Singlet Fission in a New Series of Systematically Designed Through-space Coupled Tetracene Oligomers

Singlet fission (SF) holds great promise for current photovoltaic technologies, where tetracenes, with their relatively high triplet energies, play a major role for application in silicon-based solar cells. However, the SF efficiencies in tetracene dimers are low due to the unfavorable energetics of their singlet and triplet energy levels. In the solid state, tetracene exhibits high yields of triplet formation through SF, raising great interest about the underlying mechanisms. To address this discrepancy, we designed and prepared a novel molecular system based on a hexaphenylbenzene core decorated with 2 to 6 tetracene chromophores. The spatial arrangement of tetracene units, induced by steric hindrance in the central part, dictates through-space coupling, making it a relevant model for solid-state chromophore organization. We then revealed a remarkable increase in SF quantum yield with the number of tetracenes, reaching quantitative (196 %) triplet pair formation in hexamer. We observed a short-lived correlated triplet pair and limited magnetic effects, indicating ineffective triplet dissociation in these through-space coupled systems. These findings emphasize the crucial role of the number of chromophores involved and the interchromophore arrangement for the SF efficiency. The insights gained from this study will aid designing more efficient and technology-compatible SF systems for applications in photovoltaics.

Community- and genome-based evidence for a shaping influence of redox potential on bacterial protein evolution

Despite deep interest in how environments shape microbial communities, whether redox conditions influence the sequence composition of genomes is not well known. We predicted that the carbon oxidation state (Z_C) of protein sequences would be positively correlated with redox potential (Eh). To test this prediction, we used taxonomic classifications for 68 publicly available 16S rRNA gene sequence data sets to estimate the abundances of archaeal and bacterial genomes in river & seawater, lake & pond, geothermal, hyperalkaline, groundwater, sediment, and soil environments. Locally, Z_C of community reference proteomes (i.e., all the protein sequences in each genome, weighted by taxonomic abundances but not by protein abundances) is positively correlated with Eh corrected to pH 7 (Eh7) for the majority of data sets for bacterial communities in each type of environment, and global-scale correlations are positive for bacterial communities in all environments. In contrast, archaeal communities show approximately equal frequencies of positive and negative correlations in individual data sets, and a positive pan-environmental correlation for archaea only emerges after limiting the analysis to samples with reported oxygen concentrations. These results provide empirical evidence that geochemistry modulates genome evolution and may have distinct effects on bacteria and archaea.IMPORTANCEThe identification of environmental factors that influence the elemental composition of proteins has implications for understanding microbial evolution and biogeography. Millions of years of genome evolution may provide a route for protein sequences to attain incomplete equilibrium with their chemical environment. We developed new tests of this chemical adaptation hypothesis by analyzing trends of the carbon oxidation state of community reference proteomes for microbial communities in local- and global-scale redox gradients. The results provide evidence for widespread environmental shaping of the elemental composition of protein sequences at the community level and establish a rationale for using thermodynamic models as a window into geochemical effects on microbial community assembly and evolution.

Application of particle swarm optimization to understand the mechanism of action of allosteric inhibitors of the enzyme HSD17β13

Understanding a drug candidate's mechanism of action is crucial for its further development. However, kinetic schemes are often complex and multi-parametric, especially for proteins in oligomerization equilibria. Here, we demonstrate the use of particle swarm optimization (PSO) as a method to select between different sets of parameters that are too far apart in the parameter space to be found by conventional approaches. PSO is based upon the swarming of birds: each bird in the flock assesses multiple landing spots while at the same time sharing that information with its neighbors. We applied this approach to the kinetics of HSD17β13 enzyme inhibitors, which displayed unusually large thermal shifts. Thermal shift data for HSD17β13 indicated that the inhibitor shifted the oligomerization equilibrium toward the dimeric state. Validation of the PSO approach was provided by experimental mass photometry data. These results encourage further exploration of multi-parameter optimization algorithms as tools in drug discovery.

