Genetic risk converges on regulatory networks mediating early type 2 diabetes.

Type 2 diabetes mellitus (T2D), a major cause of worldwide morbidity and mortality, is characterized by dysfunction of insulin-producing pancreatic islet ß cells1,2. T2D genome-wide association studies (GWAS) have identified hundreds of signals in non-coding and ß cell regulatory genomic regions, but deciphering their biological mechanisms remains challenging3-5. Here, to identify early disease-driving events, we performed traditional and multiplexed pancreatic tissue imaging, sorted-islet cell transcriptomics and islet functional analysis of early-stage T2D and control donors. By integrating diverse modalities, we show that early-stage T2D is characterized by ß cell-intrinsic defects that can be proportioned into gene regulatory modules with enrichment in signals of genetic risk. After identifying the ß cell hub gene and transcription factor RFX6 within one such module, we demonstrated multiple layers of genetic risk that converge on an RFX6-mediated network to reduce insulin secretion by ß cells. RFX6 perturbation in primary human islet cells alters ß cell chromatin architecture at regions enriched for T2D GWAS signals, and population-scale genetic analyses causally link genetically predicted reduced RFX6 expression with increased T2D risk. Understanding the molecular mechanisms of complex, systemic diseases necessitates integration of signals from multiple molecules, cells, organs and individuals, and thus we anticipate that this approach will be a useful template to identify and validate key regulatory networks and master hub genes for other diseases or traits using GWAS data.

Genetic effects on liver chromatin accessibility identify disease regulatory variants.

Identifying the molecular mechanisms by which genome-wide association study (GWAS) loci influence traits remains challenging. Chromatin accessibility quantitative trait loci (caQTLs) help identify GWAS loci that may alter GWAS traits by modulating chromatin structure, but caQTLs have been identified in a limited set of human tissues. Here we mapped caQTLs in human liver tissue in 20 liver samples and identified 3,123 caQTLs. The caQTL variants are enriched in liver tissue promoter and enhancer states and frequently disrupt binding motifs of transcription factors expressed in liver. We predicted target genes for 861 caQTL peaks using proximity, chromatin interactions, correlation with promoter accessibility or gene expression, and colocalization with expression QTLs. Using GWAS signals for 19 liver function and/or cardiometabolic traits, we identified 110 colocalized caQTLs and GWAS signals, 56 of which contained a predicted caPeak target gene. At the LITAF LDL-cholesterol GWAS locus, we validated that a caQTL variant showed allelic differences in protein binding and transcriptional activity. These caQTLs contribute to the epigenomic characterization of human liver and help identify molecular mechanisms and genes at GWAS loci.

Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits.

Skeletal muscle accounts for the largest proportion of human body mass, on average, and is a key tissue in complex diseases and mobility. It is composed of several different cell and muscle fiber types. Here, we optimize single-nucleus ATAC-seq (snATAC-seq) to map skeletal muscle cell-specific chromatin accessibility landscapes in frozen human and rat samples, and single-nucleus RNA-seq (snRNA-seq) to map cell-specific transcriptomes in human. We additionally perform multi-omics profiling (gene expression and chromatin accessibility) on human and rat muscle samples. We capture type I and type II muscle fiber signatures, which are generally missed by existing single-cell RNA-seq methods. We perform cross-modality and cross-species integrative analyses on 33,862 nuclei and identify seven cell types ranging in abundance from 59.6% to 1.0% of all nuclei. We introduce a regression-based approach to infer cell types by comparing transcription start site-distal ATAC-seq peaks to reference enhancer maps and show consistency with RNA-based marker gene cell type assignments. We find heterogeneity in enrichment of genetic variants linked to complex phenotypes from the UK Biobank and diabetes genome-wide association studies in cell-specific ATAC-seq peaks, with the most striking enrichment patterns in muscle mesenchymal stem cells (∼3.5% of nuclei). Finally, we overlay these chromatin accessibility maps on GWAS data to nominate causal cell types, SNPs, transcription factor motifs, and target genes for type 2 diabetes signals. These chromatin accessibility profiles for human and rat skeletal muscle cell types are a useful resource for nominating causal GWAS SNPs and cell types.

