Compromised function of the ESCRT pathway promotes endolysosomal escape of tau seeds and propagation of tau aggregation.

Intercellular propagation of protein aggregation is emerging as a key mechanism in the progression of several neurodegenerative diseases, including Alzheimer's disease and frontotemporal dementia (FTD). However, we lack a systematic understanding of the cellular pathways controlling prion-like propagation of aggregation. To uncover such pathways, here we performed CRISPR interference (CRISPRi) screens in a human cell-based model of propagation of tau aggregation monitored by FRET. Our screens uncovered that knockdown of several components of the endosomal sorting complexes required for transport (ESCRT) machinery, including charged multivesicular body protein 6 (CHMP6), or CHMP2A in combination with CHMP2B (whose gene is linked to familial FTD), promote propagation of tau aggregation. We found that knocking down the genes encoding these proteins also causes damage to endolysosomal membranes, consistent with a role for the ESCRT pathway in endolysosomal membrane repair. Leakiness of the endolysosomal compartment significantly enhanced prion-like propagation of tau aggregation, likely by making tau seeds more available to pools of cytoplasmic tau. Together, these findings suggest that endolysosomal escape is a critical step in tau propagation in neurodegenerative diseases.

Nf1 mutation disrupts activity-dependent oligodendroglial plasticity and motor learning in mice.

Neurogenetic disorders, such as neurofibromatosis type 1 (NF1), can cause cognitive and motor impairments, traditionally attributed to intrinsic neuronal defects such as disruption of synaptic function. Activity-regulated oligodendroglial plasticity also contributes to cognitive and motor functions by tuning neural circuit dynamics. However, the relevance of oligodendroglial plasticity to neurological dysfunction in NF1 is unclear. Here we explore the contribution of oligodendrocyte progenitor cells (OPCs) to pathological features of the NF1 syndrome in mice. Both male and female littermates (4-24 weeks of age) were used equally in this study. We demonstrate that mice with global or OPC-specific Nf1 heterozygosity exhibit defects in activity-dependent oligodendrogenesis and harbor focal OPC hyperdensities with disrupted homeostatic OPC territorial boundaries. These OPC hyperdensities develop in a cell-intrinsic Nf1 mutation-specific manner due to differential PI3K/AKT activation. OPC-specific Nf1 loss impairs oligodendroglial differentiation and abrogates the normal oligodendroglial response to neuronal activity, leading to impaired motor learning performance. Collectively, these findings show that Nf1 mutation delays oligodendroglial development and disrupts activity-dependent OPC function essential for normal motor learning in mice.

Harnessing Biomaterials for Immunomodulatory-Driven Tissue Engineering.

Abstract: The immune system plays a crucial role during tissue repair and wound healing processes. Biomaterials have been leveraged to assist in this in situ tissue regeneration process to dampen the foreign body response by evading or suppressing the immune system. An emerging paradigm within regenerative medicine is to use biomaterials to influence the immune system and create a pro-reparative microenvironment to instigate endogenously driven tissue repair. In this review, we discuss recent studies that focus on immunomodulation of innate and adaptive immune cells for tissue engineering applications through four biomaterial-based mechanisms of action: biophysical cues, chemical modifications, drug delivery, and sequestration. These materials enable augmented regeneration in various contexts, including vascularization, bone repair, wound healing, and autoimmune regulation. While further understanding of immune-material interactions is needed to design the next generation of immunomodulatory biomaterials, these materials have already demonstrated great promise for regenerative medicine. Lay Summary: The immune system plays an important role in tissue repair. Many biomaterial strategies have been used to promote tissue repair, and recent work in this area has looked into the possibility of doing repair by tuning. Thus, we examined the literature for recent works showcasing the efficacy of these approaches in animal models of injuries. In these studies, we found that biomaterials successfully tuned the immune response and improved the repair of various tissues. This highlights the promise of immune-modulating material strategies to improve tissue repair.

Codelivery of synergistic antimicrobials with polyelectrolyte nanocomplexes to treat bacterial biofilms and lung infections.

