Murine MHC-Deficient Nonobese Diabetic Mice Carrying Human HLA-DQ8 Develop Severe Myocarditis and Myositis in Response to Anti-PD-1 Immune Checkpoint Inhibitor Cancer Therapy.

Myocarditis has emerged as an immune-related adverse event of immune checkpoint inhibitor (ICI) cancer therapy associated with significant mortality. To ensure patients continue to safely benefit from life-saving cancer therapy, an understanding of fundamental immunological phenomena underlying ICI myocarditis is essential. We recently developed the NOD-cMHCI/II-/-.DQ8 mouse model that spontaneously develops myocarditis with lower mortality than observed in previous HLA-DQ8 NOD mouse strains. Our strain was rendered murine MHC class I and II deficient using CRISPR/Cas9 technology, making it a genetically clean platform for dissecting CD4+ T cell-mediated myocarditis in the absence of classically selected CD8+ T cells. These mice are highly susceptible to myocarditis and acute heart failure following anti-PD-1 ICI-induced treatment. Additionally, anti-PD-1 administration accelerates skeletal muscle myositis. Using histology, flow cytometry, adoptive transfers, and RNA sequencing analyses, we performed a thorough characterization of cardiac and skeletal muscle T cells, identifying shared and unique characteristics of both populations. Taken together, this report details a mouse model with features of a rare, but highly lethal clinical presentation of overlapping myocarditis and myositis following ICI therapy. This study sheds light on underlying immunological mechanisms in ICI myocarditis and provides the basis for further detailed analyses of diagnostic and therapeutic strategies.

Automated assembly of molecular mechanisms at scale from text mining and curated databases.

The analysis of omic data depends on machine-readable information about protein interactions, modifications, and activities as found in protein interaction networks, databases of post-translational modifications, and curated models of gene and protein function. These resources typically depend heavily on human curation. Natural language processing systems that read the primary literature have the potential to substantially extend knowledge resources while reducing the burden on human curators. However, machine-reading systems are limited by high error rates and commonly generate fragmentary and redundant information. Here, we describe an approach to precisely assemble molecular mechanisms at scale using multiple natural language processing systems and the Integrated Network and Dynamical Reasoning Assembler (INDRA). INDRA identifies full and partial overlaps in information extracted from published papers and pathway databases, uses predictive models to improve the reliability of machine reading, and thereby assembles individual pieces of information into non-redundant and broadly usable mechanistic knowledge. Using INDRA to create high-quality corpora of causal knowledge we show it is possible to extend protein-protein interaction databases and explain co-dependencies in the Cancer Dependency Map.

STonKGs: a sophisticated transformer trained on biomedical text and knowledge graphs.

MOTIVATION: The majority of biomedical knowledge is stored in structured databases or as unstructured text in scientific publications. This vast amount of information has led to numerous machine learning-based biological applications using either text through natural language processing (NLP) or structured data through knowledge graph embedding models. However, representations based on a single modality are inherently limited. RESULTS: To generate better representations of biological knowledge, we propose STonKGs, a Sophisticated Transformer trained on biomedical text and Knowledge Graphs (KGs). This multimodal Transformer uses combined input sequences of structured information from KGs and unstructured text data from biomedical literature to learn joint representations in a shared embedding space. First, we pre-trained STonKGs on a knowledge base assembled by the Integrated Network and Dynamical Reasoning Assembler consisting of millions of text-triple pairs extracted from biomedical literature by multiple NLP systems. Then, we benchmarked STonKGs against three baseline models trained on either one of the modalities (i.e. text or KG) across eight different classification tasks, each corresponding to a different biological application. Our results demonstrate that STonKGs outperforms both baselines, especially on the more challenging tasks with respect to the number of classes, improving upon the F1-score of the best baseline by up to 0.084 (i.e. from 0.881 to 0.965). Finally, our pre-trained model as well as the model architecture can be adapted to various other transfer learning applications. AVAILABILITY AND IMPLEMENTATION: We make the source code and the Python package of STonKGs available at GitHub (https://github.com/stonkgs/stonkgs) and PyPI (https://pypi.org/project/stonkgs/). The pre-trained STonKGs models and the task-specific classification models are respectively available at https://huggingface.co/stonkgs/stonkgs-150k and https://zenodo.org/communities/stonkgs. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Prepubertal skeletal muscle growth requires Pax7-expressing satellite cell-derived myonuclear contribution.