BiVO4 Photoanodes Enhanced with Metal Phosphide Co-Catalysts: Relevant Properties to Boost Photoanode Performance.

Achieving highly performant photoanodes for oxygen evolution is key to developing photoelectrochemical devices for solar water splitting. In this work, BiVO4 photoanodes are enhanced with a series of core-shell structured bimetallic nickel-cobalt phosphides (MPs), and key insights into the role of co-catalysts are provided. The best BiVO4 /Ni1.5 Co0.5 P and BiVO4 /Ni0.5 Co1.5 P photoanodes achieve a 3.5-fold increase in photocurrent compared with bare BiVO4 . It is discovered that this enhanced performance arises from a synergy between work function, catalytic activity, and capacitive ability of the MPs. Distribution of relaxation times analysis reveals that the contact between the MPs, BiVO4 , and the electrolyte gives rise to three routes for hole injection into the electrolyte, all of which are significantly improved by the presence of a second metal cation in the co-catalyst. Kinetic studies demonstrate that the significantly improved interfacial charge injection is due to a lower charge-transfer resistance, enhanced oxygen-evolution reaction kinetics, and larger surface hole concentrations, providing deeper insights into the carrier dynamics in these photoanode/co-catalyst systems for their rational design.

Location of Artinite (Mg2CO3(OH)2·3H2O) within the MgO-CO2-H2O system using ab initio thermodynamics.

The MgO-CO2-H2O system have a variety of important industrial applications including in catalysis, immobilisation of radionuclides and heavy metals, construction, and mineralisation and permanent storage of anthropogenic CO2. Here, we develop a computational approach to generate phase stability plots for the MgO-CO2-H2O system that do not rely on traditional experimental corrections for the solid phases. We compare the predictions made by several dispersion-corrected density-functional theory schemes, and we include the temperature-dependent Gibbs free energy through the quasi-harmonic approximation. We locate the Artinite phase (Mg2CO3(OH)2·3H2O) within the MgO-CO2-H2O phase stability plot, and we demonstrate that this widely-overlooked hydrated and carbonated phase is metastable and can be stabilised by inhibiting the formation of fully-carbonated stable phases. Similar considerations may apply more broadly to other lesser known phases. These findings provide new insight to explain conflicting results from experimental studies, and demonstrate how this phase can potentially be stabilised by optimising the synthesis conditions.

The genetic architecture of type 2 diabetes.

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups. To increase statistical power, we expanded the sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes.

Integrative analysis of gene expression, DNA methylation, physiological traits, and genetic variation in human skeletal muscle.

We integrate comeasured gene expression and DNA methylation (DNAme) in 265 human skeletal muscle biopsies from the FUSION study with >7 million genetic variants and eight physiological traits: height, waist, weight, waist-hip ratio, body mass index, fasting serum insulin, fasting plasma glucose, and type 2 diabetes. We find hundreds of genes and DNAme sites associated with fasting insulin, waist, and body mass index, as well as thousands of DNAme sites associated with gene expression (eQTM). We find that controlling for heterogeneity in tissue/muscle fiber type reduces the number of physiological trait associations, and that long-range eQTMs (>1 Mb) are reduced when controlling for tissue/muscle fiber type or latent factors. We map genetic regulators (quantitative trait loci; QTLs) of expression (eQTLs) and DNAme (mQTLs). Using Mendelian randomization (MR) and mediation techniques, we leverage these genetic maps to predict 213 causal relationships between expression and DNAme, approximately two-thirds of which predict methylation to causally influence expression. We use MR to integrate FUSION mQTLs, FUSION eQTLs, and GTEx eQTLs for 48 tissues with genetic associations for 534 diseases and quantitative traits. We identify hundreds of genes and thousands of DNAme sites that may drive the reported disease/quantitative trait genetic associations. We identify 300 gene expression MR associations that are present in both FUSION and GTEx skeletal muscle and that show stronger evidence of MR association in skeletal muscle than other tissues, which may partially reflect differences in power across tissues. As one example, we find that increased RXRA muscle expression may decrease lean tissue mass.

Multi-omic profiling of pituitary thyrotropic cells and progenitors.