Development of a ribosome profiling protocol to study translation in Kluyveromyces marxianus

Kluyveromyces marxianus is an interesting and important yeast because of particular traits such as thermotolerance and rapid growth, and for applications in food and industrial biotechnology. For both understanding its biology and developing bioprocesses, it is important to understand how K. marxianus responds and adapts to changing environments. For this, a full suite of omics tools to measure and compare global patterns of gene expression and protein synthesis is needed. We report here the development of a ribosome profiling method for K. marxianus, which allows codon resolution of translation on a genome-wide scale by deep sequencing of ribosome locations on mRNAs. To aid in the analysis and sharing of ribosome profiling data, we added the K. marxianus genome as well as transcriptome and ribosome profiling data to the publicly accessible GWIPS-viz and Trips-Viz browsers. Users are able to upload custom ribosome profiling and RNA-Seq data to both browsers, therefore allowing easy analysis and sharing of data. We also provide a set of step-by-step protocols for the experimental and bioinformatic methods that we developed.

Spatiotemporal trends in burden of uterine cancer and its attribution to body mass index in 204 countries and territories from 1990 to 2019

BACKGROUND

Uterine cancer is one of the most common female cancers worldwide, with huge heterogeneity in morbidity and mortality. Although a high body-mass index (BMI) has been linked to uterine cancer, systematic reports about the influence of high BMI and its temporal trends are scarce.

METHODS

The annual morbidity, mortality, and disability-adjusted life years (DALYs) of uterine cancer in 204 countries or territories were retrieved from the GBD 2019 study. To reflect trends in disease burden, we also calculated the estimated annual percentage change (EAPC) based on the age-standardized rates of uterine cancer from 1990 to 2019.

RESULTS

The global incident cases of uterine cancer increased 2.3 times from 187,190 in 1990 to 435,040 in 2019. Although the age-standardized incidence rate (ASIR) of uterine cancer increased worldwide from 8.67/100,000 in 1990 to 9.99/100,000 in 2019, the age-standardized death rate (ASDR) and DALY rate decreased during the same period. High socio-demographic index (SDI) countries tended to have a higher ASIR than developing regions, and their increasing trend in ASIR was also more pronounced. The disease was rare before 40 years old, but its risk rose sharply among women aged 50-70. A high BMI was linked to more than one-third of deaths from uterine cancer in 2019.

CONCLUSIONS

The incidence in developed areas was significantly higher than in developing areas and also increased much more rapidly. Elderly females, especially those with a high BMI, have a higher risk of uterine cancer. Therefore, more health resources may be needed to curb the rising burden in specific populations.

Ultrafast Excited-State Decay Mechanisms of 6-Thioguanine Followed by Sub-20 fs UV Transient Absorption Spectroscopy

Understanding the primary steps following UV photoexcitation in sulphur-substituted DNA bases (thiobases) is fundamental for developing new phototherapeutic drugs. However, the investigation of the excited-state dynamics in sub-100 fs time scales has been elusive until now due to technical challenges. Here, we track the ultrafast decay mechanisms that lead to the electron trapping in the triplet manifold for 6-thioguanine in an aqueous solution, using broadband transient absorption spectroscopy with a sub-20 fs temporal resolution. We obtain experimental evidence of the fast internal conversion from the S₂(ππ*) to the S₁(nπ*) states, which takes place in about 80 fs and demonstrates that the S₁(nπ*) state acts as a doorway to the triplet population in 522 fs. Our results are supported by MS-CASPT2 calculations, predicting a planar S₂(ππ*) pseudo-minimum in agreement with the stimulated emission signal observed in the experiment.

Exploring the Potential Relationship Between Global Greenness and DALY Loss Due to Depressive Disorders

OBJECTIVE

Prior studies have shown that greenness can reduce the burden of depressive disorders. However, most were focused on local-scale analyses while limited evaluated globally. We aimed to investigate the association between greenness and the burden of depressive disorders using data from 183 countries worldwide.

METHODS

We used the normalized difference vegetation index (NDVI) to estimate greenness. Country-level disability-adjusted life year (DALY) loss due to depressive disorders was used to represent depressive disorder burdens. A generalized linear mixed model was applied to assess the relationship between greenness and depressive disorders after controlling for covariates. Stratified analyses were conducted to determine the effects of greenness across several socio-demographic levels.

