AutonoMS: Automated Ion Mobility Metabolomic Fingerprinting.

Automation is dramatically changing the nature of laboratory life science. Robotic lab hardware that can perform manual operations with greater speed, endurance, and reproducibility opens an avenue for faster scientific discovery with less time spent on laborious repetitive tasks. A major bottleneck remains in integrating cutting-edge laboratory equipment into automated workflows, notably specialized analytical equipment, which is designed for human usage. Here we present AutonoMS, a platform for automatically running, processing, and analyzing high-throughput mass spectrometry experiments. AutonoMS is currently written around an ion mobility mass spectrometry (IM-MS) platform and can be adapted to additional analytical instruments and data processing flows. AutonoMS enables automated software agent-controlled end-to-end measurement and analysis runs from experimental specification files that can be produced by human users or upstream software processes. We demonstrate the use and abilities of AutonoMS in a high-throughput flow-injection ion mobility configuration with 5 s sample analysis time, processing robotically prepared chemical standards and cultured yeast samples in targeted and untargeted metabolomics applications. The platform exhibited consistency, reliability, and ease of use while eliminating the need for human intervention in the process of sample injection, data processing, and analysis. The platform paves the way toward a more fully automated mass spectrometry analysis and ultimately closed-loop laboratory workflows involving automated experimentation and analysis coupled to AI-driven experimentation utilizing cutting-edge analytical instrumentation. AutonoMS documentation is available at https://autonoms.readthedocs.io.

Interpreting protein abundance in Saccharomyces cerevisiae through relational learning.

MOTIVATION: Proteomic profiles reflect the functional readout of the physiological state of an organism. An increased understanding of what controls and defines protein abundances is of high scientific interest. Saccharomyces cerevisiae is a well-studied model organism, and there is a large amount of structured knowledge on yeast systems biology in databases such as the Saccharomyces Genome Database, and highly curated genome-scale metabolic models like Yeast8. These datasets, the result of decades of experiments, are abundant in information, and adhere to semantically meaningful ontologies. RESULTS: By representing this knowledge in an expressive Datalog database we generated data descriptors using relational learning that, when combined with supervised machine learning, enables us to predict protein abundances in an explainable manner. We learnt predictive relationships between protein abundances, function and phenotype; such as α-amino acid accumulations and deviations in chronological lifespan. We further demonstrate the power of this methodology on the proteins His4 and Ilv2, connecting qualitative biological concepts to quantified abundances. AVAILABILITY AND IMPLEMENTATION: All data and processing scripts are available at the following Github repository: https://github.com/DanielBrunnsaker/ProtPredict.

Genesis-DB: a database for autonomous laboratory systems.

Summary: Artificial intelligence (AI)-driven laboratory automation-combining robotic labware and autonomous software agents-is a powerful trend in modern biology. We developed Genesis-DB, a database system designed to support AI-driven autonomous laboratories by providing software agents access to large quantities of structured domain information. In addition, we present a new ontology for modeling data and metadata from autonomously performed yeast microchemostat cultivations in the framework of the Genesis robot scientist system. We show an example of how Genesis-DB enables the research life cycle by modeling yeast gene regulation, guiding future hypotheses generation and design of experiments. Genesis-DB supports AI-driven discovery through automated reasoning and its design is portable, generic, and easily extensible to other AI-driven molecular biology laboratory data and beyond. Availability and implementation: Genesis-DB code and installation instructions are available at the GitHub repository https://github.com/TW-Genesis/genesis-database-system.git. The database use case demo code and data are also available through GitHub (https://github.com/TW-Genesis/genesis-database-demo.git). The ontology can be downloaded here: https://github.com/TW-Genesis/genesis-ontology/releases/download/v0.0.23/genesis.owl. The ontology term descriptions (including mappings to existing ontologies) and maintenance standard operating procedures can be found at: https://github.com/TW-Genesis/genesis-ontology.

Protein-ligand binding affinity prediction exploiting sequence constituent homology.