The functional role of Pax7-expressing satellite cells (SCs) in postnatal skeletal muscle development beyond weaning remains obscure. Therefore, the relevance of SCs during prepubertal growth, a period after weaning but prior to the onset of puberty, has not been examined. Here, we have characterized mouse skeletal muscle growth during prepuberty and found significant increases in myofiber cross-sectional area that correlated with SC-derived myonuclear number. Remarkably, genome-wide RNA-sequencing analysis established that post-weaning juvenile and early adolescent skeletal muscle have markedly different gene expression signatures. These distinctions are consistent with extensive skeletal muscle maturation during this essential, albeit brief, developmental phase. Indelible labeling of SCs with Pax7CreERT2/+ ; Rosa26nTnG/+ mice demonstrated SC-derived myonuclear contribution during prepuberty, with a substantial reduction at puberty onset. Prepubertal depletion of SCs in Pax7CreERT2/+ ; Rosa26DTA/+ mice reduced myofiber size and myonuclear number, and caused force generation deficits to a similar extent in both fast and slow-contracting muscles. Collectively, these data demonstrate SC-derived myonuclear accretion as a cellular mechanism that contributes to prepubertal hypertrophic skeletal muscle growth.

BID preferentially activates BAK while BIM preferentially activates BAX, affecting chemotherapy response.

Apoptosis is a highly regulated form of cell death that controls normal homeostasis as well as the antitumor activity of many chemotherapeutic agents. Commitment to death via the mitochondrial apoptotic pathway requires activation of the mitochondrial pore-forming proteins BAK or BAX. Activation can be effected by the activator BH3-only proteins BID or BIM, which have been considered to be functionally redundant in this role. Herein, we show that significant activation preferences exist between these proteins: BID preferentially activates BAK while BIM preferentially activates BAX. Furthermore, we find that cells lacking BAK are relatively resistant to agents that require BID activation for maximal induction of apoptosis, including topoisomerase inhibitors and TRAIL. Consequently, patients with tumors that harbor a loss of BAK1 exhibit an inferior response to topoisomerase inhibitor treatment in the clinic. Therefore, BID and BIM have nonoverlapping roles in the induction of apoptosis via BAK and BAX, affecting chemotherapy response.

INDRA-IPM: interactive pathway modeling using natural language with automated assembly.

SUMMARY: INDRA-IPM (Interactive Pathway Map) is a web-based pathway map modeling tool that combines natural language processing with automated model assembly and visualization. INDRA-IPM contextualizes models with expression data and exports them to standard formats. AVAILABILITY AND IMPLEMENTATION: INDRA-IPM is available at: http://pathwaymap.indra.bio. Source code is available at http://github.com/sorgerlab/indra_pathway_map. The underlying web service API is available at http://api.indra.bio:8000. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Production of a NiO/Al primary battery employing powder-based electrodes.

This paper describes the use of aluminum and zinc as anodic materials for a battery employing nickel (II) oxide (NiO) as cathode. Comparison of both materials resulted in the development of a compact, cost effective, and easy to use primary NiO/Al battery employing an alkaline electrolyte. The system features electrodes composed of powder forms of the active materials on modified paper substrates that are contained in a simple multilayer design utilizing thin laminated plastic materials to provide structure and flexibility to the battery as well as a paper separator. Various concentrations of potassium hydroxide (KOH) electrolyte were examined and maximum performance was observed at 6 M KOH. A maximum current density and power density of 1.94 mA/cm2 and 1 mW/cm2 , respectively was achieved. This user-friendly device was able to produce a maximum capacity of 2.33 mAh/g when 2 mA/g was applied. This work demonstrates the viability of a paper-based battery featuring powder electrodes as a possible power source for microelectronic devices.

