Kinetic and Thermodynamic Control of G-Quadruplex Polymorphism by Na+ and K+ Cations.

G-Quadruplexes (G4s) are ubiquitous nucleic acid folding motifs that exhibit structural diversity that is dependent on cationic conditions. In this work, we exploit temperature-controlled single-molecule fluorescence resonance energy transfer (smFRET) to elucidate the kinetic and thermodynamic mechanisms by which monovalent cations (K+ and Na+) impact folding topologies for a simple G-quadruplex sequence (5'-GGG-(TAAGGG)3-3') with a three-state folding equilibrium. Kinetic measurements indicate that Na+ and K+ influence G4 formation in two distinctly different ways: the presence of Na+ modestly enhances an antiparallel G4 topology through an induced fit (IF) mechanism with a low affinity (Kd = 228 ± 26 mM), while K+ drives G4 into a parallel/hybrid topology via a conformational selection (CS) mechanism with much higher affinity (Kd = 1.9 ± 0.2 mM). Additionally, temperature-dependent studies of folding rate constants and equilibrium ratios reveal distinctly different thermodynamic driving forces behind G4 binding to K+ (ΔH°bind > 0, ΔS°bind > 0) versus Na+ (ΔH°bind < 0, ΔS°bind < 0), which further illuminates the diversity of the possible pathways for monovalent facilitation of G-quadruplex folding.

Changing word meanings in biomedical literature reveal pandemics and new technologies.

While we often think of words as having a fixed meaning that we use to describe a changing world, words are also dynamic and changing. Scientific research can also be remarkably fast-moving, with new concepts or approaches rapidly gaining mind share. We examined scientific writing, both preprint and pre-publication peer-reviewed text, to identify terms that have changed and examine their use. One particular challenge that we faced was that the shift from closed to open access publishing meant that the size of available corpora changed by over an order of magnitude in the last two decades. We developed an approach to evaluate semantic shift by accounting for both intra- and inter-year variability using multiple integrated models. This analysis revealed thousands of change points in both corpora, including for terms such as 'cas9', 'pandemic', and 'sars'. We found that the consistent change-points between pre-publication peer-reviewed and preprinted text are largely related to the COVID-19 pandemic. We also created a web app for exploration that allows users to investigate individual terms ( https://greenelab.github.io/word-lapse/ ). To our knowledge, our research is the first to examine semantic shift in biomedical preprints and pre-publication peer-reviewed text, and provides a foundation for future work to understand how terms acquire new meanings and how peer review affects this process.

Dissolved gases in the deep North Atlantic track ocean ventilation processes.

Gas exchange between the atmosphere and ocean interior profoundly impacts global climate and biogeochemistry. However, our understanding of the relevant physical processes remains limited by a scarcity of direct observations. Dissolved noble gases in the deep ocean are powerful tracers of physical air-sea interaction due to their chemical and biological inertness, yet their isotope ratios have remained underexplored. Here, we present high-precision noble gas isotope and elemental ratios from the deep North Atlantic (~32°N, 64°W) to evaluate gas exchange parameterizations using an ocean circulation model. The unprecedented precision of these data reveal deep-ocean undersaturation of heavy noble gases and isotopes resulting from cooling-driven air-to-sea gas transport associated with deep convection in the northern high latitudes. Our data also imply an underappreciated and large role for bubble-mediated gas exchange in the global air-sea transfer of sparingly soluble gases, including O2, N2, and SF6. Using noble gases to validate the physical representation of air-sea gas exchange in a model also provides a unique opportunity to distinguish physical from biogeochemical signals. As a case study, we compare dissolved N2/Ar measurements in the deep North Atlantic to physics-only model predictions, revealing excess N2 from benthic denitrification in older deep waters (below 2.9 km). These data indicate that the rate of fixed N removal in the deep Northeastern Atlantic is at least three times higher than the global deep-ocean mean, suggesting tight coupling with organic carbon export and raising potential future implications for the marine N cycle.

Hetnet connectivity search provides rapid insights into how two biomedical entities are related.

