientific advisor to Tony Blairs government) before devoting the next 30 years to working in HPC.

During this time, Adrian was an integral part of the team setting up the Hartree Centre home to some of the most advanced computing, data and AI technologies in the UK ran the scientific computing department at the Science & Technology Facilities Council before moving to the European Centre for Medium-Range Weather Forecasts (ECMWF), delivering highly critical, time-sensitive computations that relied heavily on HPC modelling for speed and accuracy.

He says: Historically, HPC was seen as a complex, specialist activity requiring special machines and expert techniques. But, especially in recent months, we have seen a dramatic change in who wants and needs access to HPC.

Traditionally, the biggest users were the automotive and aerospace sectors. But in the current COVID-19 climate, aeroplanes arent being purchased and consumers arent buying cars, whereas we are seeing a dramatic rise in the use of HPC within the life sciences industry not least because the need has never been greater.

HPC expert Adrian Wander

The life sciences sector was initially slow to catch onto HPC compared with the physical science community. In 2007, the Biotechnology & Biological Sciences Research Council (BBSRC) paid for 10 per cent of Britains academic national supercomputer service, HECToR (High End Computing Terascale Resource) but it ended up dropping the partnership because it wasnt being used by its community.

More recently there has been a huge upsurge in life sciences to explore workloads more actively and efficiently e.g. via simulation and modelling of protein folding and the structures of proteins, and similar areas. And, of course, Artificial Intelligence is being relied on heavily in the search for new drugs by automatically sampling huge numbers of drug candidates on the target treatment.

Adrian adds: Over the next few years, HPC will become both simpler to use and more easily available and it will inevitably become a more mainstream part of research portfolios just as incubators and wet benches are now.

Even with genome sequencing, the Oxford nanopore system takes you down into quite small organisations doing this kind of work because the new sequencing machines make sequencing quite easy to do.

The tricky part is putting all the bits together to assemble the full genome and this requires increasing amounts of compute power, irrespective of the size of the organisation doing the assembly.

The technological advance in this field has been incredible: Sequencing the first human genome was an international effort that cost around $1 billion and took 13 years to complete. Today, genomic studies and meta-genomics are routinely run for between $3000-$5000 and take little more than a couple of days to complete.

To quantify the remarkable difference HPC is making to scientific discovery, one only has to note the following: After HIV-1 (the main cause of AIDS in humans) was first identified in 1981, it took almost three more decades to genetically decode it.

Four years later, in 2013, the SARS outbreak (due to another coronavirus) was decoded within three months. This year the genome behind COVID-19 was decoded and published globally within days. Things are indeed changing in the life sciences sector. And rapidly so.

At the end of May, this country led by UK Research and Innovation became the first European super-computing partner to join the COVID-19 High Performance Computing Consortium, contributing more than 20 Petaflops of HPC capability to the global effort addressing the coronavirus crisis.

The consortium currently has 56 active projects and more than 430 Petaflops of compute which, collectively, is equal to the computing power of almost 500,000 laptops.

For perspective, a supercomputer with just eight petaflops can do a million calculations per person in the world per second. But, by pooling supercomputing capacity the consortium offers 50 times that and hopes to reduce the time it takes to discover new molecules which could lead to coronavirus treatments and a vaccine.

Last week, it was revealed that Summit, the worlds second-fastest supercomputer, had been employed to produce a genetic study of COVID-19 patients, analysing 2.5 billion genetic combinations in just two weeks.

The insights which Summit has produced through HPC and AI are significant in understanding how coronavirus causes the COVID-19 disease and additionally indicate potential new therapies to treat the worst symptoms.

Of course, return on investment is also important in driving the move towards HPC. Hyperion Research recently reported that every dollar spent on HPC in life sciences earns $160 of which $41 is profit or cost-saving.

Adrian explains: On the face of it, when you look at the cost of hosted services, it might seem expensive. But you need to weigh that against the fact that you no longer need an in-house team of high voltage engineers and all that comes with them.

And for the big drug companies and pharmas using increasing amounts of HPC cycles for drug discovery, personalised healthcare has the potential to become a huge profit-making market.

With astronomical levels of computational life sciences data being produced daily, the need for secure storage to house this and advanced computing infrastructure to rapidly analyse the vast datasets is becoming paramount. Thats the reason why more and more research institutions are outsourcing or co-locating to specialist data centres such as the bleeding edge Kao Data campus in Harlow.

Adam Nethersole

Kao Datas director, Adam Nethersole explains: Staying static isnt an option within a highly competitive sector where being first to market is everything especially when were talking about life-saving treatments, medicines and vaccines.

So across the life sciences sector, most universities, laboratories or research institutes are looking to expand their access to high performance computing.

But, of course, many of these organisations are pretty landlocked and with old architecture and there isnt available space that can be turned into a hyper-efficient data centre unless youre in new, state of the art facilities.

And even if you are able to scale internally and have the technical expertise in-house to do this you still need to consider how youre going to power and cool the additional servers especially in locations like Cambridge where there simply isnt a vast surplus of available electricity ready and available to be utilised.

One solution is using hyperscale cloud services but, while these are great for streaming videos, music and playing video games, they arent optimal for specialist computing which requires servers located closely together (and not virtualised in the cloud) and in many cases, bespoke, tailored IT architecture. Cloud platforms also tend to be expensive when moving large amounts of data.

This is why were seeing an increasing number of enquiries about moving computing infrastructure off-premise and into an advanced industrial scale data centre like the one we operate at Kao Data.

With multi-megawatts of power and space available immediately and excellent connectivity back-up to Cambridge citys research parks, were ideally placed to support.