Fundamental trade-offs between information flow in single cells and cellular populations.

Signal transduction networks allow eukaryotic cells to make decisions based on information about intracellular state and the environment. Biochemical noise significantly diminishes the fidelity of signaling: networks examined to date seem to transmit less than 1 bit of information. It is unclear how networks that control critical cell-fate decisions (e.g., cell division and apoptosis) can function with such low levels of information transfer. Here, we use theory, experiments, and numerical analysis to demonstrate an inherent trade-off between the information transferred in individual cells and the information available to control population-level responses. Noise in receptor-mediated apoptosis reduces information transfer to approximately 1 bit at the single-cell level but allows 3-4 bits of information to be transmitted at the population level. For processes such as eukaryotic chemotaxis, in which single cells are the functional unit, we find high levels of information transmission at a single-cell level. Thus, low levels of information transfer are unlikely to represent a physical limit. Instead, we propose that signaling networks exploit noise at the single-cell level to increase population-level information transfer, allowing extracellular ligands, whose levels are also subject to noise, to incrementally regulate phenotypic changes. This is particularly critical for discrete changes in fate (e.g., life vs. death) for which the key variable is the fraction of cells engaged. Our findings provide a framework for rationalizing the high levels of noise in metazoan signaling networks and have implications for the development of drugs that target these networks in the treatment of cancer and other diseases.

FamPlex: a resource for entity recognition and relationship resolution of human protein families and complexes in biomedical text mining.

BACKGROUND: For automated reading of scientific publications to extract useful information about molecular mechanisms it is critical that genes, proteins and other entities be correctly associated with uniform identifiers, a process known as named entity linking or "grounding." Correct grounding is essential for resolving relationships among mined information, curated interaction databases, and biological datasets. The accuracy of this process is largely dependent on the availability of machine-readable resources associating synonyms and abbreviations commonly found in biomedical literature with uniform identifiers. RESULTS: In a task involving automated reading of ∼215,000 articles using the REACH event extraction software we found that grounding was disproportionately inaccurate for multi-protein families (e.g., "AKT") and complexes with multiple subunits (e.g."NF- κB"). To address this problem we constructed FamPlex, a manually curated resource defining protein families and complexes as they are commonly encountered in biomedical text. In FamPlex the gene-level constituents of families and complexes are defined in a flexible format allowing for multi-level, hierarchical membership. To create FamPlex, text strings corresponding to entities were identified empirically from literature and linked manually to uniform identifiers; these identifiers were also mapped to equivalent entries in multiple related databases. FamPlex also includes curated prefix and suffix patterns that improve named entity recognition and event extraction. Evaluation of REACH extractions on a test corpus of ∼54,000 articles showed that FamPlex significantly increased grounding accuracy for families and complexes (from 15 to 71%). The hierarchical organization of entities in FamPlex also made it possible to integrate otherwise unconnected mechanistic information across families, subfamilies, and individual proteins. Applications of FamPlex to the TRIPS/DRUM reading system and the Biocreative VI Bioentity Normalization Task dataset demonstrated the utility of FamPlex in other settings. CONCLUSION: FamPlex is an effective resource for improving named entity recognition, grounding, and relationship resolution in automated reading of biomedical text. The content in FamPlex is available in both tabular and Open Biomedical Ontology formats at https://github.com/sorgerlab/famplex under the Creative Commons CC0 license and has been integrated into the TRIPS/DRUM and REACH reading systems.

Conductive MOF electrodes for stable supercapacitors with high areal capacitance.