Hetnets, short for "heterogeneous networks", contain multiple node and relationship types and offer a way to encode biomedical knowledge. One such example, Hetionet connects 11 types of nodes - including genes, diseases, drugs, pathways, and anatomical structures - with over 2 million edges of 24 types. Previous work has demonstrated that supervised machine learning methods applied to such networks can identify drug repurposing opportunities. However, a training set of known relationships does not exist for many types of node pairs, even when it would be useful to examine how nodes of those types are meaningfully connected. For example, users may be curious not only how metformin is related to breast cancer, but also how the GJA1 gene might be involved in insomnia. We developed a new procedure, termed hetnet connectivity search, that proposes important paths between any two nodes without requiring a supervised gold standard. The algorithm behind connectivity search identifies types of paths that occur more frequently than would be expected by chance (based on node degree alone). We find that predictions are broadly similar to those from previously described supervised approaches for certain node type pairs. Scoring of individual paths is based on the most specific paths of a given type. Several optimizations were required to precompute significant instances of node connectivity at the scale of large knowledge graphs. We implemented the method on Hetionet and provide an online interface at https://het.io/search . We provide an open source implementation of these methods in our new Python package named hetmatpy .

100 million years of turtle paleoniche dynamics enable the prediction of latitudinal range shifts in a warming world.

Past responses to environmental change provide vital baseline data for estimating the potential resilience of extant taxa to future change. Here, we investigate the latitudinal range contraction that terrestrial and freshwater turtles (Testudinata) experienced from the Late Cretaceous to the Paleogene (100.5-23.03 mya) in response to major climatic changes. We apply ecological niche modeling (ENM) to reconstruct turtle niches, using ancient and modern distribution data, paleogeographic reconstructions, and the HadCM3L climate model to quantify their range shifts in the Cretaceous and late Eocene. We then use the insights provided by these models to infer their probable ecological responses to future climate scenarios at different representative concentration pathways (RCPs 4.5 and 8.5 for 2100), which project globally increased temperatures and spreading arid biomes at lower to mid-latitudes. We show that turtle ranges are predicted to expand poleward in the Northern Hemisphere, with decreased habitat suitability at lower latitudes, inverting a trend of latitudinal range contraction that has been prevalent since the Eocene. Trionychids and freshwater turtles can more easily track their niches than Testudinidae and other terrestrial groups. However, habitat destruction and fragmentation at higher latitudes will probably reduce the capability of turtles and tortoises to cope with future climate changes.

Expanding a database-derived biomedical knowledge graph via multi-relation extraction from biomedical abstracts.

BACKGROUND: Knowledge graphs support biomedical research efforts by providing contextual information for biomedical entities, constructing networks, and supporting the interpretation of high-throughput analyses. These databases are populated via manual curation, which is challenging to scale with an exponentially rising publication rate. Data programming is a paradigm that circumvents this arduous manual process by combining databases with simple rules and heuristics written as label functions, which are programs designed to annotate textual data automatically. Unfortunately, writing a useful label function requires substantial error analysis and is a nontrivial task that takes multiple days per function. This bottleneck makes populating a knowledge graph with multiple nodes and edge types practically infeasible. Thus, we sought to accelerate the label function creation process by evaluating how label functions can be re-used across multiple edge types. RESULTS: We obtained entity-tagged abstracts and subsetted these entities to only contain compounds, genes, and disease mentions. We extracted sentences containing co-mentions of certain biomedical entities contained in a previously described knowledge graph, Hetionet v1. We trained a baseline model that used database-only label functions and then used a sampling approach to measure how well adding edge-specific or edge-mismatch label function combinations improved over our baseline. Next, we trained a discriminator model to detect sentences that indicated a biomedical relationship and then estimated the number of edge types that could be recalled and added to Hetionet v1. We found that adding edge-mismatch label functions rarely improved relationship extraction, while control edge-specific label functions did. There were two exceptions to this trend, Compound-binds-Gene and Gene-interacts-Gene, which both indicated physical relationships and showed signs of transferability. Across the scenarios tested, discriminative model performance strongly depends on generated annotations. Using the best discriminative model for each edge type, we recalled close to 30% of established edges within Hetionet v1. CONCLUSIONS: Our results show that this framework can incorporate novel edges into our source knowledge graph. However, results with label function transfer were mixed. Only label functions describing very similar edge types supported improved performance when transferred. We expect that the continued development of this strategy may provide essential building blocks to populating biomedical knowledge graphs with discoveries, ensuring that these resources include cutting-edge results.

Experiments and Modeling of Flow-Enhanced Nucleation in LLDPE.