MOTIVATION: Molecular docking is a commonly used approach for estimating binding conformations and their resultant binding affinities. Machine learning has been successfully deployed to enhance such affinity estimations. Many methods of varying complexity have been developed making use of some or all the spatial and categorical information available in these structures. The evaluation of such methods has mainly been carried out using datasets from PDBbind. Particularly the Comparative Assessment of Scoring Functions (CASF) 2007, 2013, and 2016 datasets with dedicated test sets. This work demonstrates that only a small number of simple descriptors is necessary to efficiently estimate binding affinity for these complexes without the need to know the exact binding conformation of a ligand. RESULTS: The developed approach of using a small number of ligand and protein descriptors in conjunction with gradient boosting trees demonstrates high performance on the CASF datasets. This includes the commonly used benchmark CASF2016 where it appears to perform better than any other approach. This methodology is also useful for datasets where the spatial relationship between the ligand and protein is unknown as demonstrated using a large ChEMBL-derived dataset. AVAILABILITY AND IMPLEMENTATION: Code and data uploaded to https://github.com/abbiAR/PLBAffinity.

High-throughput metabolomics for the design and validation of a diauxic shift model.

Saccharomyces cerevisiae is a very well studied organism, yet ∼20% of its proteins remain poorly characterized. Moreover, recent studies seem to indicate that the pace of functional discovery is slow. Previous work has implied that the most probable path forward is via not only automation but fully autonomous systems in which active learning is applied to guide high-throughput experimentation. Development of tools and methods for these types of systems is of paramount importance. In this study we use constrained dynamical flux balance analysis (dFBA) to select ten regulatory deletant strains that are likely to have previously unexplored connections to the diauxic shift. We then analyzed these deletant strains using untargeted metabolomics, generating profiles which were then subsequently investigated to better understand the consequences of the gene deletions in the metabolic reconfiguration of the diauxic shift. We show that metabolic profiles can be utilised to not only gaining insight into cellular transformations such as the diauxic shift, but also on regulatory roles and biological consequences of regulatory gene deletion. We also conclude that untargeted metabolomics is a useful tool for guidance in high-throughput model improvement, and is a fast, sensitive and informative approach appropriate for future large-scale functional analyses of genes. Moreover, it is well-suited for automated approaches due to relative simplicity of processing and the potential to make massively high-throughput.

Romantic relationship quality and functioning for individuals with clinical and sub-clinical social anxiety: a scoping review.

BACKGROUND: Social Anxiety Disorder (SAD) is associated with pervasive functional impairments and chronicity. Romantic relationship functioning and quality for individuals with SAD has been previously explored but existing studies have not been synthesised. AIMS: This scoping review charted existing literature regarding the quality and functioning of romantic relationships for people with SAD and high sub-clinical social anxiety (SA). METHODS: The review used a scoping approach to explore the current evidence base relating to SA, romantic relationship quality and functioning. Articles published in English after 1980 that reported either clinical or high sub-clinical SA were eligible. Double screening, data extraction, quality assessment, and thematic analysis of studies was conducted. RESULTS: 50 studies from 46 articles were identified, involving a range of community, college, adolescent, and clinical samples. Thematic analysis identified four themes; Relationship Quality, Satisfaction and Commitment; Communication and Self-Disclosure; Conflict, Social Support and Trust; Intimacy, Closeness and Sexual Satisfaction. CONCLUSIONS: The review highlights that evidence relating to romantic relationship functioning for individuals with SAD and high sub-clinical SA is heterogeneous, with relationship initiation in particular relatively under-explored. Further research is required to elucidate key constructs and interpersonal processes related to relationship functioning, and to inform treatment approaches with this group.

Nutrient deprivation and hypoxia alter T cell immune checkpoint expression: potential impact for immunotherapy.