BACKGROUND: The pituitary gland is a neuroendocrine organ containing diverse cell types specialized in secreting hormones that regulate physiology. Pituitary thyrotropes produce thyroid-stimulating hormone (TSH), a critical factor for growth and maintenance of metabolism. The transcription factors POU1F1 and GATA2 have been implicated in thyrotrope fate, but the transcriptomic and epigenomic landscapes of these neuroendocrine cells have not been characterized. The goal of this work was to discover transcriptional regulatory elements that drive thyrotrope fate. RESULTS: We identified the transcription factors and epigenomic changes in chromatin that are associated with differentiation of POU1F1-expressing progenitors into thyrotropes using cell lines that represent an undifferentiated Pou1f1 lineage progenitor (GHF-T1) and a committed thyrotrope line that produces TSH (TαT1). We compared RNA-seq, ATAC-seq, histone modification (H3K27Ac, H3K4Me1, and H3K27Me3), and POU1F1 binding in these cell lines. POU1F1 binding sites are commonly associated with bZIP transcription factor consensus binding sites in GHF-T1 cells and Helix-Turn-Helix (HTH) or basic Helix-Loop-Helix (bHLH) factors in TαT1 cells, suggesting that these classes of transcription factors may recruit or cooperate with POU1F1 binding at unique sites. We validated enhancer function of novel elements we mapped near Cga, Pitx1, Gata2, and Tshb by transfection in TαT1 cells. Finally, we confirmed that an enhancer element near Tshb can drive expression in thyrotropes of transgenic mice, and we demonstrate that GATA2 enhances Tshb expression through this element. CONCLUSION: These results extend the ENCODE multi-omic profiling approach to the pituitary gland, which should be valuable for understanding pituitary development and disease pathogenesis.

Genome-wide chromatin accessibility and transcriptome profiling show minimal epigenome changes and coordinated transcriptional dysregulation of hedgehog signaling in Danforth's short tail mice.

Danforth's short tail (Sd) mice provide an excellent model for investigating the underlying etiology of human caudal birth defects, which affect 1 in 10 000 live births. Sd animals exhibit aberrant axial skeleton, urogenital and gastrointestinal development similar to human caudal malformation syndromes including urorectal septum malformation, caudal regression, vertebral-anal-cardiac-tracheo-esophageal fistula-renal-limb (VACTERL) association and persistent cloaca. Previous studies have shown that the Sd mutation results from an endogenous retroviral (ERV) insertion upstream of the Ptf1a gene resulting in its ectopic expression at E9.5. Though the genetic lesion has been determined, the resulting epigenomic and transcriptomic changes driving the phenotype have not been investigated. Here, we performed ATAC-seq experiments on isolated E9.5 tailbud tissue, which revealed minimal changes in chromatin accessibility in Sd/Sd mutant embryos. Interestingly, chromatin changes were localized to a small interval adjacent to the Sd ERV insertion overlapping a known Ptf1a enhancer region, which is conserved in mice and humans. Furthermore, mRNA-seq experiments revealed increased transcription of Ptf1a target genes and, importantly, downregulation of hedgehog pathway genes. Reduced sonic hedgehog (SHH) signaling was confirmed by in situ hybridization and immunofluorescence suggesting that the Sd phenotype results, in part, from downregulated SHH signaling. Taken together, these data demonstrate substantial transcriptome changes in the Sd mouse, and indicate that the effect of the ERV insertion on Ptf1a expression may be mediated by increased chromatin accessibility at a conserved Ptf1a enhancer. We propose that human caudal dysgenesis disorders may result from dysregulation of hedgehog signaling pathways.

A Common Type 2 Diabetes Risk Variant Potentiates Activity of an Evolutionarily Conserved Islet Stretch Enhancer and Increases C2CD4A and C2CD4B Expression.

