Building a collaborative cloud platform to accelerate heart, lung, blood, and sleep research.

Research increasingly relies on interrogating large-scale data resources. The NIH National Heart, Lung, and Blood Institute developed the NHLBI BioData CatalystⓇ (BDC), a community-driven ecosystem where researchers, including bench and clinical scientists, statisticians, and algorithm developers, find, access, share, store, and compute on large-scale datasets. This ecosystem provides secure, cloud-based workspaces, user authentication and authorization, search, tools and workflows, applications, and new innovative features to address community needs, including exploratory data analysis, genomic and imaging tools, tools for reproducibility, and improved interoperability with other NIH data science platforms. BDC offers straightforward access to large-scale datasets and computational resources that support precision medicine for heart, lung, blood, and sleep conditions, leveraging separately developed and managed platforms to maximize flexibility based on researcher needs, expertise, and backgrounds. Through the NHLBI BioData Catalyst Fellows Program, BDC facilitates scientific discoveries and technological advances. BDC also facilitated accelerated research on the coronavirus disease-2019 (COVID-19) pandemic.

Dug: a semantic search engine leveraging peer-reviewed knowledge to query biomedical data repositories.

MOTIVATION: As the number of public data resources continues to proliferate, identifying relevant datasets across heterogenous repositories is becoming critical to answering scientific questions. To help researchers navigate this data landscape, we developed Dug: a semantic search tool for biomedical datasets utilizing evidence-based relationships from curated knowledge graphs to find relevant datasets and explain why those results are returned. RESULTS: Developed through the National Heart, Lung and Blood Institute's (NHLBI) BioData Catalyst ecosystem, Dug has indexed more than 15 911 study variables from public datasets. On a manually curated search dataset, Dug's total recall (total relevant results/total results) of 0.79 outperformed default Elasticsearch's total recall of 0.76. When using synonyms or related concepts as search queries, Dug (0.36) far outperformed Elasticsearch (0.14) in terms of total recall with no significant loss in the precision of its top results. AVAILABILITY AND IMPLEMENTATION: Dug is freely available at https://github.com/helxplatform/dug. An example Dug deployment is also available for use at https://search.biodatacatalyst.renci.org/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Dynamic human MutSα-MutLα complexes compact mismatched DNA.

DNA mismatch repair (MMR) corrects errors that occur during DNA replication. In humans, mutations in the proteins MutSα and MutLα that initiate MMR cause Lynch syndrome, the most common hereditary cancer. MutSα surveilles the DNA, and upon recognition of a replication error it undergoes adenosine triphosphate-dependent conformational changes and recruits MutLα. Subsequently, proliferating cell nuclear antigen (PCNA) activates MutLα to nick the error-containing strand to allow excision and resynthesis. The structure-function properties of these obligate MutSα-MutLα complexes remain mostly unexplored in higher eukaryotes, and models are predominately based on studies of prokaryotic proteins. Here, we utilize atomic force microscopy (AFM) coupled with other methods to reveal time- and concentration-dependent stoichiometries and conformations of assembling human MutSα-MutLα-DNA complexes. We find that they assemble into multimeric complexes comprising three to eight proteins around a mismatch on DNA. On the timescale of a few minutes, these complexes rearrange, folding and compacting the DNA. These observations contrast with dominant models of MMR initiation that envision diffusive MutS-MutL complexes that move away from the mismatch. Our results suggest MutSα localizes MutLα near the mismatch and promotes DNA configurations that could enhance MMR efficiency by facilitating MutLα nicking the DNA at multiple sites around the mismatch. In addition, such complexes may also protect the mismatch region from nucleosome reassembly until repair occurs, and they could potentially remodel adjacent nucleosomes.

Cross Modality Microscopy Segmentation via Adversarial Adaptation.

Deep learning techniques have been successfully applied to automatically segment and quantify cell-types in images acquired from both confocal and light sheet fluorescence microscopy. However, the training of deep learning networks requires a massive amount of manually-labeled training data, which is a very time-consuming operation. In this paper, we demonstrate an adversarial adaptation method to transfer deep network knowledge for microscopy segmentation from one imaging modality (e.g., confocal) to a new imaging modality (e.g., light sheet) for which no or very limited labeled training data is available. Promising segmentation results show that the proposed transfer learning approach is an effective way to rapidly develop segmentation solutions for new imaging methods.

Enhanced electrostatic force microscopy reveals higher-order DNA looping mediated by the telomeric protein TRF2.

Shelterin protein TRF2 modulates telomere structures by promoting dsDNA compaction and T-loop formation. Advancement of our understanding of the mechanism underlying TRF2-mediated DNA compaction requires additional information regarding DNA paths in TRF2-DNA complexes. To uncover the location of DNA inside protein-DNA complexes, we recently developed the Dual-Resonance-frequency-Enhanced Electrostatic force Microscopy (DREEM) imaging technique. DREEM imaging shows that in contrast to chromatin with DNA wrapping around histones, large TRF2-DNA complexes (with volumes larger than TRF2 tetramers) compact DNA inside TRF2 with portions of folded DNA appearing at the edge of these complexes. Supporting coarse-grained molecular dynamics simulations uncover the structural requirement and sequential steps during TRF2-mediated DNA compaction and result in folded DNA structures with protruding DNA loops as seen in DREEM imaging. Revealing DNA paths in TRF2 complexes provides new mechanistic insights into structure-function relationships underlying telomere maintenance pathways.

Visualizing the Path of DNA through Proteins Using DREEM Imaging.

Many cellular functions require the assembly of multiprotein-DNA complexes. A growing area of structural biology aims to characterize these dynamic structures by combining atomic-resolution crystal structures with lower-resolution data from techniques that provide distributions of species, such as small-angle X-ray scattering, electron microscopy, and atomic force microscopy (AFM). A significant limitation in these combinatorial methods is localization of the DNA within the multiprotein complex. Here, we combine AFM with an electrostatic force microscopy (EFM) method to develop an exquisitely sensitive dual-resonance-frequency-enhanced EFM (DREEM) capable of resolving DNA within protein-DNA complexes. Imaging of nucleosomes and DNA mismatch repair complexes demonstrates that DREEM can reveal both the path of the DNA wrapping around histones and the path of DNA as it passes through both single proteins and multiprotein complexes. Finally, DREEM imaging requires only minor modifications of many existing commercial AFMs, making the technique readily available.

Angiopoietin-like protein 4 inhibition of lipoprotein lipase: evidence for reversible complex formation.

Elevated triglycerides are associated with an increased risk of cardiovascular disease, and lipoprotein lipase (LPL) is the rate-limiting enzyme for the hydrolysis of triglycerides from circulating lipoproteins. The N-terminal domain of angiopoietin-like protein 4 (ANGPTL4) inhibits LPL activity. ANGPTL4 was previously described as an unfolding molecular chaperone of LPL that catalytically converts active LPL dimers into inactive monomers. Our studies show that ANGPTL4 is more accurately described as a reversible, noncompetitive inhibitor of LPL. We find that inhibited LPL is in a complex with ANGPTL4, and upon dissociation, LPL regains lipase activity. Furthermore, we have generated a variant of ANGPTL4 that is dependent on divalent cations for its ability to inhibit LPL. We show that LPL inactivation by this regulatable variant of ANGPTL4 is fully reversible after treatment with a chelator.