One of Cambridges most forward-thinking research institutions, EMBL-EBI, has already done this and we are in conversation with others about helping them plan their computing footprint for the next 10, 15 and 20 years.

The rest is here:
HPC revolutionising speed of life science research - Business Weekly

Molecular Modelling Market report provides a detailed evaluation of the market by highlighting information on different aspects which include drivers, restraints, opportunities, threats, and global markets including progress trends, competitive landscape analysis, and key regions expansion status.This report is comprehensive numerical analyses of the Molecular Modelling industry and provides data for making strategies to increase the market growth and success. The Report also estimates the market size, Price, Revenue, Gross Margin and Market Share, cost structure and growth rate for decision making.

Molecular Modelling Market provides key analysis on the market status of theMolecular Modelling manufacturers with best facts and figures, meaning, definition, SWOT analysis, expert opinions and the latest developments across the globe. The Report also calculate the market size,Molecular Modelling Sales, Price, Revenue, Gross Margin and Market Share, cost structure and growth rate.

Final Report will add the analysis of the impact of COVID-19 on this industry.

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The research covers the current Molecular Modellingmarket size of the market and its growth rates based on 6-year records with company outline of Key players/manufacturers:

Brief Description about Molecular Modelling market:

Molecular modelling encompasses all methods, theoretical and computational, used to model or mimic the behaviour of molecules. The methods are used in the fields of computational chemistry, drug design, computational biology and materials science to study molecular systems ranging from small chemical systems to large biological molecules and material assemblies.

Molecular modelling methods are now used routinely to investigate the structure, dynamics, surface properties, and thermodynamics of inorganic, biological, and polymeric systems. The types of biological activity that have been investigated using molecular modelling include protein folding, enzyme catalysis, protein stability, conformational changes associated with biomolecular function, and molecular recognition of proteins, DNA, and membrane complexes.

By the product type, the Molecular Modelling marketis primarily split into:

By the end users/application, Molecular Modelling marketreport coversthe following segments:

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The key regions covered in theMolecular Modelling market report are:

Key Reasons to Purchase:

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Table of Contents with Major Points :

Detailed TOC of Global Molecular ModellingMarket Research Report 2020-2026, by Manufacturers, Regions, Types and Applications

1 Molecular Modelling MarketOverview1.1ProductOverviewandScopeof Molecular Modelling1.2 Molecular Modelling SegmentbyType1.3 Molecular Modelling SegmentbyApplication1.4Global Molecular Modelling MarketSizeEstimatesandForecasts1.5 Molecular Modelling Industry1.6 Molecular Modelling MarketTrends

2Global Molecular Modelling MarketCompetitionbyManufacturers2.1Global Molecular Modelling SalesMarketSharebyManufacturers(2015-2020)2.2Global Molecular Modelling RevenueSharebyManufacturers(2015-2020)2.3Global Molecular Modelling AveragePricebyManufacturers(2015-2020)2.4Manufacturers Molecular Modelling ManufacturingSites,AreaServed,ProductType2.5 Molecular Modelling MarketCompetitiveSituationandTrends2.6ManufacturersMergers&Acquisitions,ExpansionPlans2.7PrimaryInterviewswithKey Molecular Modelling Players(OpinionLeaders)

3 Molecular Modelling RetrospectiveMarketScenariobyRegion3.1Global Molecular Modelling RetrospectiveMarketScenarioinSalesbyRegion:2015-20203.2Global Molecular Modelling RetrospectiveMarketScenarioinRevenuebyRegion:2015-20203.3NorthAmerica Molecular Modelling MarketFacts&FiguresbyCountry3.4Europe Molecular Modelling MarketFacts&FiguresbyCountry3.5AsiaPacific Molecular Modelling MarketFacts&FiguresbyRegion3.6LatinAmerica Molecular Modelling MarketFacts&FiguresbyCountry3.7MiddleEastandAfrica Molecular Modelling MarketFacts&FiguresbyCountry

4Global Molecular Modelling HistoricMarketAnalysisbyType4.1Global Molecular Modelling SalesMarketSharebyType(2015-2020)4.2Global Molecular Modelling RevenueMarketSharebyType(2015-2020)4.3Global Molecular Modelling PriceMarketSharebyType(2015-2020)4.4Global Molecular Modelling MarketSharebyPriceTier(2015-2020):Low-End,Mid-RangeandHigh-End

5Global Molecular Modelling HistoricMarketAnalysisbyApplication5.1Global Molecular Modelling SalesMarketSharebyApplication(2015-2020)5.2Global Molecular Modelling RevenueMarketSharebyApplication(2015-2020)5.3Global Molecular Modelling PricebyApplication(2015-2020)

6CompanyProfilesandKeyFiguresin Molecular Modelling Business7 Molecular Modelling ManufacturingCostAnalysis8MarketingChannel,DistributorsandCustomers9MarketDynamics9.1MarketTrends9.2OpportunitiesandDrivers9.3Challenges9.4PortersFiveForcesAnalysis

10GlobalMarketForecast10.1Global Molecular Modelling MarketEstimatesandProjectionsbyType10.2 Molecular Modelling MarketEstimatesandProjectionsbyApplication10.3 Molecular Modelling MarketEstimatesandProjectionsbyRegion10.4NorthAmerica Molecular Modelling EstimatesandProjections(2021-2026)10.5Europe Molecular Modelling EstimatesandProjections(2021-2026)10.6AsiaPacific Molecular Modelling EstimatesandProjections(2021-2026)10.7LatinAmerica Molecular Modelling EstimatesandProjections(2021-2026)10.8MiddleEastandAfrica Molecular Modelling EstimatesandProjections(2021-2026)

11ResearchFindingandConclusion12MethodologyandDataSource

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