Improved Quantitative Plant Proteomics via the Combination of Targeted and Untargeted Data Acquisition.

Quantitative proteomics strategies - which are playing important roles in the expanding field of plant molecular systems biology - are traditionally designated as either hypothesis driven or non-hypothesis driven. Many of these strategies aim to select individual peptide ions for tandem mass spectrometry (MS/MS), and to do this mixed hypothesis driven and non-hypothesis driven approaches are theoretically simple to implement. In-depth investigations into the efficacies of such approaches have, however, yet to be described. In this study, using combined samples of unlabeled and metabolically (15)N-labeled Arabidopsis thaliana proteins, we investigate the mixed use of targeted data acquisition (TDA) and data dependent acquisition (DDA) - referred to as TDA/DDA - to facilitate both hypothesis driven and non-hypothesis driven quantitative data collection in individual LC-MS/MS experiments. To investigate TDA/DDA for hypothesis driven data collection, 7 miRNA target proteins of differing size and abundance were targeted using inclusion lists comprised of 1558 m/z values, using 3 different TDA/DDA experimental designs. In samples in which targeted peptide ions were of particularly low abundance (i.e., predominantly only marginally above mass analyser detection limits), TDA/DDA produced statistically significant increases in the number of targeted peptides identified (230 ± 8 versus 80 ± 3 for DDA; p = 1.1 × 10(-3)) and quantified (35 ± 3 versus 21 ± 2 for DDA; p = 0.038) per experiment relative to the use of DDA only. These expected improvements in hypothesis driven data collection were observed alongside unexpected improvements in non-hypothesis driven data collection. Untargeted peptide ions with m/z values matching those in inclusion lists were repeatedly identified and quantified across technical replicate TDA/DDA experiments, resulting in significant increases in the percentages of proteins repeatedly quantified in TDA/DDA experiments only relative to DDA experiments only (33.0 ± 2.6% versus 8.0 ± 2.7%, respectively; p = 0.011). These results were observed together with uncompromised broad-scale MS/MS data collection in TDA/DDA experiments relative to DDA experiments. Using our observations we provide guidelines for TDA/DDA method design for quantitative plant proteomics studies, and suggest that TDA/DDA is a broadly underutilized proteomics data acquisition strategy

Omada: robust clustering of transcriptomes through multiple testing

Background Cohort studies increasingly collect biosamples for molecular profiling and are observing molecular heterogeneity. High-throughput RNA sequencing is providing large datasets capable of reflecting disease mechanisms. Clustering approaches have produced a number of tools to help dissect complex heterogeneous datasets, but selecting the appropriate method and parameters to perform exploratory clustering analysis of transcriptomic data requires deep understanding of machine learning and extensive computational experimentation. Tools that assist with such decisions without prior field knowledge are nonexistent. To address this, we have developed Omada, a suite of tools aiming to automate these processes and make robust unsupervised clustering of transcriptomic data more accessible through automated machine learning–based functions. Findings The efficiency of each tool was tested with 7 datasets characterized by different expression signal strengths to capture a wide spectrum of RNA expression datasets. Our toolkit’s decisions reflected the real number of stable partitions in datasets where the subgroups are discernible. Within datasets with less clear biological distinctions, our tools either formed stable subgroups with different expression profiles and robust clinical associations or revealed signs of problematic data such as biased measurements. Conclusions In conclusion, Omada successfully automates the robust unsupervised clustering of transcriptomic data, making advanced analysis accessible and reliable even for those without extensive machine learning expertise. Implementation of Omada is available at http://bioconductor.org/packages/omada/

Size Doesn't Matter: Towards a More Inclusive Philosophy of Biology

notes: As the primary author, O’Malley drafted the paper, and gathered and analysed data (scientific papers and talks). Conceptual analysis was conducted by both authors.publication-status: Publishedtypes: ArticlePhilosophers of biology, along with everyone else, generally perceive life to fall into two broad categories, the microbes and macrobes, and then pay most of their attention to the latter. ‘Macrobe’ is the word we propose for larger life forms, and we use it as part of an argument for microbial equality. We suggest that taking more notice of microbes – the dominant life form on the planet, both now and throughout evolutionary history – will transform some of the philosophy of biology’s standard ideas on ontology, evolution, taxonomy and biodiversity. We set out a number of recent developments in microbiology – including biofilm formation, chemotaxis, quorum sensing and gene transfer – that highlight microbial capacities for cooperation and communication and break down conventional thinking that microbes are solely or primarily single-celled organisms. These insights also bring new perspectives to the levels of selection debate, as well as to discussions of the evolution and nature of multicellularity, and to neo-Darwinian understandings of evolutionary mechanisms. We show how these revisions lead to further complications for microbial classification and the philosophies of systematics and biodiversity. Incorporating microbial insights into the philosophy of biology will challenge many of its assumptions, but also give greater scope and depth to its investigations

Clustering pulmonary hypertension patients using the plasma proteome.

