In vivo biosensing via tissue-localizable near-infrared-fluorescent single-walled carbon nanotubes

Single-walled carbon nanotubes are particularly attractive for biomedical applications, because they exhibit a fluorescent signal in a spectral region where there is minimal interference from biological media. Although single-walled carbon nanotubes have been used as highly sensitive detectors for various compounds, their use as in vivo biomarkers requires the simultaneous optimization of various parameters, including biocompatibility, molecular recognition, high fluorescence quantum efficiency and signal transduction. Here we show that a polyethylene glycol ligated copolymer stabilizes near-infrared-fluorescent single-walled carbon nanotubes sensors in solution, enabling intravenous injection into mice and the selective detection of local nitric oxide concentration with a detection limit of 1 µM. The half-life for liver retention is 4 h, with sensors clearing the lungs within 2 h after injection, thus avoiding a dominant route of in vivo nanotoxicology. After localization within the liver, it is possible to follow the transient inflammation using nitric oxide as a marker and signalling molecule. To this end, we also report a spatial-spectral imaging algorithm to deconvolute fluorescence intensity and spatial information from measurements. Finally, we demonstrate that alginate-encapsulated single-walled carbon nanotubes can function as implantable inflammation sensors for nitric oxide detection, with no intrinsic immune reactivity or other adverse response for more than 400 days.National Institutes of Health (U.S.) (T32 Training Grant in Environmental Toxicology ES007020)National Cancer Institute (U.S.) (Grant P01 CA26731)National Institute of Environmental Health Sciences (Grant P30 ES002109)Arnold and Mabel Beckman Foundation (Young Investigator Award)National Science Foundation (U.S.). Presidential Early Career Award for Scientists and EngineersScientific and Technological Research Council of Turkey (TUBITAK 2211 Research Fellowship Programme)Scientific and Technological Research Council of Turkey (TUBITAK 2214 Research Fellowship Programme)Middle East Technical University. Faculty Development ProgrammeSanofi Aventis (Firm) (Biomedical Innovation Grant

Extracting phylogenetic dimensions of coevolution reveals hidden functional signals

International audienceDespite the structural and functional information contained in the statistical coupling between pairs of residues in a protein, coevolution associated with function is often obscured by artifactual signals such as genetic drift, which shapes a protein’s phylogenetic history and gives rise to concurrent variation between protein sequences that is not driven by selection for function. Here, we introduce a background model for phylogenetic contributions of statistical coupling that separates the coevolution signal due to inter-clade and intra-clade sequence comparisons and demonstrate that coevolution can be measured on multiple phylogenetic timescales within a single protein. Our method, nested coevolution (NC), can be applied as an extension to any coevolution metric. We use NC to demonstrate that poorly conserved residues can nonetheless have important roles in protein function. Moreover, NC improved the structural-contact predictions of several coevolution-based methods, particularly in subsampled alignments with fewer sequences. NC also lowered the noise in detecting functional sectors of collectively coevolving residues. Sectors of coevolving residues identified after application of NC were more spatially compact and phylogenetically distinct from the rest of the protein, and strongly enriched for mutations that disrupt protein activity. Thus, our conceptualization of the phylogenetic separation of coevolution provides the potential to further elucidate relationships among protein evolution, function, and genetic diseases

Dataset 2

DIAUXIC GROWTH TIMECOURSE ON BC187 AND YJM978 (Figure 2, 5A-B, S4B-D, S5, S7C, S11C; Also used in figure 5C, S7B). Contains .fcs files of raw flow cytometry data and .csv files of sugar concentrations

Datasets 8-10

Dataset 8. ABSOLUTE COUNTING CONTROL (Figure S4A). Contains .fcs files of raw flow cytometry data. Dataset 9. LONG TIMECOURSE INDUCTION (Figure S8, S9B). Contains .fcs files of raw flow cytometry data. Dataset 10. PRE-CONDITION EFFECT ON INDUCTION KINETICS (Figure S10). Contains .fcs files of raw flow cytometry data

Data from: Natural variation in preparation for nutrient depletion reveals a cost-benefit tradeoff

Maximizing growth and survival in the face of a complex, time-varying environment is a common problem for single-celled organisms in the wild. When offered two different sugars as carbon sources, microorganisms first consume the preferred sugar, then undergo a transient growth delay, the “diauxic lag,” while inducing genes to metabolize the less preferred sugar. This delay is commonly assumed to be an inevitable consequence of selection to maximize use of the preferred sugar. Contrary to this view, we found that many natural isolates of Saccharomyces cerevisiae display short or nonexistent diauxic lags when grown in mixtures of glucose (preferred) and galactose. These strains induce galactose utilization (GAL) genes hours before glucose exhaustion, thereby “preparing” for the transition from glucose to galactose metabolism. The extent of preparation varies across strains, and seems to be determined by the steady-state response of GAL genes to mixtures of glucose and galactose rather than by induction kinetics. Although early GAL gene induction gives strains a competitive advantage once glucose runs out, it comes at a cost while glucose is still present. Costs and benefits correlate with the degree of preparation: strains with higher expression of GAL genes prior to glucose exhaustion experience a larger upfront growth cost but also a shorter diauxic lag. Our results show that classical diauxic growth is only one extreme on a continuum of growth strategies constrained by a cost–benefit tradeoff. This type of continuum is likely to be common in nature, as similar tradeoffs can arise whenever cells evolve to use mixtures of nutrients

Dataset 11

TIMELAPSE MICROSCOPY (Figure S13). Contains .tiff files of raw microscopy data

Dataset 5

STEADY-STATE FITNESS OF BC187 AND YJM978 (Figure 5C, S12). Contains .fcs files of raw flow cytometry data

Dataset 1

GROWTH CURVES (Figure 1, S1-3, S11A-B; Also used in figure 3, 4, 7C). Contains .csv files of raw OD600 readings from plate reader

Dataset 7

STEADY-STATE FITNESS OF MULTIPLE STRAINS (Figure 7, S14). Contains .fcs files of raw flow cytometry data

Dataset 3

DIAUXIC GROWTH TIMECOURSE ON MULTIPLE STRAINS (Figure 3, S7A-B,D-F,I, S9A). Contains .fcs files of raw flow cytometry data