Owing to their high power density and superior cyclability relative to batteries, electrochemical double layer capacitors (EDLCs) have emerged as an important electrical energy storage technology that will play a critical role in the large-scale deployment of intermittent renewable energy sources, smart power grids, and electrical vehicles. Because the capacitance and charge-discharge rates of EDLCs scale with surface area and electrical conductivity, respectively, porous carbons such as activated carbon, carbon nanotubes and crosslinked or holey graphenes are used exclusively as the active electrode materials in EDLCs. One class of materials whose surface area far exceeds that of activated carbons, potentially allowing them to challenge the dominance of carbon electrodes in EDLCs, is metal-organic frameworks (MOFs). The high porosity of MOFs, however, is conventionally coupled to very poor electrical conductivity, which has thus far prevented the use of these materials as active electrodes in EDLCs. Here, we show that Ni3(2,3,6,7,10,11-hexaiminotriphenylene)2 (Ni3(HITP)2), a MOF with high electrical conductivity, can serve as the sole electrode material in an EDLC. This is the first example of a supercapacitor made entirely from neat MOFs as active materials, without conductive additives or other binders. The MOF-based device shows an areal capacitance that exceeds those of most carbon-based materials and capacity retention greater than 90% over 10,000 cycles, in line with commercial devices. Given the established structural and compositional tunability of MOFs, these results herald the advent of a new generation of supercapacitors whose active electrode materials can be tuned rationally, at the molecular level.

From word models to executable models of signaling networks using automated assembly.

Word models (natural language descriptions of molecular mechanisms) are a common currency in spoken and written communication in biomedicine but are of limited use in predicting the behavior of complex biological networks. We present an approach to building computational models directly from natural language using automated assembly. Molecular mechanisms described in simple English are read by natural language processing algorithms, converted into an intermediate representation, and assembled into executable or network models. We have implemented this approach in the Integrated Network and Dynamical Reasoning Assembler (INDRA), which draws on existing natural language processing systems as well as pathway information in Pathway Commons and other online resources. We demonstrate the use of INDRA and natural language to model three biological processes of increasing scope: (i) p53 dynamics in response to DNA damage, (ii) adaptive drug resistance in BRAF-V600E-mutant melanomas, and (iii) the RAS signaling pathway. The use of natural language makes the task of developing a model more efficient and it increases model transparency, thereby promoting collaboration with the broader biology community.

Inorganic Solid-State Electrolytes for Lithium Batteries: Mechanisms and Properties Governing Ion Conduction.

This Review is focused on ion-transport mechanisms and fundamental properties of solid-state electrolytes to be used in electrochemical energy-storage systems. Properties of the migrating species significantly affecting diffusion, including the valency and ionic radius, are discussed. The natures of the ligand and metal composing the skeleton of the host framework are analyzed and shown to have large impacts on the performance of solid-state electrolytes. A comprehensive identification of the candidate migrating species and structures is carried out. Not only the bulk properties of the conductors are explored, but the concept of tuning the conductivity through interfacial effects-specifically controlling grain boundaries and strain at the interfaces-is introduced. High-frequency dielectric constants and frequencies of low-energy optical phonons are shown as examples of properties that correlate with activation energy across many classes of ionic conductors. Experimental studies and theoretical results are discussed in parallel to give a pathway for further improvement of solid-state electrolytes. Through this discussion, the present Review aims to provide insight into the physical parameters affecting the diffusion process, to allow for more efficient and target-oriented research on improving solid-state ion conductors.

Optimizing ring assembly reveals the strength of weak interactions.