RESULTS

The findings showed a significant negative association between greenness and the health burden of depressive disorders with a coefficient of -0.196 (95% CI: -0.356, -0.035) in the DALY changes per interquartile unit increment of NDVI. The stratified analyses suggested beneficial effects of greenness on depressive disorders across sex, various age groups especially for those aged <49 years, with low-income and/or those living in highly urbanized countries.

CONCLUSIONS

Our study noted that greenness exposure was significant negative association with the burden of depressive disorders. The findings should be viewed as recommendations for relevant authorities in supporting environmental greenness enhancement to reduce the mental burdens.

Future Land Use for Insect Meat Production Among Countries: A Global Classification

A potentially suitable alternative to reduce land use by livestock production is insect meat production. However, land use predictions for insect meat production, which are important in the planning of food production strategies in each country, have not been well-performed. To consider the strategy of insect meat production with regard to land use, the categorical perspectives of countries would be highly useful. Here, using previous simulation results, we used random forest machine learning to classify the potential land use of 157 countries for insect meat production under future climate change. From the categorical maps, we showed the global distribution of five different country categories and found that the land area of the countries may be an important factor in considering the future increase in insect meat production. Our classification could be used to help formulate future food policies by considering the increase in insect meat production in each country, as well as regionally and/or globally.

Global Geographic and Temporal Analysis of SARS-CoV-2 Haplotypes Normalized by COVID-19 Cases During the Pandemic

Since the identification of SARS-CoV-2, a large number of genomes have been sequenced with unprecedented speed around the world. This marks a unique opportunity to analyze virus spreading and evolution in a worldwide context. Currently, there is not a useful haplotype description to help to track important and globally scattered mutations. Also, differences in the number of sequenced genomes between countries and/or months make it difficult to identify the emergence of haplotypes in regions where few genomes are sequenced but a large number of cases are reported. We propose an approach based on the normalization by COVID-19 cases of relative frequencies of mutations using all the available data to identify major haplotypes. Furthermore, we can use a similar normalization approach to tracking the temporal and geographic distribution of haplotypes in the world. Using 171,461 genomes, we identify five major haplotypes or operational taxonomic units (OTUs) based on nine high-frequency mutations. OTU_3 characterized by mutations R203K and G204R is currently the most frequent haplotype circulating in four of the six continents analyzed (South America, North America, Europe, Asia, Africa, and Oceania). On the other hand, during almost all months analyzed, OTU_5 characterized by the mutation T85I in nsp2 is the most frequent in North America. Recently (since September), OTU_2 has been established as the most frequent in Europe. OTU_1, the ancestor haplotype, is near to extinction showed by its low number of isolations since May. Also, we analyzed whether age, gender, or patient status is more related to a specific OTU. We did not find OTU's preference for any age group, gender, or patient status. Finally, we discuss structural and functional hypotheses in the most frequently identified mutations, none of those mutations show a clear effect on the transmissibility or pathogenicity.

Responses of the Pheromone-Binding Protein of the Silk Moth Bombyx mori on a Graphene Biosensor Match Binding Constants in Solution

An electronic biosensor for odors was assembled by immobilizing the silk moth Bombyx mori pheromone binding protein (BmorPBP1) on a reduced graphene oxide surface of a field-effect transistor. At physiological pH, the sensor detects the B. mori pheromones, bombykol and bombykal, with good affinity and specificity. Among the other odorants tested, only eugenol elicited a strong signal, while terpenoids and other odorants (linalool, geraniol, isoamyl acetate, and 2-isobutyl-3-methoxypyrazine) produced only very weak responses. Parallel binding assays were performed with the same protein and the same ligands, using the common fluorescence approach adopted for similar proteins. The results are in good agreement with the sensor’s responses: bombykol and bombykal, together with eugenol, proved to be strong ligands, while the other compounds showed only poor affinity. When tested at pH 4, the protein failed to bind bombykol both in solution and when immobilized on the sensor. This result further indicates that the BmorPBP1 retains its full activity when immobilized on a surface, including the conformational change observed in acidic conditions. The good agreement between fluorescence assays and sensor responses suggests that ligand-binding assays in solution can be used to screen mutants of a binding protein when selecting the best form to be immobilized on a biosensor.