A computational and experimental framework for quantifying flow-enhanced nucleation (FEN) in polymers is presented and demonstrated for an industrial-grade linear low-density polyethylene (LLDPE). Experimentally, kinetic measurements of isothermal crystallization were performed by using fast-scanning calorimetry (FSC) for melts that were presheared at various strain rates. The effect of shear on the average conformation tensor of the melt was modeled with the discrete slip-link model (DSM). The conformation tensor was then related to the acceleration in nucleation kinetics by using an expression previously validated with nonequilibrium molecular dynamics (NEMD). The expression is based on the nematic order tensor of Kuhn segments, which can be obtained from the conformation tensor of entanglement strands. The single adjustable parameter of the model was determined by fitting to the experimental FSC data. This expression accurately describes FEN for the LLDPE, representing a significant advancement toward the development of a fully integrated processing model for crystallizable polymers.

Characterization of the genome and silk-gland transcriptomes of Darwin's bark spider (Caerostris darwini).

Natural silks crafted by spiders comprise some of the most versatile materials known. Artificial silks-based on the sequences of their natural brethren-replicate some desirable biophysical properties and are increasingly utilized in commercial and medical applications today. To characterize the repertoire of protein sequences giving silks their biophysical properties and to determine the set of expressed genes across each unique silk gland contributing to the formation of natural silks, we report here draft genomic and transcriptomic assemblies of Darwin's bark spider, Caerostris darwini, an orb-weaving spider whose dragline is one of the toughest known biomaterials on Earth. We identify at least 31 putative spidroin genes, with expansion of multiple spidroin gene classes relative to the golden orb-weaver, Trichonephila clavipes. We observed substantial sharing of spidroin repetitive sequence motifs between species as well as new motifs unique to C. darwini. Comparative gene expression analyses across six silk gland isolates in females plus a composite isolate of all silk glands in males demonstrated gland and sex-specific expression of spidroins, facilitating putative assignment of novel spidroin genes to classes. Broad expression of spidroins across silk gland types suggests that silks emanating from a given gland represent composite materials to a greater extent than previously appreciated. We hypothesize that the extraordinary toughness of C. darwini major ampullate dragline silk may relate to the unique protein composition of major ampullate spidroins, combined with the relatively high expression of stretchy flagelliform spidroins whose union into a single fiber may be aided by novel motifs and cassettes that act as molecule-binding helices. Our assemblies extend the catalog of sequences and sets of expressed genes that confer the unique biophysical properties observed in natural silks.

Could simplified stimuli change how the brain performs visual search tasks? A deep neural network study.

Visual search is a complex behavior influenced by many factors. To control for these factors, many studies use highly simplified stimuli. However, the statistics of these stimuli are very different from the statistics of the natural images that the human visual system is optimized by evolution and experience to perceive. Could this difference change search behavior? If so, simplified stimuli may contribute to effects typically attributed to cognitive processes, such as selective attention. Here we use deep neural networks to test how optimizing models for the statistics of one distribution of images constrains performance on a task using images from a different distribution. We train four deep neural network architectures on one of three source datasets-natural images, faces, and x-ray images-and then adapt them to a visual search task using simplified stimuli. This adaptation produces models that exhibit performance limitations similar to humans, whereas models trained on the search task alone exhibit no such limitations. However, we also find that deep neural networks trained to classify natural images exhibit similar limitations when adapted to a search task that uses a different set of natural images. Therefore, the distribution of data alone cannot explain this effect. We discuss how future work might integrate an optimization-based approach into existing models of visual search behavior.

Adaptation to genome decay in the structure of the smallest eukaryotic ribosome.

The evolution of microbial parasites involves the counterplay between natural selection forcing parasites to improve and genetic drifts forcing parasites to lose genes and accumulate deleterious mutations. Here, to understand how this counterplay occurs at the scale of individual macromolecules, we describe cryo-EM structure of ribosomes from Encephalitozoon cuniculi, a eukaryote with one of the smallest genomes in nature. The extreme rRNA reduction in E. cuniculi ribosomes is accompanied with unparalleled structural changes, such as the evolution of previously unknown molten rRNA linkers and bulgeless rRNA. Furthermore, E. cuniculi ribosomes withstand the loss of rRNA and protein segments by evolving an ability to use small molecules as structural mimics of degenerated rRNA and protein segments. Overall, we show that the molecular structures long viewed as reduced, degenerated, and suffering from debilitating mutations possess an array of compensatory mechanisms that allow them to remain active despite the extreme molecular reduction.

Examining linguistic shifts between preprints and publications.