Bacterial biofilm infections, particularly those of Pseudomonas aeruginosa (PA), have high rates of antimicrobial tolerance and are commonly found in chronic wound and cystic fibrosis lung infections. Combination therapeutics that act synergistically can overcome antimicrobial tolerance; however, the delivery of multiple therapeutics at relevant dosages remains a challenge. We therefore developed a nanoscale drug carrier for antimicrobial codelivery by combining approaches from polyelectrolyte nanocomplex (NC) formation and layer-by-layer electrostatic self-assembly. This strategy led to NC drug carriers loaded with tobramycin antibiotics and antimicrobial silver nanoparticles (AgTob-NCs). AgTob-NCs displayed synergistic enhancements in antimicrobial activity against both planktonic and biofilm PA cultures, with positively charged NCs outperforming negatively charged formulations. NCs were evaluated in mouse models of lung infection, leading to reduced bacterial burden and improved survival outcomes. This approach therefore shows promise for nanoscale therapeutic codelivery to treat recalcitrant bacterial infections.

WASP integrates substrate topology and cell polarity to guide neutrophil migration.

To control their movement, cells need to coordinate actin assembly with the geometric features of their substrate. Here, we uncover a role for the actin regulator WASP in the 3D migration of neutrophils. We show that WASP responds to substrate topology by enriching to sites of inward, substrate-induced membrane deformation. Superresolution imaging reveals that WASP preferentially enriches to the necks of these substrate-induced invaginations, a distribution that could support substrate pinching. WASP facilitates recruitment of the Arp2/3 complex to these sites, stimulating local actin assembly that couples substrate features with the cytoskeleton. Surprisingly, WASP only enriches to membrane deformations in the front half of the cell, within a permissive zone set by WASP's front-biased regulator Cdc42. While WASP KO cells exhibit relatively normal migration on flat substrates, they are defective at topology-directed migration. Our data suggest that WASP integrates substrate topology with cell polarity by selectively polymerizing actin around substrate-induced membrane deformations in the front half of the cell.

OpenCell: Endogenous tagging for the cartography of human cellular organization.

Elucidating the wiring diagram of the human cell is a central goal of the postgenomic era. We combined genome engineering, confocal live-cell imaging, mass spectrometry, and data science to systematically map the localization and interactions of human proteins. Our approach provides a data-driven description of the molecular and spatial networks that organize the proteome. Unsupervised clustering of these networks delineates functional communities that facilitate biological discovery. We found that remarkably precise functional information can be derived from protein localization patterns, which often contain enough information to identify molecular interactions, and that RNA binding proteins form a specific subgroup defined by unique interaction and localization properties. Paired with a fully interactive website (opencell.czbiohub.org), our work constitutes a resource for the quantitative cartography of human cellular organization.

Reducing Physicians' Cognitive Load During Chart Review: A Problem-Oriented Summary of the Patient Electronic Record.

Overabundance of information within electronic health records (EHRs) has resulted in a need for automated systems to mitigate the cognitive burden on physicians utilizing today's EHR systems. We present ProSPER, a Problem-oriented Summary of the Patient Electronic Record that displays a patient summary centered around an auto-generated problem list and disease-specific views for chronic conditions. ProSPER was developed using 1,500 longitudinal patient records from two large multi-specialty medical groups in the United States, and leverages multiple natural language processing (NLP) components targeting various fundamental (e.g. syntactic analysis), clinical (e.g. adverse drug event extraction) and summarizing (e.g. problem list generation) tasks. We report evaluation results for each component and discuss how specific components address existing physician challenges in reviewing EHR data. This work demonstrates the need to leverage holistic information in EHRs to build a comprehensive summarization application, and the potential for NLP-based applications to support physicians and improve clinical care.

Identification of Semantically Similar Sentences in Clinical Notes: Iterative Intermediate Training Using Multi-Task Learning.