Global perspectives on the three criteria for premature ejaculation: An observational study of ejaculatory latency, ejaculatory control and bother/distress

Criteria for premature ejaculation (PE) were established using Western-based samples, yet these criteria are applied worldwide for its diagnosis. This study (a) determined whether men from various world regions differ/agree on their views of ejaculation latency (ELT) and their perceptions of ejaculatory control and bother/distress, the three criteria for PE, and (b) compared PE and non-PE men across worldwide regions on these measures. 1,065 participants were recruited via social media to respond to a survey about men's typical, ideal and PE ELTs, about their own ELT, and about perceptions of ejaculatory control and bother/distress related to PE. Responses from men from four worldwide regions were compared to a reference group of North American/European men, and PE men were compared with non-PE men across three world regions. Results showed that most world region groups showed similarity in ELT estimations. The Sub-Saharan group focused more heavily on the importance of ejaculatory control. Both ELT and ejaculatory control differed between PE and non-PE groups in all regions assessed. In conclusion, perceived ELTs and ejaculatory control show substantial consistency across world regions despite geo-cultural variations and traditions. Such findings argue for the universality of the concepts of ELT, control and bother/distress related to PE.

Assessing global Sentinel-2 coverage dynamics and data availability for operational Earth observation (EO) applications using the EO-Compass

Sentinel-2 scenes are increasingly being used in operational Earth observation (EO) applications at regional, continental and global scales, in near-real time applications, and with multi-temporal approaches. On a broader scale, they are therefore one of the most important facilitators of the Digital Earth. However, the data quality and availability are not spatially and temporally homogeneous due to effects related to cloudiness, the position on the Earth or the acquisition plan. The spatio-temporal inhomogeneity of the underlying data may therefore affect any big remote sensing analysis and is important to consider. This study presents an assessment of the metadata for all accessible Sentinel-2 Level-1C scenes acquired in 2017, enabling the spatio-temporal coverage and availability to be quantified, including scene availability and cloudiness. Spatial exploratory analysis of the global, multi-temporal metadata also reveals that higher acquisition frequencies do not necessarily yield more cloud-free scenes and exposes metadata quality issues, e.g. systematically incorrect cloud cover estimation in high, non-vegetated altitudes. The continuously updated datasets and analysis results are accessible as a Web application called EO-Compass. It contributes to a better understanding and selection of Sentinel-2 scenes, and improves the planning and interpretation of remote sensing analyses.

In Search of Biomarkers for Pathogenesis and Control of Leishmaniasis by Global Analyses of Leishmania-Infected Macrophages

Leishmaniasis is a vector-borne, neglected tropical disease with a worldwide distribution that can present in a variety of clinical forms, depending on the parasite species and host genetic background. The pathogenesis of this disease remains far from being elucidated because the involvement of a complex immune response orchestrated by host cells significantly affects the clinical outcome. Among these cells, macrophages are the main host cells, produce cytokines and chemokines, thereby triggering events that contribute to the mediation of the host immune response and, subsequently, to the establishment of infection or, alternatively, disease control. There has been relatively limited commercial interest in developing new pharmaceutical compounds to treat leishmaniasis. Moreover, advances in the understanding of the underlying biology of Leishmania spp. have not translated into the development of effective new chemotherapeutic compounds. As a result, biomarkers as surrogate disease endpoints present several potential advantages to be used in the identification of targets capable of facilitating therapeutic interventions considered to ameliorate disease outcome. More recently, large-scale genomic and proteomic analyses have allowed the identification and characterization of the pathways involved in the infection process in both parasites and the host, and these analyses have been shown to be more effective than studying individual molecules to elucidate disease pathogenesis. RNA-seq and proteomics are large-scale approaches that characterize genes or proteins in a given cell line, tissue, or organism to provide a global and more integrated view of the myriad biological processes that occur within a cell than focusing on an individual gene or protein. Bioinformatics provides us with the means to computationally analyze and integrate the large volumes of data generated by high-throughput sequencing approaches. The integration of genomic expression and proteomic data offers a rich multi-dimensional analysis, despite the inherent technical and statistical challenges. We propose that these types of global analyses facilitate the identification, among a large number of genes and proteins, those that hold potential as biomarkers. The present review focuses on large-scale studies that have identified and evaluated relevant biomarkers in macrophages in response to Leishmania infection.