Genome-wide association studies (GWASs) and functional genomics approaches implicate enhancer disruption in islet dysfunction and type 2 diabetes (T2D) risk. We applied genetic fine-mapping and functional (epi)genomic approaches to a T2D- and proinsulin-associated 15q22.2 locus to identify a most likely causal variant, determine its direction of effect, and elucidate plausible target genes. Fine-mapping and conditional analyses of proinsulin levels of 8,635 non-diabetic individuals from the METSIM study support a single association signal represented by a cluster of 16 strongly associated (p < 10-17) variants in high linkage disequilibrium (r2 > 0.8) with the GWAS index SNP rs7172432. These variants reside in an evolutionarily and functionally conserved islet and ß cell stretch or super enhancer; the most strongly associated variant (rs7163757, p = 3 × 10-19) overlaps a conserved islet open chromatin site. DNA sequence containing the rs7163757 risk allele displayed 2-fold higher enhancer activity than the non-risk allele in reporter assays (p < 0.01) and was differentially bound by ß cell nuclear extract proteins. Transcription factor NFAT specifically potentiated risk-allele enhancer activity and altered patterns of nuclear protein binding to the risk allele in vitro, suggesting that it could be a factor mediating risk-allele effects. Finally, the rs7163757 proinsulin-raising and T2D risk allele (C) was associated with increased expression of C2CD4B, and possibly C2CD4A, both of which were induced by inflammatory cytokines, in human islets. Together, these data suggest that rs7163757 contributes to genetic risk of islet dysfunction and T2D by increasing NFAT-mediated islet enhancer activity and modulating C2CD4B, and possibly C2CD4A, expression in (patho)physiologic states.

Super-enhancers delineate disease-associated regulatory nodes in T cells.

Enhancers regulate spatiotemporal gene expression and impart cell-specific transcriptional outputs that drive cell identity. Super-enhancers (SEs), also known as stretch-enhancers, are a subset of enhancers especially important for genes associated with cell identity and genetic risk of disease. CD4(+) T cells are critical for host defence and autoimmunity. Here we analysed maps of mouse T-cell SEs as a non-biased means of identifying key regulatory nodes involved in cell specification. We found that cytokines and cytokine receptors were the dominant class of genes exhibiting SE architecture in T cells. Nonetheless, the locus encoding Bach2, a key negative regulator of effector differentiation, emerged as the most prominent T-cell SE, revealing a network in which SE-associated genes critical for T-cell biology are repressed by BACH2. Disease-associated single-nucleotide polymorphisms for immune-mediated disorders, including rheumatoid arthritis, were highly enriched for T-cell SEs versus typical enhancers or SEs in other cell lineages. Intriguingly, treatment of T cells with the Janus kinase (JAK) inhibitor tofacitinib disproportionately altered the expression of rheumatoid arthritis risk genes with SE structures. Together, these results indicate that genes with SE architecture in T cells encompass a variety of cytokines and cytokine receptors but are controlled by a 'guardian' transcription factor, itself endowed with an SE. Thus, enumeration of SEs allows the unbiased determination of key regulatory nodes in T cells, which are preferentially modulated by pharmacological intervention.

The Atomic-Level Structure of Cementitious Calcium Aluminate Silicate Hydrate.

Despite use of blended cements containing significant amounts of aluminum for over 30 years, the structural nature of aluminum in the main hydration product, calcium aluminate silicate hydrate (C-A-S-H), remains elusive. Using first-principles calculations, we predict that aluminum is incorporated into the bridging sites of the linear silicate chains and that at high Ca:Si and H2O ratios, the stable coordination number of aluminum is six. Specifically, we predict that silicate-bridging [AlO2(OH)4]5- complexes are favored, stabilized by hydroxyl ligands and charge balancing calcium ions in the interlayer space. This structure is then confirmed experimentally by one- and two-dimensional dynamic nuclear polarization enhanced 27Al and 29Si solid-state NMR experiments. We notably assign a narrow 27Al NMR signal at 5 ppm to the silicate-bridging [AlO2(OH)4]5- sites and show that this signal correlates to 29Si NMR signals from silicates in C-A-S-H, conflicting with its conventional assignment to a "third aluminate hydrate" (TAH) phase. We therefore conclude that TAH does not exist. This resolves a long-standing dilemma about the location and nature of the six-fold-coordinated aluminum observed by 27Al NMR in C-A-S-H samples.

The usefulness of molecular-dynamics simulations in clarifying the activation enthalpy of oxygen-vacancy migration in the perovskite oxide BaTiO3.