Preprints allow researchers to make their findings available to the scientific community before they have undergone peer review. Studies on preprints within bioRxiv have been largely focused on article metadata and how often these preprints are downloaded, cited, published, and discussed online. A missing element that has yet to be examined is the language contained within the bioRxiv preprint repository. We sought to compare and contrast linguistic features within bioRxiv preprints to published biomedical text as a whole as this is an excellent opportunity to examine how peer review changes these documents. The most prevalent features that changed appear to be associated with typesetting and mentions of supporting information sections or additional files. In addition to text comparison, we created document embeddings derived from a preprint-trained word2vec model. We found that these embeddings are able to parse out different scientific approaches and concepts, link unannotated preprint-peer-reviewed article pairs, and identify journals that publish linguistically similar papers to a given preprint. We also used these embeddings to examine factors associated with the time elapsed between the posting of a first preprint and the appearance of a peer-reviewed publication. We found that preprints with more versions posted and more textual changes took longer to publish. Lastly, we constructed a web application (https://greenelab.github.io/preprint-similarity-search/) that allows users to identify which journals and articles that are most linguistically similar to a bioRxiv or medRxiv preprint as well as observe where the preprint would be positioned within a published article landscape.

Sal-type ABC-F proteins: intrinsic and common mediators of pleuromutilin resistance by target protection in staphylococci.

The first member of the pleuromutilin (PLM) class suitable for systemic antibacterial chemotherapy in humans recently entered clinical use, underscoring the need to better understand mechanisms of PLM resistance in disease-causing bacterial genera. Of the proteins reported to mediate PLM resistance in staphylococci, the least-well studied to date is Sal(A), a putative ABC-F NTPase that-by analogy to other proteins of this type-may act to protect the ribosome from PLMs. Here, we establish the importance of Sal proteins as a common source of PLM resistance across multiple species of staphylococci. Sal(A) is revealed as but one member of a larger group of Sal-type ABC-F proteins that vary considerably in their ability to mediate resistance to PLMs and other antibiotics. We find that specific sal genes are intrinsic to particular staphylococcal species, and show that this gene family is likely ancestral to the genus Staphylococcus. Finally, we solve the cryo-EM structure of a representative Sal-type protein (Sal(B)) in complex with the staphylococcal 70S ribosome, revealing that Sal-type proteins bind into the E site to mediate target protection, likely by displacing PLMs and other antibiotics via an allosteric mechanism.

Automated annotation of birdsong with a neural network that segments spectrograms.

Songbirds provide a powerful model system for studying sensory-motor learning. However, many analyses of birdsong require time-consuming, manual annotation of its elements, called syllables. Automated methods for annotation have been proposed, but these methods assume that audio can be cleanly segmented into syllables, or they require carefully tuning multiple statistical models. Here, we present TweetyNet: a single neural network model that learns how to segment spectrograms of birdsong into annotated syllables. We show that TweetyNet mitigates limitations of methods that rely on segmented audio. We also show that TweetyNet performs well across multiple individuals from two species of songbirds, Bengalese finches and canaries. Lastly, we demonstrate that using TweetyNet we can accurately annotate very large datasets containing multiple days of song, and that these predicted annotations replicate key findings from behavioral studies. In addition, we provide open-source software to assist other researchers, and a large dataset of annotated canary song that can serve as a benchmark. We conclude that TweetyNet makes it possible to address a wide range of new questions about birdsong.

Hetnet connectivity search provides rapid insights into how biomedical entities are related.

BACKGROUND: Hetnets, short for "heterogeneous networks," contain multiple node and relationship types and offer a way to encode biomedical knowledge. One such example, Hetionet, connects 11 types of nodes-including genes, diseases, drugs, pathways, and anatomical structures-with over 2 million edges of 24 types. Previous work has demonstrated that supervised machine learning methods applied to such networks can identify drug repurposing opportunities. However, a training set of known relationships does not exist for many types of node pairs, even when it would be useful to examine how nodes of those types are meaningfully connected. For example, users may be curious about not only how metformin is related to breast cancer but also how a given gene might be involved in insomnia. FINDINGS: We developed a new procedure, termed hetnet connectivity search, that proposes important paths between any 2 nodes without requiring a supervised gold standard. The algorithm behind connectivity search identifies types of paths that occur more frequently than would be expected by chance (based on node degree alone). Several optimizations were required to precompute significant instances of node connectivity at the scale of large knowledge graphs. CONCLUSION: We implemented the method on Hetionet and provide an online interface at https://het.io/search. We provide an open-source implementation of these methods in our new Python package named hetmatpy.