AIM: Use of immune checkpoint blockade to enhance T cell-mediated immunity within the hostile tumour microenvironment (TME) is an attractive approach in oesophageal adenocarcinoma (OAC). This study explored the effects of the hostile TME, including nutrient deprivation and hypoxia, on immune checkpoint (IC) expression and T cell phenotypes, and the potential use of nivolumab to enhance T cell function under such conditions. METHODS AND RESULTS: ICs were upregulated on stromal immune cells within the tumour including PD-L2, CTLA-4 and TIGIT. OAC patient-derived PBMCs co-cultured with OE33 OAC cells upregulated LAG-3 and downregulated the co-stimulatory marker CD27 on T cells, highlighting the direct immunosuppressive effects of tumour cells on T cells. Hypoxia and nutrient deprivation altered the secretome of OAC patient-derived PBMCs, which induced upregulation of PD-L1 and PD-L2 on OE33 OAC cells thus enhancing an immune-resistant phenotype. Importantly, culturing OAC patient-derived PBMCs under dual hypoxia and glucose deprivation, reflective of the conditions within the hostile TME, upregulated an array of ICs on the surface of T cells including PD-1, CTLA-4, A2aR, PD-L1 and PD-L2 and decreased expression of IFN-γ by T cells. Addition of nivolumab under these hostile conditions decreased the production of pro-tumorigenic cytokine IL-10. CONCLUSION: Collectively, these findings highlight the immunosuppressive crosstalk between tumour cells and T cells within the OAC TME. The ability of nivolumab to suppress pro-tumorigenic T cell phenotypes within the hostile TME supports a rationale for the use of immune checkpoint blockade to promote anti-tumour immunity in OAC. Study schematic: (A) IC expression profiles were assessed on CD45+ cells in peripheral whole blood and infiltrating tumour tissue from OAC patients in the treatment-naïve setting. (B) PBMCs were isolated from OAC patients and expanded ex vivo for 5 days using anti-CD3/28 + IL-2 T cell activation protocol and then co-cultured for 48 h with OE33 cells. T cell phenotypes were then assessed by flow cytometry. (C) PBMCs were isolated from OAC patients and expanded ex vivo for 5 days using anti-CD3/28 + IL-2 T cell activation protocol and then further cultured under conditions of nutrient deprivation or hypoxia for 48 h and T cell phenotypes were then assessed by flow cytometry. KEY FINDINGS: (A) TIGIT, CTLA-4 and PD-L2 were upregulated on CD45+ immune cells and CTLA-4 expression on CD45+ cells correlated with a subsequent decreased response to neoadjuvant regimen. (B) Following a 48 h co-culture with OE33 cells, T cells upregulated LAG-3 and decreased CD27 co-stimulatory marker. (C) Nutrient deprivation and hypoxia upregulated a range of ICs on T cells and decreased IFN-γ production by T cells. Nivolumab decreased IL-10 production by T cells under nutrient deprivation-hypoxic conditions.

Batched Bayesian Optimization for Drug Design in Noisy Environments.

The early stages of the drug design process involve identifying compounds with suitable bioactivities via noisy assays. As databases of possible drugs are often very large, assays can only be performed on a subset of the candidates. Selecting which assays to perform is best done within an active learning process, such as batched Bayesian optimization, and aims to reduce the number of assays that must be performed. We compare how noise affects different batched Bayesian optimization techniques and introduce a retest policy to mitigate the effect of noise. Our experiments show that batched Bayesian optimization remains effective, even when large amounts of noise are present, and that the retest policy enables more active compounds to be identified in the same number of experiments.

Improved prediction of gene expression through integrating cell signalling models with machine learning.

BACKGROUND: A key problem in bioinformatics is that of predicting gene expression levels. There are two broad approaches: use of mechanistic models that aim to directly simulate the underlying biology, and use of machine learning (ML) to empirically predict expression levels from descriptors of the experiments. There are advantages and disadvantages to both approaches: mechanistic models more directly reflect the underlying biological causation, but do not directly utilize the available empirical data; while ML methods do not fully utilize existing biological knowledge. RESULTS: Here, we investigate overcoming these disadvantages by integrating mechanistic cell signalling models with ML. Our approach to integration is to augment ML with similarity features (attributes) computed from cell signalling models. Seven sets of different similarity feature were generated using graph theory. Each set of features was in turn used to learn multi-target regression models. All the features have significantly improved accuracy over the baseline model - without the similarity features. Finally, the seven multi-target regression models were stacked together to form an overall prediction model that was significantly better than the baseline on 95% of genes on an independent test set. The similarity features enable this stacking model to provide interpretable knowledge about cancer, e.g. the role of ERBB3 in the MCF7 breast cancer cell line. CONCLUSION: Integrating mechanistic models as graphs helps to both improve the predictive results of machine learning models, and to provide biological knowledge about genes that can help in building state-of-the-art mechanistic models.