Most cellular processes rely on large multiprotein complexes that must assemble into a well-defined quaternary structure in order to function. A number of prominent examples, including the 20S core particle of the proteasome and the AAA+ family of ATPases, contain ring-like structures. Developing an understanding of the complex assembly pathways employed by ring-like structures requires a characterization of the problems these pathways have had to overcome as they evolved. In this work, we use computational models to uncover one such problem: a deadlocked plateau in the assembly dynamics. When the molecular interactions between subunits are too strong, this plateau leads to significant delays in assembly and a reduction in steady-state yield. Conversely, if the interactions are too weak, assembly delays are caused by the instability of crucial intermediates. Intermediate affinities thus maximize the efficiency of assembly for homomeric ring-like structures. In the case of heteromeric rings, we find that rings including at least one weak interaction can assemble efficiently and robustly. Estimation of affinities from solved structures of ring-like complexes indicates that heteromeric rings tend to contain a weak interaction, confirming our prediction. In addition to providing an evolutionary rationale for structural features of rings, our work forms the basis for understanding the complex assembly pathways of stacked rings like the proteasome and suggests principles that would aid in the design of synthetic ring-like structures that self-assemble efficiently.

Improving Confidence in Competencies for International Medical Trips Using a Curriculum with Simulation.

Many incoming medical and undergraduate students seek out international medical mission trips to supplement their education and training. However, few have the necessary skills to perform simple clinical tasks such as taking vital signs or conducting an initial patient interview. We conducted a small pilot study to assess the impact of simulation exercises on teaching incoming first-year medical students and undergraduate students basic clinical skills and teamwork. Our study population consisted of nine incoming medical students and 11 undergraduate students who participated in a training session involving simulated tasks prior to taking a medical mission trip to Nicaragua. Participants completed a survey before and after the simulation and at the end of the trip. All 20 indicated the simulation was effective in teaching clinical and team-building skills. In addition, the simulation exercise improved participants' confidence in their ability to perform certain clinical tasks and work as a team prior to the mission trip. We concluded that simulation is effective for incoming medical and undergraduate students and can be used prior to global health trips to increase their confidence in performing tasks required for a successful experience.

Programming biological models in Python using PySB.

Mathematical equations are fundamental to modeling biological networks, but as networks get large and revisions frequent, it becomes difficult to manage equations directly or to combine previously developed models. Multiple simultaneous efforts to create graphical standards, rule-based languages, and integrated software workbenches aim to simplify biological modeling but none fully meets the need for transparent, extensible, and reusable models. In this paper we describe PySB, an approach in which models are not only created using programs, they are programs. PySB draws on programmatic modeling concepts from little b and ProMot, the rule-based languages BioNetGen and Kappa and the growing library of Python numerical tools. Central to PySB is a library of macros encoding familiar biochemical actions such as binding, catalysis, and polymerization, making it possible to use a high-level, action-oriented vocabulary to construct detailed models. As Python programs, PySB models leverage tools and practices from the open-source software community, substantially advancing our ability to distribute and manage the work of testing biochemical hypotheses. We illustrate these ideas using new and previously published models of apoptosis.

Women's values in contraceptive choice: a systematic review of relevant attributes included in decision aids.

BACKGROUND: Women can choose from a range of contraceptive methods that differ in important ways. Inadequate decision support may lead them to select a method that poorly fits their circumstances, leading to dissatisfaction, misuse, or nonuse. Decision support interventions, such as decision aids, may help women choose a method of contraception that best fits their personal circumstances. To guide future decision aid development, we aim to summarize the attributes of contraceptive methods included in available decision aids as well as surveys and interviews of women actively choosing a contraceptive method. METHODS: We conducted a systematic review to identify attributes of contraceptive methods that may be important to women when engaging in this decision making process. We performed a database search of MEDLINE/PubMed, Ovid EMBASE, OVID CENTRAL, Ovid PsycInfo, EBSCO CINAHL, Popline, and Scopus from 1985 until 2013 to identify decision aids, structured interviews and questionnaires reporting attributes of contraceptive options that are of importance to women. A free-text internet search was also performed to identify additional decision support tools. All articles and tools were reviewed in duplicate for inclusion, and a summary list of attributes was compiled. RESULTS: We included 20 surveys, 1 semistructured interview report and 19 decision aids, reporting 32 unique attributes. While some attributes were consistently included in surveys/interviews and decision aids, several were included more often in decision aids as opposed to surveys/interviews (e.g., STI prevention, noncontraceptive benefits, how the method is used, requirement of a healthcare provider), and vice versa (e.g., a woman's vicarious experience with contraceptive methods). Key attributes mentioned in both surveys/interviews and decision aids include efficacy (29 total mentioned) and side effects/health risks (28 total mentioned). While a limited number of decision support tools were formally evaluated, many were not rigorously studied. CONCLUSIONS: Many attributes were identified as potentially important to women choosing a method of contraception, but these were inconsistently included in the reviewed resources. Formal evaluation of decision support tools for contraceptive choice and involvement of users in the development process may lead to more user-centered design and implementation.