Primary processes in the bacterial reaction center probed by two-dimensional electronic spectroscopy

In the initial steps of photosynthesis, reaction centers convert solar energy to stable charge-separated states with near-unity quantum efficiency. The reaction center from purple bacteria remains an important model system for probing the structure-function relationship and understanding mechanisms of photosynthetic charge separation. Here we perform 2D electronic spectroscopy (2DES) on bacterial reaction centers (BRCs) from two mutants of the purple bacterium Rhodobacter capsulatus, spanning the Q _y absorption bands of the BRC. We analyze the 2DES data using a multiexcitation global-fitting approach that employs a common set of basis spectra for all excitation frequencies, incorporating inputs from the linear absorption spectrum and the BRC structure. We extract the exciton energies, resolving the previously hidden upper exciton state of the special pair. We show that the time-dependent 2DES data are well-represented by a two-step sequential reaction scheme in which charge separation proceeds from the excited state of the special pair (P*) to P⁺H_A^- via the intermediate P⁺B_A^- When inhomogeneous broadening and Stark shifts of the B* band are taken into account we can adequately describe the 2DES data without the need to introduce a second charge-separation pathway originating from the excited state of the monomeric bacteriochlorophyll B_A*.

Global Analysis of miRNA-mRNA Interaction Network in Breast Cancer with Brain Metastasis

BACKGROUND

MicroRNAs (miRNAs) have been linked to a number of cancer types including breast cancer. The rate of brain metastases is 10-30% in patients with advanced breast cancer which is associated with poor prognosis. The potential application of miRNAs in the diagnostics and therapeutics of breast cancer with brain metastasis is an area of intense interest. In an initial effort to systematically address the differential expression of miRNAs and mRNAs in primary breast cancer which may provide clues for early detection of brain metastasis, we analyzed the consequent changes in global patterns of gene expression in Gene Expression Omnibus (GEO) data set obtained by microarray from patients with in situ carcinoma and patients with brain metastasis.

MATERIALS AND METHODS

The miRNA-pathway regulatory network and miRNA-mRNA regulatory network were investigated in breast cancer specimens from patients with brain metastasis to screen for significantly dysregulated miRNAs followed by prediction of their target genes and pathways by Gene Ontology (GO) analysis.

RESULTS

Functional coordination of the changes of gene expression can be modulated by individual miRNAs. Two miRNAs, hsa-miR-17-5p and hsa-miR-16-5p, were identified as having the highest associations with targeted mRNAs [such as B-cell lymphoma 2 (BCL2), small body size/mothers against decapentaplegic 3 (SMAD3) and suppressor of cytokine signaling 1 (SOCS1)] and pathways associated with epithelial-mesenchymal transitions and other processes linked with cancer metastasis (including cell cycle, adherence junctions and extracellular matrix-receptor interaction). mRNAs for two genes [HECT, UBA and WWE domain containing 1 (HUWE1) and BCL2] were found to have the highest associations with miRNAs, which were down-regulated in brain metastasis specimens of breast cancer. The change of 11 selected miRNAs was verified in The Cancer Genome Atlas (TCGA) breast cancer dataset. Up-regulation of hsa-miR-17-5p was detected in triple-negative breast cancer tissues in TCGA. Furthermore, a negative correlation of hsa-miR-17-5p with overall survival and phosphatase and tensin homolog (PTEN) and BCL2 target genes was found in TCGA breast cancer specimens.