Introduction: Patients with pulmonary hypertension are classified according to clinical criteria to inform treatment decisions. Knowledge of the molecular drivers of pulmonary hypertension might better inform treatment choice. Methods: Between 2013 and 2021, 470 patients with pulmonary hypertension, 136 disease controls and 59 healthy controls were enrolled as a discovery cohort. Plasma levels of 7288 proteins were assayed (SomaScan 7K platform). Proteins that distinguished pulmonary hypertension from both control groups were selected for unsupervised clustering (k-means clustering of UMAP dimensions). Clinical characteristics and outcomes were compared across clusters. Separate cohorts of serially sampled patients from pulmonary hypertension centers in the United Kingdom (n=229) and France (n=79) provided independent validation. Results: 156 plasma proteins that distinguished pulmonary hypertension from disease and healthy controls formed 4 clusters with diverse 5-year survival rates: 78% (cluster 4), 62% (cluster 2), 44% (cluster 3), and 33% (cluster 1). The distinction and clinical relevance of the clusters were confirmed in validation cohorts by their association with survival. To further characterise the therapeutic relevance of the clusters we investigated 2 experimental drug targets: the Platelet-Derived Growth Factor (PDGF) pathway was up-regulated in cluster 3 compared to other clusters and the Transforming Growth Factor-β (TGF-β) pathway was up-regulated in cluster 1. Conclusion: Plasma proteomic profiling of patients with pulmonary hypertension distinguishes 4 clusters, independent of the clinical classification. These groups, based on differential plasma protein levels, could act as theragnostic biomarkers for new therapies targeting PDGF and TGF-β pathways. This article is open access and distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/)

Positioning Imatinib for pulmonary arterial hypertension: a dose finding phase 2 study

RATIONALE: Imatinib 400mg daily reduces pulmonary vascular resistance and improves exercise capacity in patients with pulmonary arterial hypertension. Concerns about safety and tolerability limit its use. OBJECTIVES: To identify a safe and tolerated dose of oral imatinib between 100mg and 400mg daily and evaluate its efficacy. METHODS: Oral imatinib was added to the background therapy of 17 patients with pulmonary arterial hypertension, including 13 implanted with devices providing daily measurements of cardiopulmonary haemodynamics and physical activity. The first patient started on 100mg daily. The next 12 patients, recruited serially, started on 200mg, 300mg or 400mg daily, following a Continuous Reassessment Method sequence. An extension cohort (patients 14 to 17) received 100mg or 200mg daily. MEASUREMENTS AND MAIN RESULTS: The Continuous Assessment Method recommended starting dose was 200mg daily. The most common side effect was nausea. Imatinib reduced mean pulmonary artery pressure (-6.5 mmHg, 95%CI -2.4 to -10.6, P<0.01) and total pulmonary resistance (-2.8 Wood Units, 95%CI -1.5 to -4.2, P<0.001) with no significant change in cardiac output. The reduction in total pulmonary resistance was dose and exposure-dependent; the reduction from baseline with imatinib 200mg daily was -20.3% (95%CI -14.3 to -26.3%). Total pulmonary resistance and night heart rate declined steadily over the first 28 days of treatment and remained below baseline up to 40 days following imatinib withdrawal. CONCLUSIONS: Oral imatinib 200mg daily is well tolerated as an add-on treatment in pulmonary arterial hypertension. A delay in the return of cardiopulmonary haemodynamics to baseline was observed after stopping imatinib. Clinical trial registration available at www. CLINICALTRIALS: gov, ID: NCT04416750 This article is open access and distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/)

Two prospective, multicenter studies for the identification of biomarker signatures for early detection of pulmonary hypertension (PH): the CIPHER and CIPHER‐MRI studies