A simple spatial extension to the extended connectivity interaction features for binding affinity prediction.

The representation of the protein-ligand complexes used in building machine learning models play an important role in the accuracy of binding affinity prediction. The Extended Connectivity Interaction Features (ECIF) is one such representation. We report that (i) including the discretized distances between protein-ligand atom pairs in the ECIF scheme improves predictive accuracy, and (ii) in an evaluation using gradient boosted trees, we found that the resampling method used in selecting the best hyperparameters has a strong effect on predictive performance, especially for benchmarking purposes.

Testing the reproducibility and robustness of the cancer biology literature by robot.

Scientific results should not just be 'repeatable' (replicable in the same laboratory under identical conditions), but also 'reproducible' (replicable in other laboratories under similar conditions). Results should also, if possible, be 'robust' (replicable under a wide range of conditions). The reproducibility and robustness of only a small fraction of published biomedical results has been tested; furthermore, when reproducibility is tested, it is often not found. This situation is termed 'the reproducibility crisis', and it is one the most important issues facing biomedicine. This crisis would be solved if it were possible to automate reproducibility testing. Here, we describe the semi-automated testing for reproducibility and robustness of simple statements (propositions) about cancer cell biology automatically extracted from the literature. From 12 260 papers, we automatically extracted statements predicted to describe experimental results regarding a change of gene expression in response to drug treatment in breast cancer, from these we selected 74 statements of high biomedical interest. To test the reproducibility of these statements, two different teams used the laboratory automation system Eve and two breast cancer cell lines (MCF7 and MDA-MB-231). Statistically significant evidence for repeatability was found for 43 statements, and significant evidence for reproducibility/robustness in 22 statements. In two cases, the automation made serendipitous discoveries. The reproduced/robust knowledge provides significant insight into cancer. We conclude that semi-automated reproducibility testing is currently achievable, that it could be scaled up to generate a substantive source of reliable knowledge and that automation has the potential to mitigate the reproducibility crisis.

Cooperation between chemotherapy and immune checkpoint blockade to enhance anti-tumour T cell immunity in oesophageal adenocarcinoma.

Response rates to immune checkpoint blockade (ICB) remain low in oesophageal adenocarcinoma (OAC). Combining ICB with immunostimulatory chemotherapies to boost response rates is an attractive approach for converting 'cold' tumours into 'hot' tumours. This study profiled immune checkpoint (IC) expression on circulating and tumour-infiltrating T cells in OAC patients and correlated these findings with clinical characteristics. The effect of first-line chemotherapy regimens (FLOT and CROSS) on anti-tumour T cell immunity was assessed to help guide design of ICB and chemotherapy combinations in the first-line setting. The ability of ICB to enhance lymphocyte-mediated cytolysis of OAC cells in the absence and presence of post-FLOT and post-CROSS chemotherapy tumour cell secretome was assessed by a CCK-8 assay. Expression of ICs on T cells positively correlated with higher grade tumours and a subsequent poor response to neoadjuvant treatment. First-line chemotherapy regimens substantially altered IC expression profiles of T cells increasing PD-1, A2aR, KLRG-1, PD-L1, PD-L2 and CD160 and decreasing TIM-3 and LAG-3. In addition, pro-inflammatory T cell cytokine profiles were enhanced by first-line chemotherapy regimens. T cell activation status was significantly altered; both chemotherapy regimens upregulated co-stimulatory markers ICOS and CD69 yet downregulated co-stimulatory marker CD27. However, ICB attenuated chemotherapy-induced downregulation of CD27 on T cells and promoted differentiation of effector memory T cells into a terminally differentiated state. Importantly, dual nivolumab-ipilimumab treatment increased lymphocyte-mediated cytolysis of OAC cells, an effect further enhanced in the presence of post-FLOT tumour cell secretome. These findings justify a rationale to administer ICBs concurrently with first-line chemotherapies.

Inception-Based Network and Multi-Spectrogram Ensemble Applied To Predict Respiratory Anomalies and Lung Diseases.