CONCLUSION

Our findings provide a functionally coordinated expression pattern of different families of miRNAs that may have potential to provide clinicians with a strategy to treat breast cancer with brain metastasis from a systems-rather than a single-gene perspective.

Origin of Analyte-Induced Porous Silicon Photoluminescence Quenching

We report on gaseous analyte-induced photoluminescence (PL) quenching of porous silicon, as-prepared (ap-pSi) and oxidized (ox-pSi). By using steady-state and emission wavelength-dependent time-resolved intensity luminescence measurements in concert with a global analysis scheme, we find that the analyte-induced quenching is best described by a three-component static quenching model. In the model, there are blue, green, and red emitters (associated with the nanocrystallite core and surface trap states) that each exhibit unique analyte-emitter association constants and these association constants are a consequence of differences in the pSi surface chemistries.

Noise-Corrected Principal Component Analysis of fluorescence lifetime imaging data

Fluorescence Lifetime Imaging (FLIM) is an attractive microscopy method in the life sciences, yielding information on the sample otherwise unavailable through intensity-based techniques. A novel Noise-Corrected Principal Component Analysis (NC-PCA) method for time-domain FLIM data is presented here. The presence and distribution of distinct microenvironments are identified at lower photon counts than previously reported, without requiring prior knowledge of their number or of the dye's decay kinetics. A noise correction based on the Poisson statistics inherent to Time-Correlated Single Photon Counting is incorporated. The approach is validated using simulated data, and further applied to experimental FLIM data of HeLa cells stained with membrane dye di-4-ANEPPDHQ. Two distinct lipid phases were resolved in the cell membranes, and the modification of the order parameters of the plasma membrane during cholesterol depletion was also detected. Noise-corrected Principal Component Analysis of FLIM data resolves distinct microenvironments in cell membranes of live HeLa cells.

Using Global Analysis to Extend the Accuracy and Precision of Binding Measurements with T cell Receptors and Their Peptide/MHC Ligands

In cellular immunity, clonally distributed T cell receptors (TCRs) engage complexes of peptides bound to major histocompatibility complex proteins (pMHCs). In the interactions of TCRs with pMHCs, regions of restricted and variable diversity align in a structurally complex fashion. Many studies have used mutagenesis to attempt to understand the "roles" played by various interface components in determining TCR recognition properties such as specificity and cross-reactivity. However, these measurements are often complicated or even compromised by the weak affinities TCRs maintain toward pMHC. Here, we demonstrate how global analysis of multiple datasets can be used to significantly extend the accuracy and precision of such TCR binding experiments. Application of this approach should positively impact efforts to understand TCR recognition and facilitate the creation of mutational databases to help engineer TCRs with tuned molecular recognition properties. We also show how global analysis can be used to analyze double mutant cycles in TCR-pMHC interfaces, which can lead to new insights into immune recognition.

Integration and global analysis of isothermal titration calorimetry data for studying macromolecular interactions

Isothermal titration calorimetry (ITC) is a powerful and widely used method to measure the energetics of macromolecular interactions by recording a thermogram of differential heating power during a titration. However, traditional ITC analysis is limited by stochastic thermogram noise and by the limited information content of a single titration experiment. Here we present a protocol for bias-free thermogram integration based on automated shape analysis of the injection peaks, followed by combination of isotherms from different calorimetric titration experiments into a global analysis, statistical analysis of binding parameters and graphical presentation of the results. This is performed using the integrated public-domain software packages NITPIC, SEDPHAT and GUSSI. The recently developed low-noise thermogram integration approach and global analysis allow for more precise parameter estimates and more reliable quantification of multisite and multicomponent cooperative and competitive interactions. Titration experiments typically take 1-2.5 h each, and global analysis usually takes 10-20 min.