A blood test identifying patients at increased risk of pulmonary hypertension (PH) could streamline the investigative pathway. The prospective, multicenter CIPHER study aimed to develop a microRNA-based signature for detecting PH in breathless patients and enrolled adults with a high suspicion of PH who had undergone right heart catheterization (RHC). The CIPHER-MRI study was added to assess the performance of this CIPHER signature in a population with low probability of having PH who underwent cardiac magnetic resonance imaging (cMRI) instead of RHC. The microRNA signature was developed using a penalized linear regression (LASSO) model. Data were modeled both with and without N-terminal pro-brain natriuretic peptide (NT-proBNP). Signature performance was assessed against predefined thresholds (lower 98.7% CI bound of ≥0.73 for sensitivity and ≥0.53 for specificity, based on a meta-analysis of echocardiographic data), using RHC as the true diagnosis. Overall, 926 CIPHER participants were screened and 888 were included in the analysis. Of 688 RHC-confirmed PH cases, approximately 40% were already receiving PH treatment. Fifty microRNA (from 311 investigated) were algorithmically selected to be included in the signature. Sensitivity [97.5% CI] of the signature was 0.85 [0.80–0.89] for microRNA-alone and 0.90 [0.86–0.93] for microRNA+NT-proBNP, and the corresponding specificities were 0.33 [0.24–0.44] and 0.28 [0.20–0.39]. Of 80 CIPHER-MRI participants with evaluable data, 7 were considered PH-positive by cMRI whereas 52 were considered PH-positive by the microRNA signature. Due to low specificity, the CIPHER miRNA-based signature for PH (either with or without NT-proBNP in model) did not meet the prespecified diagnostic threshold for the primary analysis

Diagnostic MicroRNA signatures to support classification of pulmonary hypertension

BACKGROUND: Patients with pulmonary hypertension (PH) are classified based on disease etiology and hemodynamic drivers. Classification informs treatment. The heart failure biomarker NT-proBNP (N-terminal pro-B-type natriuretic peptide) is used to help inform risk but is not specific to PH or sub-classification groups. There are currently no other biomarkers in clinical use to help guide diagnosis or risk. METHODS: We profiled a retrospective cohort of 1150 patients from 3 expert centers with PH and 334 non-PH symptomatic controls (disease controls) from the United Kingdom to measure circulating levels of 650 microRNAs (miRNAs) in serum. NT-proBNP (ELISA) and 326 well-detected miRNAs (polymerase chain reaction) were prioritized by feature selection using multiple machine learning models. From the selected miRNAs, generalized linear models were used to describe miRNA signatures to differentiate PH and pulmonary arterial hypertension from the disease controls, and pulmonary arterial hypertension, PH due to left heart disease, PH due to lung disease, and chronic thromboembolic pulmonary hypertension from other forms of PH. These signatures were validated on a UK test cohort and independently validated in the prospective CIPHER study (A Prospective, Multicenter, Noninterventional Study for the Identification of Biomarker Signatures for the Early Detection of Pulmonary Hypertension) comprising 349 patients with PH and 93 disease controls. RESULTS: NT-proBNP achieved a balanced accuracy of 0.74 and 0.75 at identifying PH and pulmonary arterial hypertension from disease controls with a threshold of 254 and 362 pg/mL, respectively but was unable to sub-categorize PH subgroups. In the UK cohort, miRNA signatures performed similarly to NT-proBNP in distinguishing PH (area under the curve of 0.7 versus 0.78), and pulmonary arterial hypertension (area under the curve of 0.73 versus 0.79) from disease controls. MicroRNA signatures outperformed NT-proBNP in distinguishing PH classification groups. External testing in the CIPHER cohort demonstrated that miRNA signatures, in conjunction with NT-proBNP, age, and sex, performed better than either NT-proBNP or miRNAs alone in sub-classifying PH. CONCLUSIONS: We suggest a threshold for NT-proBNP to identify patients with a high probability of PH, and the subsequent use of circulating miRNA signatures to help differentiate PH subgroups

Scoping future research for air pollution recovery indicators (APRI). (Workshop report)