This paper presents an inception-based deep neural network for detecting lung diseases using respiratory sound input. Recordings of respiratory sound collected from patients are first transformed into spectrograms where both spectral and temporal information are well represented, in a process referred to as front-end feature extraction. These spectrograms are then fed into the proposed network, in a process referred to as back-end classification, for detecting whether patients suffer from lung-related diseases. Our experiments, conducted over the ICBHI benchmark metadataset of respiratory sound, achieve competitive ICBHI scores of 0.53/0.45 and 0.87/0.85 regarding respiratory anomaly and disease detection, respectively.

Transformational machine learning: Learning how to learn from many related scientific problems.

Almost all machine learning (ML) is based on representing examples using intrinsic features. When there are multiple related ML problems (tasks), it is possible to transform these features into extrinsic features by first training ML models on other tasks and letting them each make predictions for each example of the new task, yielding a novel representation. We call this transformational ML (TML). TML is very closely related to, and synergistic with, transfer learning, multitask learning, and stacking. TML is applicable to improving any nonlinear ML method. We tested TML using the most important classes of nonlinear ML: random forests, gradient boosting machines, support vector machines, k-nearest neighbors, and neural networks. To ensure the generality and robustness of the evaluation, we utilized thousands of ML problems from three scientific domains: drug design, predicting gene expression, and ML algorithm selection. We found that TML significantly improved the predictive performance of all the ML methods in all the domains (4 to 50% average improvements) and that TML features generally outperformed intrinsic features. Use of TML also enhances scientific understanding through explainable ML. In drug design, we found that TML provided insight into drug target specificity, the relationships between drugs, and the relationships between target proteins. TML leads to an ecosystem-based approach to ML, where new tasks, examples, predictions, and so on synergistically interact to improve performance. To contribute to this ecosystem, all our data, code, and our ∼50,000 ML models have been fully annotated with metadata, linked, and openly published using Findability, Accessibility, Interoperability, and Reusability principles (∼100 Gbytes).

A Genetic Trap in Yeast for Inhibitors of SARS-CoV-2 Main Protease.

The ongoing COVID-19 pandemic urges searches for antiviral agents that can block infection or ameliorate its symptoms. Using dissimilar search strategies for new antivirals will improve our overall chances of finding effective treatments. Here, we have established an experimental platform for screening of small molecule inhibitors of the SARS-CoV-2 main protease in Saccharomyces cerevisiae cells, genetically engineered to enhance cellular uptake of small molecules in the environment. The system consists of a fusion of the Escherichia coli toxin MazF and its antitoxin MazE, with insertion of a protease cleavage site in the linker peptide connecting the MazE and MazF moieties. Expression of the viral protease confers cleavage of the MazEF fusion, releasing the MazF toxin from its antitoxin, resulting in growth inhibition. In the presence of a small molecule inhibiting the protease, cleavage is blocked and the MazF toxin remains inhibited, promoting growth. The system thus allows positive selection for inhibitors. The engineered yeast strain is tagged with a fluorescent marker protein, allowing precise monitoring of its growth in the presence or absence of inhibitor. We detect an established main protease inhibitor by a robust growth increase, discernible down to 1 µM. The system is suitable for robotized large-scale screens. It allows in vivo evaluation of drug candidates and is rapidly adaptable for new variants of the protease with deviant site specificities. IMPORTANCE The COVID-19 pandemic may continue for several years before vaccination campaigns can put an end to it globally. Thus, the need for discovery of new antiviral drug candidates will remain. We have engineered a system in yeast cells for the detection of small molecule inhibitors of one attractive drug target of SARS-CoV-2, its main protease, which is required for viral replication. The ability to detect inhibitors in live cells brings the advantage that only compounds capable of entering the cell and remain stable there will score in the system. Moreover, because of its design in yeast cells, the system is rapidly adaptable for tuning the detection level and eventual modification of the protease cleavage site in the case of future mutant variants of the SARS-CoV-2 main protease or even for other proteases.

NERO: a biomedical named-entity (recognition) ontology with a large, annotated corpus reveals meaningful associations through text embedding.