Satellite cells in the growth and maintenance of muscle.

Embryonic skeletal muscle growth is contingent upon a population of somite derived satellite cells, however, the contribution of these cells to early postnatal skeletal muscle growth remains relatively high. As prepubertal postnatal development proceeds, the activity and contribution of satellite cells to skeletal muscle growth diminishes. Eventually, at around puberty, a population of satellite cells escapes terminal commitment, continues to express the paired box transcription factor Pax7, and reside in a quiescent state orbiting the myofiber periphery adjacent to the basal lamina. After adolescence, some satellite cell contributions to muscle maintenance and adaptation occur, however, their necessity is reduced relative to embryonic, early postnatal, and prepubertal growth.

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.

Introduction: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Methods: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors. Results: Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. Discussion: The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.

Insights into muscle stem cell dynamics during postnatal development.

During development, resident stem cell populations contribute to the growth and maturation of tissue and organs. In skeletal muscle, muscle stem cells, or satellite cells (SCs), are responsible for the maturation of postnatal myofibers. However, the role SCs play in later stages of postnatal growth, and thus, when they enter a mature quiescent state is controversial. Here, we discuss the current literature regarding the role SCs play in all stages of postnatal growth, from birth to puberty onset to young adulthood. We additionally highlight the implications of SC loss or dysfunction during developmental stages, both in the context of experimental paradigms and disease settings.

Automated Network Assembly of Mechanistic Literature for Informed Evidence Identification to Support Cancer Risk Assessment.

BACKGROUND: Mechanistic data is increasingly used in hazard identification of chemicals. However, the volume of data is large, challenging the efficient identification and clustering of relevant data. OBJECTIVES: We investigated whether evidence identification for hazard assessment can become more efficient and informed through an automated approach that combines machine reading of publications with network visualization tools. METHODS: We chose 13 chemicals that were evaluated by the International Agency for Research on Cancer (IARC) Monographs program incorporating the key characteristics of carcinogens (KCCs) approach. Using established literature search terms for KCCs, we retrieved and analyzed literature using Integrated Network and Dynamical Reasoning Assembler (INDRA). INDRA combines large-scale literature processing with pathway databases and extracts relationships between biomolecules, bioprocesses, and chemicals into statements (e.g., "benzene activates DNA damage"). These statements were subsequently assembled into networks and compared with the KCC evaluation by the IARC, to evaluate the informativeness of our approach. RESULTS: We found, in general, larger networks for those chemicals which the IARC has evaluated the evidence to be strong for KCC induction. Larger networks were not directly linked to publication count, given that we retrieved small networks for several chemicals with little support for KCC activation according to the IARC, despite the significant volume of literature for these specific chemicals. In addition, interpreting networks for genotoxicity and DNA repair showed concordance with the IARC KCC evaluation. DISCUSSION: Our method is an automated approach to condense mechanistic literature into searchable and interpretable networks based on an a priori ontology. The approach is no replacement of expert evaluation but, instead, provides an informed structure for experts to quickly identify which statements are made in which papers and how these could connect. We focused on the KCCs because these are supported by well-described search terms. The method needs to be tested in other frameworks as well to demonstrate its generalizability. https://doi.org/10.1289/EHP9112.