Atmospheric nitrogen (N) pollution is a major and ongoing cause of biodiversity loss across the UK, but in some locations N pollution pressures have been declining. In response to these dynamics, JNCC requested a workshop to help to scope Phase 2 of the Air Pollution Recovery Indicators (APRI) project. The damaging effects of excess N load and of gaseous ammonia on many ecosystems are clear. However, the processes and timescales of ecosystem recovery following a decrease in pollution pressure are less well understood. The APRI project aims to take practical steps to fill this knowledge gap by delivering new scientific research focused on indicators of ecosystem and species recovery from N pollution. In Phase 1, predominantly below-ground responses are being studied at a dry heathland site where experimental additions of N were made between 1998 and 2011, revealing lingering effects on soil chemistry, the soil fungi community and vegetation structure (Kowal et al. 2024). The effect on mycorrhizal fungi, and using these fungi as recovery indicators, is being examined in more detail with recently established assessment methods (Arrigoni et al. 2023). Phase 2 of APRI will consider recovery from N impacts more broadly, e.g. by studying other habitats or species. Further empirical research may be commissioned to better understand recovery pathways from air pollution. A workshop was held on 7–8 November 2023 to help develop an action plan for the remainder of the APRI project. This report summarises the workshop discussion and ensuing work. We note that the focus of the APRI project is on assessing recovery. It is therefore essential to contrast responses of ecosystems subject to decreased pollution pressure with indicators from ecosystems experiencing ongoing pollution. Properties that have been used previously to assess impacts can be used to understand recovery, and novel indicators of ecosystem change are also likely to be useful for assessing recovery. Whatever indicators are chosen to assess change, benchmarking data will be needed to assess the range of potential values and relationships with N deposition. Results from the workshop and subsequent discussions include: • Eleven criteria to help choose appropriate indicators in relation to declining N deposition: Speed of response, Sensitivity of response, Specificity of response, Generality to multiple habitats, Relatedness to recovery endpoints, Previous use, Breadth of pollution gradient, Added value to other policy areas, Resilience in face of anticipated change, Feasibility of collection, Measurement uncertainty. • The need to consider a basket of indicators to indicate recovery from N pollution. Such a basket could include examples from different categories e.g. indicators of pressure, biogeochemical response indicators, and biotic response indicators, with individual indicators likely responding over different timescales. The exact choice may depend on the habitat concerned and the availability of prior data, as well as the question being posed and/or policy goal. • Explicit recommendations on sites to target in APRI Phase 2 to gain information on recovery indicator trajectories, namely (i) well-designed field experiments where N addition has ceased, and (ii) point sources of emissions that have ceased to operate, preferably with a super-imposition of an experimental treatment or treatments. Given uncertainties associated with modelled historical, contemporary, and future N deposition and the potential for confounding variables, analysing survey data from across the UK will be unlikely to provide robust information within the timeframes of the APRI Phase 2. We recommend further assessments may help develop detailed plans for empirical work in Phase 2 of APRI. Potential next steps are to: • Finalise a list of potential and priority indicators of recovery from air pollution (which may differ by habitat type), specifically from high levels of N deposition and/or high atmospheric reactive N concentrations. This finalisation could be done through active participation of the air pollution community and the completion of ‘live’ spreadsheets that address potential indicator criteria. • Summarise relevant data on recovery indicators, across key semi-natural habitats. This summary should include data available from other countries with similar environmental contexts, to help disentangle drivers of change in the UK context. This evidence will help understand recovery pathways from air pollution. As above, this could be done through the active participation of the air pollution community and the completion of ‘live’ spreadsheets. Such an approach could also enable gap analyses, for example identifying where we are missing information by habitat and/or environmental conditions. • Identify areas where co-located monitoring of N with existing habitat/species monitoring could enhance the likelihood for establishing recovery indicators. This should enhance other similar activity such as through the Natural Capital and Ecosystem Assessment programme and the UK Air Pollution Impacts on Ecosystems Networks (APIENs). • Develop a list of priority habitats and sites where empirical research is needed to better understand recovery pathways, including a gap analysis of habitats, methods and/or indicators. • Encourage activities that enhance understanding of ammonia emission sources at local scale (e.g. 1 km or less), to help better identify areas where N pollution has decreased, and recovery might be detected. This could include intensive monitoring or collating and sharing information about permitted N sources. • Develop case studies, including potentially from APRI Phase 1, to demonstrate how existing evidence on localised recovery in semi-natural habitats of conservation importance can be used by policy- and decision-makers to help drive policy toward continued reductions in emissions of reactive N