We employed molecular-dynamics simulations with interatomic pair-potentials to examine oxygen-vacancy diffusion in the cubic phase of perovskite BaTiO3 as a function of temperature. By comparing the absolute rate of vacancy diffusion as well as its temperature dependence with experimental data, we are able to narrow down the activation enthalpy of migration to 0.70-0.76 eV.

The energetics of carbonated PuO2 surfaces affects nanoparticle morphology: a DFT+U study.

Radiolytic corrosion of actinide materials represent an issue for the long term storage and disposal of nuclear materials. Molecular species adsorbed at the surface of the actinides may impact the rate of radiolysis, and as the surfaces corrode, the soluble toxic and radioactive species leach into groundwater. It is therefore critical to characterise the surface composition of actinides. Here, we employ ab initio modelling to determine the surface composition of PuO2 with respect to adsorbed CO2. We found that CO2 interacts strongly with the surface forming carbonate species. By mapping the energetics of this interaction, we then calculate the temperature of desorption, finding that surface morphology has a strong impact on the adsorption of CO2, with the {100} being the most and the {111} the least affected by carbonation. Finally, we predict the effect of carbonation on the morphology of PuO2 nanoparticles as a function of temperature and pressure, finding that truncated octahedral is the preferred morphology. This modelling strategy helps characterise surface compensition and nanoparticle morphology, and we discuss the implication for radiolytically driven dispersal of material into the environment.

Comparative analysis of metazoan chromatin organization.

Genome function is dynamically regulated in part by chromatin, which consists of the histones, non-histone proteins and RNA molecules that package DNA. Studies in Caenorhabditis elegans and Drosophila melanogaster have contributed substantially to our understanding of molecular mechanisms of genome function in humans, and have revealed conservation of chromatin components and mechanisms. Nevertheless, the three organisms have markedly different genome sizes, chromosome architecture and gene organization. On human and fly chromosomes, for example, pericentric heterochromatin flanks single centromeres, whereas worm chromosomes have dispersed heterochromatin-like regions enriched in the distal chromosomal 'arms', and centromeres distributed along their lengths. To systematically investigate chromatin organization and associated gene regulation across species, we generated and analysed a large collection of genome-wide chromatin data sets from cell lines and developmental stages in worm, fly and human. Here we present over 800 new data sets from our ENCODE and modENCODE consortia, bringing the total to over 1,400. Comparison of combinatorial patterns of histone modifications, nuclear lamina-associated domains, organization of large-scale topological domains, chromatin environment at promoters and enhancers, nucleosome positioning, and DNA replication patterns reveals many conserved features of chromatin organization among the three organisms. We also find notable differences in the composition and locations of repressive chromatin. These data sets and analyses provide a rich resource for comparative and species-specific investigations of chromatin composition, organization and function.

Genetic regulatory signatures underlying islet gene expression and type 2 diabetes.

Genome-wide association studies (GWAS) have identified >100 independent SNPs that modulate the risk of type 2 diabetes (T2D) and related traits. However, the pathogenic mechanisms of most of these SNPs remain elusive. Here, we examined genomic, epigenomic, and transcriptomic profiles in human pancreatic islets to understand the links between genetic variation, chromatin landscape, and gene expression in the context of T2D. We first integrated genome and transcriptome variation across 112 islet samples to produce dense cis-expression quantitative trait loci (cis-eQTL) maps. Additional integration with chromatin-state maps for islets and other diverse tissue types revealed that cis-eQTLs for islet-specific genes are specifically and significantly enriched in islet stretch enhancers. High-resolution chromatin accessibility profiling using assay for transposase-accessible chromatin sequencing (ATAC-seq) in two islet samples enabled us to identify specific transcription factor (TF) footprints embedded in active regulatory elements, which are highly enriched for islet cis-eQTL. Aggregate allelic bias signatures in TF footprints enabled us de novo to reconstruct TF binding affinities genetically, which support the high-quality nature of the TF footprint predictions. Interestingly, we found that T2D GWAS loci were strikingly and specifically enriched in islet Regulatory Factor X (RFX) footprints. Remarkably, within and across independent loci, T2D risk alleles that overlap with RFX footprints uniformly disrupt the RFX motifs at high-information content positions. Together, these results suggest that common regulatory variations have shaped islet TF footprints and the transcriptome and that a confluent RFX regulatory grammar plays a significant role in the genetic component of T2D predisposition.