BACKGROUND: Although electronic health records (EHRs) have been widely adopted in health care, effective use of EHR data is often limited because of redundant information in clinical notes introduced by the use of templates and copy-paste during note generation. Thus, it is imperative to develop solutions that can condense information while retaining its value. A step in this direction is measuring the semantic similarity between clinical text snippets. To address this problem, we participated in the 2019 National NLP Clinical Challenges (n2c2)/Open Health Natural Language Processing Consortium (OHNLP) clinical semantic textual similarity (ClinicalSTS) shared task. OBJECTIVE: This study aims to improve the performance and robustness of semantic textual similarity in the clinical domain by leveraging manually labeled data from related tasks and contextualized embeddings from pretrained transformer-based language models. METHODS: The ClinicalSTS data set consists of 1642 pairs of deidentified clinical text snippets annotated in a continuous scale of 0-5, indicating degrees of semantic similarity. We developed an iterative intermediate training approach using multi-task learning (IIT-MTL), a multi-task training approach that employs iterative data set selection. We applied this process to bidirectional encoder representations from transformers on clinical text mining (ClinicalBERT), a pretrained domain-specific transformer-based language model, and fine-tuned the resulting model on the target ClinicalSTS task. We incrementally ensembled the output from applying IIT-MTL on ClinicalBERT with the output of other language models (bidirectional encoder representations from transformers for biomedical text mining [BioBERT], multi-task deep neural networks [MT-DNN], and robustly optimized BERT approach [RoBERTa]) and handcrafted features using regression-based learning algorithms. On the basis of these experiments, we adopted the top-performing configurations as our official submissions. RESULTS: Our system ranked first out of 87 submitted systems in the 2019 n2c2/OHNLP ClinicalSTS challenge, achieving state-of-the-art results with a Pearson correlation coefficient of 0.9010. This winning system was an ensembled model leveraging the output of IIT-MTL on ClinicalBERT with BioBERT, MT-DNN, and handcrafted medication features. CONCLUSIONS: This study demonstrates that IIT-MTL is an effective way to leverage annotated data from related tasks to improve performance on a target task with a limited data set. This contribution opens new avenues of exploration for optimized data set selection to generate more robust and universal contextual representations of text in the clinical domain.

How essential are unstructured clinical narratives and information fusion to clinical trial recruitment?

Electronic health records capture patient information using structured controlled vocabularies and unstructured narrative text. While structured data typically encodes lab values, encounters and medication lists, unstructured data captures the physician's interpretation of the patient's condition, prognosis, and response to therapeutic intervention. In this paper, we demonstrate that information extraction from unstructured clinical narratives is essential to most clinical applications. We perform an empirical study to validate the argument and show that structured data alone is insufficient in resolving eligibility criteria for recruiting patients onto clinical trials for chronic lymphocytic leukemia (CLL) and prostate cancer. Unstructured data is essential to solving 59% of the CLL trial criteria and 77% of the prostate cancer trial criteria. More specifically, for resolving eligibility criteria with temporal constraints, we show the need for temporal reasoning and information integration with medical events within and across unstructured clinical narratives and structured data.

A review of approaches to identifying patient phenotype cohorts using electronic health records.

OBJECTIVE: To summarize literature describing approaches aimed at automatically identifying patients with a common phenotype. MATERIALS AND METHODS: We performed a review of studies describing systems or reporting techniques developed for identifying cohorts of patients with specific phenotypes. Every full text article published in (1) Journal of American Medical Informatics Association, (2) Journal of Biomedical Informatics, (3) Proceedings of the Annual American Medical Informatics Association Symposium, and (4) Proceedings of Clinical Research Informatics Conference within the past 3 years was assessed for inclusion in the review. Only articles using automated techniques were included. RESULTS: Ninety-seven articles met our inclusion criteria. Forty-six used natural language processing (NLP)-based techniques, 24 described rule-based systems, 41 used statistical analyses, data mining, or machine learning techniques, while 22 described hybrid systems. Nine articles described the architecture of large-scale systems developed for determining cohort eligibility of patients. DISCUSSION: We observe that there is a rise in the number of studies associated with cohort identification using electronic medical records. Statistical analyses or machine learning, followed by NLP techniques, are gaining popularity over the years in comparison with rule-based systems. CONCLUSIONS: There are a variety of approaches for classifying patients into a particular phenotype. Different techniques and data sources are used, and good performance is reported on datasets at respective institutions. However, no system makes comprehensive use of electronic medical records addressing all of their known weaknesses.

Inter-annotator reliability of medical events, coreferences and temporal relations in clinical narratives by annotators with varying levels of clinical expertise.

The manual annotation of clinical narratives is an important step for training and validating the performance of automated systems that utilize these clinical narratives. We build an annotation specification to capture medical events, and coreferences and temporal relations between medical events in clinical text. Unfortunately, the process of clinical data annotation is both time consuming and costly. Many annotation efforts have used physicians to annotate the data. We investigate using annotators that are current students or graduates from diverse clinical backgrounds with varying levels of clinical experience. In spite of this diversity, the annotation agreement across our team of annotators is high; the average inter-annotator kappa statistic for medical events, coreferences, temporal relations, and medical event concept unique identifiers was 0.843, 0.859, 0.833, and 0.806, respectively. We describe methods towards leveraging the annotations to support temporal reasoning with medical events.