Improving ITC studies of cyclodextrin inclusion compounds by global analysis of conventional and non-conventional experiments

The study of 1:1 cyclodextrin inclusion compounds by isothermal titration calorimetry was explored in a theoretical and experimental point of view to compare the efficiency of conventional and non-conventional experiments. All direct and competitive protocols were described and evaluated in terms of accuracy on both binding constant and inclusion enthalpy. Significant improvement in the calorimetric characterization may be obtained by means of the global analysis of non-conventional experiments coupled to the standard titration protocol. While the titration-release approach proved to be the most accurate strategy for classical complexations, the valuable contribution of other non-conventional experiments was demonstrated for issues concerning weak stability, enthalpy, or solubility.

Increasing homogeneity in global food supplies and the implications for food security

The narrowing of diversity in crop species contributing to the world's food supplies has been considered a potential threat to food security. However, changes in this diversity have not been quantified globally. We assess trends over the past 50 y in the richness, abundance, and composition of crop species in national food supplies worldwide. Over this period, national per capita food supplies expanded in total quantities of food calories, protein, fat, and weight, with increased proportions of those quantities sourcing from energy-dense foods. At the same time the number of measured crop commodities contributing to national food supplies increased, the relative contribution of these commodities within these supplies became more even, and the dominance of the most significant commodities decreased. As a consequence, national food supplies worldwide became more similar in composition, correlated particularly with an increased supply of a number of globally important cereal and oil crops, and a decline of other cereal, oil, and starchy root species. The increase in homogeneity worldwide portends the establishment of a global standard food supply, which is relatively species-rich in regard to measured crops at the national level, but species-poor globally. These changes in food supplies heighten interdependence among countries in regard to availability and access to these food sources and the genetic resources supporting their production, and give further urgency to nutrition development priorities aimed at bolstering food security.

Analytical Ultracentrifugation as a Tool for Studying Protein Interactions

The last two decades have led to significant progress in the field of analytical ultracentrifugation driven by instrumental, theoretical, and computational methods. This review will highlight key developments in sedimentation equilibrium (SE) and sedimentation velocity (SV) analysis. For SE, this includes the analysis of tracer sedimentation equilibrium at high concentrations with strong thermodynamic non-ideality, and for ideally interacting systems the development of strategies for the analysis of heterogeneous interactions towards global multi-signal and multi-speed SE analysis with implicit mass conservation. For SV, this includes the development and applications of numerical solutions of the Lamm equation, noise decomposition techniques enabling direct boundary fitting, diffusion deconvoluted sedimentation coefficient distributions, and multi-signal sedimentation coefficient distributions. Recently, effective particle theory has uncovered simple physical rules for the co-migration of rapidly exchanging systems of interacting components in SV. This has opened new possibilities for the robust interpretation of the boundary patterns of heterogeneous interacting systems. Together, these SE and SV techniques have led to new approaches to study macromolecular interactions across the entire the spectrum of affinities, including both attractive and repulsive interactions, in both dilute and highly concentrated solutions, which can be applied to single-component solutions of self-associating proteins as well as the study of multi-protein complex formation in multi-component solutions.

Global multi-method analysis of affinities and cooperativity in complex systems of macromolecular interactions

Cooperativity, multisite, and multicomponent interactions are hallmarks of biological systems of interacting macromolecules. Their thermodynamic characterization is often very challenging due to the notoriously low information content of binding isotherms. We introduce a strategy for the global multimethod analysis of data from multiple techniques (GMMA) that exploits enhanced information content emerging from the mutual constraints of the simultaneous modeling of orthogonal observables from calorimetric, spectroscopic, hydrodynamic, biosensing, or other thermodynamic binding experiments. We describe new approaches to address statistical problems that arise in the analysis of dissimilar data sets. The GMMA approach can significantly increase the complexity of interacting systems that can be accurately thermodynamically characterized.