Genomic annotation of disease-associated variants reveals shared functional contexts.

Variation in non-coding DNA, encompassing gene regulatory regions such as enhancers and promoters, contributes to risk for complex disorders, including type 2 diabetes. While genome-wide association studies have successfully identified hundreds of type 2 diabetes loci throughout the genome, the vast majority of these reside in non-coding DNA, which complicates the process of determining their functional significance and level of priority for further study. Here we review the methods used to experimentally annotate these non-coding variants, to nominate causal variants and to link them to diabetes pathophysiology. In recent years, chromatin profiling, massively parallel sequencing, high-throughput reporter assays and CRISPR gene editing technologies have rapidly become indispensable tools. Rather than treating individual variants in isolation, we discuss the importance of accounting for context, both genetic (such as flanking DNA sequence) and environmental (such as cellular state or environmental exposure). Incorporating these features shows promise in terms of revealing biologically convergent molecular signatures across distant and seemingly unrelated loci. Studying regulatory elements in the proper context will be crucial for interpreting the functional significance of disease-associated variants and applying the resulting knowledge to improve patient care.

YAMDA: thousandfold speedup of EM-based motif discovery using deep learning libraries and GPU.

Motivation: Motif discovery in large biopolymer sequence datasets can be computationally demanding, presenting significant challenges for discovery in omics research. MEME, arguably one of the most popular motif discovery software, takes quadratic time with respect to dataset size, leading to excessively long runtimes for large datasets. Therefore, there is a demand for fast programs that can generate results of the same quality as MEME. Results: Here we describe YAMDA, a highly scalable motif discovery software package. It is built on Pytorch, a tensor computation deep learning library with strong GPU acceleration that is highly optimized for tensor operations that are also useful for motifs. YAMDA takes linear time to find motifs as accurately as MEME, completing in seconds or minutes, which translates to speedups over a thousandfold. Availability and implementation: YAMDA is freely available on Github (https://github.com/daquang/YAMDA). Supplementary information: Supplementary data are available at Bioinformatics online.

Impact of Hydrogen on the Intermediate Oxygen Clusters and Diffusion in Fluorite Structured UO2+ x.

Uranium dioxide is the most prevalent nuclear fuel. Defect clusters are known to be present in significant concentrations in hyperstoichoimetric uranium oxide, UO2+ x, and have a significant impact on the corrosion of the material. A detailed understanding of the defect clusters that form is required for accurate diffusion models in UO2+ x. Using ab initio calculations, we show that at low excess oxygen concentration, where defects are mostly isolated oxygen interstitials, hydrogen stabilizes the initial clustering. The simplest cluster at this low excess oxygen stoichiometry consists of a pair of oxygen ions bound to an oxygen vacancy, namely the split mono-interstital, which resembles larger split interstitials clusters in UO2+ x. Our data shows that, depending on local hydrogen concertation, the presence of hydrogen stabilizes this cluster over isolated oxygen interstitials.

Bayesian determination of the effect of a deep eutectic solvent on the structure of lipid monolayers.

In this work, we present the first example of the self-assembly of phospholipid monolayers at the interface between air and an ionic solvent. Deep eutectic solvents are a novel class of environmentally friendly, non-aqueous, room temperature liquids with tunable properties, that have wide-ranging potential applications and are capable of promoting the self-assembly of surfactant molecules. We use a chemically-consistent Bayesian modelling of X-ray and neutron reflectometry measurements to show that these monolayers broadly behave as they do on water. This method allows for the monolayer structure to be determined, alongside the molecular volumes of the individual monolayer components, without the need for water-specific constraints to be introduced. Furthermore, using this method we are able to better understand the correlations present between parameters in the analytical model. This example of a non-aqueous phospholipid monolayer has important implications for the potential uses of these solvents and for our understanding of how biomolecules behave in the absence of water.