Machine reading (MR) is essential for unlocking valuable knowledge contained in millions of existing biomedical documents. Over the last two decades1,2, the most dramatic advances in MR have followed in the wake of critical corpus development3. Large, well-annotated corpora have been associated with punctuated advances in MR methodology and automated knowledge extraction systems in the same way that ImageNet4 was fundamental for developing machine vision techniques. This study contributes six components to an advanced, named entity analysis tool for biomedicine: (a) a new, Named Entity Recognition Ontology (NERO) developed specifically for describing textual entities in biomedical texts, which accounts for diverse levels of ambiguity, bridging the scientific sublanguages of molecular biology, genetics, biochemistry, and medicine; (b) detailed guidelines for human experts annotating hundreds of named entity classes; (c) pictographs for all named entities, to simplify the burden of annotation for curators; (d) an original, annotated corpus comprising 35,865 sentences, which encapsulate 190,679 named entities and 43,438 events connecting two or more entities; (e) validated, off-the-shelf, named entity recognition (NER) automated extraction, and; (f) embedding models that demonstrate the promise of biomedical associations embedded within this corpus.

The Prognostic Value of the Lymph Node in Oesophageal Adenocarcinoma; Incorporating Clinicopathological and Immunological Profiling.

Response rates to the current gold standards of care for treating oesophageal adenocarcinoma (OAC) remain modest with 15-25% of patients achieving meaningful pathological responses, highlighting the need for novel therapeutic strategies. This study consists of immune, angiogenic, and inflammatory profiling of the tumour microenvironment (TME) and lymph node microenvironment (LNME) in OAC. The prognostic value of nodal involvement and clinicopathological features was compared using a retrospective cohort of OAC patients (n = 702). The expression of inhibitory immune checkpoints by T cells infiltrating tumour-draining lymph nodes (TDLNs) and tumour tissue post-chemo(radio)therapy at surgical resection was assessed by flow cytometry. Nodal metastases is of equal prognostic importance to clinical tumour stage and tumour regression grade (TRG) in OAC. The TME exhibited a greater immuno-suppressive phenotype than the LNME. Our data suggests that blockade of these checkpoints may have a therapeutic rationale for boosting response rates in OAC.

Hypoxia and its impact on the tumour microenvironment of gastroesophageal cancers.

The malfeasant role of the hypoxic tumour microenvironment (TME) in cancer progression was recognized decades ago but the exact mechanisms that augment the hallmarks of cancer and promote treatment resistance continue to be elucidated. Gastroesophageal cancers (GOCs) represent a major burden of worldwide disease, responsible for the deaths of over 1 million people annually. Disentangling the impact of hypoxia in GOCs enables a better overall understanding of the disease pathogenesis while shining a light on novel therapeutic strategies and facilitating precision treatment approaches with the ultimate goal of improving outcomes for patients with these diseases. This review discusses the underlying principles and processes of the hypoxic response and the effect of hypoxia in promoting the hallmarks of cancer in the context of GOCs. We focus on its bidirectional influence on inflammation and how it drives angiogenesis, innate and adaptive immune evasion, metastasis, and the reprogramming of cellular bioenergetics. The contribution of the hypoxic GOC TME to treatment resistance is examined and a brief overview of the pharmacodynamics of hypoxia-targeted therapeutics is given. The principal methods that are used in measuring hypoxia and how they may enhance prognostication or provide rationale for individually tailored management in the case of tumours with significant hypoxic regions are also discussed.

The tumour immune microenvironment in oesophageal cancer.

Oesophageal cancer (OC) is an inflammation-associated malignancy linked to gastro-oesophageal reflux disease, obesity and tobacco use. Knowledge of the microenvironment of oesophageal tumours is relevant to our understanding of the development of OC and its biology, and has major implications for understanding the response to standard therapies and immunotherapies, as well as for uncovering novel targets. In this context, we discuss what is known about the TME in OC from tumour initiation to development and progression, and how this is relevant to therapy sensitivity and resistance in the two major types of OC. We provide an immunological characterisation of the OC TME and discuss its prognostic implications with specific comparison with the Immunoscore and immune-hot, -cold, altered-immunosuppressed and -altered-excluded models. Targeted therapeutics for the TME under pre-clinical and clinical investigation in OCs are also summarised. A deeper understanding of the TME will enable the development of combination approaches to concurrently target the tumour cells and TME delivering precision medicine to OC patients.