Real-time quality control of optical backscattering data from Biogeochemical-Argo floats.

BACKGROUND: Biogeochemical-Argo floats are collecting an unprecedented number of profiles of optical backscattering measurements in the global ocean. Backscattering (BBP) data are crucial to understanding ocean particle dynamics and the biological carbon pump. Yet, so far, no procedures have been agreed upon to quality control BBP data in real time. METHODS: Here, we present a new suite of real-time quality-control tests and apply them to the current global BBP Argo dataset. The tests were developed by expert BBP users and Argo data managers and have been implemented on a snapshot of the entire Argo dataset. RESULTS: The new tests are able to automatically flag most of the "bad" BBP profiles from the raw dataset. CONCLUSIONS: The proposed tests have been approved by the Biogeochemical-Argo Data Management Team and will be implemented by the Argo Data Assembly Centres to deliver real-time quality-controlled profiles of optical backscattering. Provided they reach a pressure of about 1000 dbar, these tests could also be applied to BBP profiles collected by other platforms.

Measuring Excess Heat Capacities of Deoxyribonucleic Acid (DNA) Folding at the Single-Molecule Level.

Measurements of the thermodynamic properties of biomolecular folding (ΔG°, ΔH°, ΔS°, etc.) provide a wealth of information on the folding process and have long played a central role in biophysical investigation. In particular, the excess heat capacity of folding (ΔCP) is crucial, as typically measured in bulk ensemble studies by differential scanning calorimetry (DSC) and isothermal titration calorimetry (ITC). Here, we report the first measurements of ΔCP at the single-molecule level using the single-molecule fluorescence resonance energy transfer (smFRET) as well as the very first measurements of the heat capacity change associated with achieving the transition state (ΔC‡P) for nucleic acid folding. The deoxyribonucleic acid (DNA) hairpin used in these studies exhibits an excess heat capacity for hybridization (ΔCP = -340 ± 60 J/mol/K per base pair) consistent with the range of literature expectations (ΔCP = -100 to -420 J/mol/K per base pair). Furthermore, the measured activation heat capacities (ΔC‡P) for such hairpin unfolding are consistent with a folding transition state containing few fully formed base pairs, in agreement with prevailing models of DNA hybridization.

Pushing Camera-Based Single-Molecule Kinetic Measurements to the Frame Acquisition Limit with Stroboscopic smFRET.

Single-molecule fluorescence resonance energy transfer (smFRET) experiments permit detailed examination of microscopic dynamics. However, kinetic rate constants determined by smFRET are susceptible to systematic underestimation when the rate constants are comparable to the data acquisition rate. We demonstrate how such systematic errors in camera-based total internal reflection fluorescence microscopy experiments can be greatly reduced by using stroboscopic illumination/detection, allowing accurate rate constant determination up to the data sampling rate and yielding an order of magnitude increase in the dynamic range. Implementation of these stroboscopic smFRET ideas is straightforward, and the stroboscopically obtained data are compatible with multiple trajectory analysis methods, including dwell-time analysis and hidden Markov modeling. Such stroboscopic methods therefore offer a remarkably simple yet valuable addition to the smFRET toolkit, requiring only relatively modest modification to the normal data collection and analysis procedures.

Chirality-Dependent Amino Acid Modulation of RNA Folding.

The preponderance of a specific d- or l-chirality in fats, sugars, amino acids, nucleic acids, and so on is ubiquitous in nature, yet the biological origin of such chiral dominance (i.e., with one enantiomer overwhelmingly present) remains an open question. One plausible proposal for the predominance of l-chirality in amino acids could be through evolutionary templating of chiral RNA-folding via chaperone activity. To help evaluate this possibility, single molecule fluorescence experiments have been performed that measure the chiral dependence of chaperone folding dynamics for the simple tetraloop-tetraloop receptor (TL-TLR) tertiary binding motif in the presence of a series of chiral amino acids. Specifically, d- vs l-arginine is found to accelerate the unfolding of this RNA motif in a chirally selective fashion, with temperature-dependent studies of the kinetics performed to extract free energy, enthalpy, and entropy landscapes for the underlying thermodynamics. Furthermore, all-atom molecular dynamics (MD) simulations are pursued to provide additional physical insight into this chiral sensitivity, which reveal enantiomer-specific sampling of nucleic acid surfaces by d- vs l-arginine and support a putative mechanism for chirally specific denaturation of RNA tertiary structure by arginine but not other amino acids.