Strategies for assessing proton linkage to bimolecular interactions by global analysis of isothermal titration calorimetry data

Isothermal titration calorimetry (ITC) is a traditional and powerful method for studying the linkage of ligand binding to proton uptake or release. The theoretical framework has been developed for more than two decades and numerous applications have appeared. In the current work, we explored strategic aspects of experimental design. To this end, we simulated families of ITC data sets that embed different strategies with regard to the number of experiments, range of experimental pH, buffer ionization enthalpy, and temperature. We then re-analyzed the families of data sets in the context of global analysis, employing a proton linkage binding model implemented in the global data analysis platform SEDPHAT, and examined the information content of all data sets by a detailed statistical error analysis of the parameter estimates. In particular, we studied the impact of different assumptions about the knowledge of the exact concentrations of the components, which in practice presents an experimental limitation for many systems. For example, the uncertainty in concentration may reflect imperfectly known extinction coefficients and stock concentrations or may account for different extents of partial inactivation when working with proteins at different pH values. Our results show that the global analysis can yield reliable estimates of the thermodynamic parameters for intrinsic binding and protonation, and that in the context of the global analysis the exact molecular component concentrations may not be required. Additionally, a comparison of data from different experimental strategies illustrates the benefit of conducting experiments at a range of temperatures.

The global analysis of DEER data

Double Electron-Electron Resonance (DEER) has emerged as a powerful technique for measuring long range distances and distance distributions between paramagnetic centers in biomolecules. This information can then be used to characterize functionally relevant structural and dynamic properties of biological molecules and their macromolecular assemblies. Approaches have been developed for analyzing experimental data from standard four-pulse DEER experiments to extract distance distributions. However, these methods typically use an a priori baseline correction to account for background signals. In the current work an approach is described for direct fitting of the DEER signal using a model for the distance distribution which permits a rigorous error analysis of the fitting parameters. Moreover, this approach does not require a priori background correction of the experimental data and can take into account excluded volume effects on the background signal when necessary. The global analysis of multiple DEER data sets is also demonstrated. Global analysis has the potential to provide new capabilities for extracting distance distributions and additional structural parameters in a wide range of studies.

Methodological considerations for global analysis of cellular FLIM/FRET measurements

Global algorithms can improve the analysis of fluorescence energy transfer (FRET) measurement based on fluorescence lifetime microscopy. However, global analysis of FRET data is also susceptible to experimental artifacts. This work examines several common artifacts and suggests remedial experimental protocols. Specifically, we examined the accuracy of different methods for instrument response extraction and propose an adaptive method based on the mean lifetime of fluorescent proteins. We further examined the effects of image segmentation and a priori constraints on the accuracy of lifetime extraction. Methods to test the applicability of global analysis on cellular data are proposed and demonstrated. The accuracy of global fitting degrades with lower photon count. By systematically tracking the effect of the minimum photon count on lifetime and FRET prefactors when carrying out global analysis, we demonstrate a correction procedure to recover the correct FRET parameters, allowing us to obtain protein interaction information even in dim cellular regions with photon counts as low as 100 per decay curve.

Analysis of cooperativity by isothermal titration calorimetry

Cooperative binding pervades Nature. This review discusses the use of isothermal titration calorimetry (ITC) in the identification and characterisation of cooperativity in biological interactions. ITC has broad scope in the analysis of cooperativity as it determines binding stiochiometries, affinities and thermodynamic parameters, including enthalpy and entropy in a single experiment. Examples from the literature are used to demonstrate the applicability of ITC in the characterisation of cooperative systems.

Ultrafast Method for the Analysis of Fluorescence Lifetime Imaging Microscopy Data Based on the Laguerre Expansion Technique

We report a new deconvolution method for fluorescence lifetime imaging microscopy (FLIM) based on the Laguerre expansion technique. The performance of this method was tested on synthetic and real FLIM images. The following interesting properties of this technique were demonstrated. 1) The fluorescence intensity decay can be estimated simultaneously for all pixels, without a priori assumption of the decay functional form. 2) The computation speed is extremely fast, performing at least two orders of magnitude faster than current algorithms. 3) The estimated maps of Laguerre expansion coefficients provide a new domain for representing FLIM information. 4) The number of images required for the analysis is relatively small, allowing reduction of the acquisition time. These findings indicate that the developed Laguerre expansion technique for FLIM analysis represents a robust and extremely fast deconvolution method that enables practical applications of FLIM in medicine, biology, biochemistry, and chemistry.