Psychobiological stress response to a lung cancer diagnosis: a prospective study of patients in Iceland and Sweden.

BACKGROUND: A diagnostic work-up leading to a lung cancer diagnosis is a severely stressful experience that may impact tumor progression. Yet, prospective data are scarce on psychological and biological components of stress at the time of lung cancer diagnosis. The aim of this study was to assess pre-to-post diagnosis change in psychological distress and urinary excretion of catecholamines in patients with suspected lung cancer. METHODS: Participants were 167 patients within the LUCASS study, recruited at referral for suspected lung cancer to University Hospitals in Iceland and Sweden. Patients completed questionnaires on perceived distress (Hospital Anxiety and Depression Scale, HADS) before and after diagnosis of lung cancer or a non-malignant origin. A subpopulation of 85 patients also provided overnight urine for catecholamine analysis before and at a median of 24 days after diagnosis but before treatment. RESULTS: A lung cancer diagnosis was confirmed in 123 (73.7%) patients, with a mean age of 70.1 years. Patients diagnosed with lung cancer experienced a post-diagnosis increase in psychological distress (p = 0.010), while patients with non-malignant lung pathology showed a reduction in distress (p = 0.070). Both urinary epinephrine (p = 0.001) and norepinephrine (p = 0.032) levels were higher before the diagnosis among patients eventually diagnosed with lung cancer compared to those with non-malignant lung pathology. We observed indications of associations between pre-to-post diagnosis changes in perceived distress and changes in urinary catecholamine levels. CONCLUSION: Receiving a lung cancer diagnosis is associated with an increase in psychological distress, while elevated catecholamine levels are evident already before lung cancer diagnosis.

AutoCCS: automated collision cross-section calculation software for ion mobility spectrometry-mass spectrometry.

MOTIVATION: Ion mobility spectrometry (IMS) separations are increasingly used in conjunction with mass spectrometry (MS) for separation and characterization of ionized molecular species. Information obtained from IMS measurements includes the ion's collision cross section (CCS), which reflects its size and structure and constitutes a descriptor for distinguishing similar species in mixtures that cannot be separated using conventional approaches. Incorporating CCS into MS-based workflows can improve the specificity and confidence of molecular identification. At present, there is no automated, open-source pipeline for determining CCS of analyte ions in both targeted and untargeted fashion, and intensive user-assisted processing with vendor software and manual evaluation is often required. RESULTS: We present AutoCCS, an open-source software to rapidly determine CCS values from IMS-MS measurements. We conducted various IMS experiments in different formats to demonstrate the flexibility of AutoCCS for automated CCS calculation: (i) stepped-field methods for drift tube-based IMS (DTIMS), (ii) single-field methods for DTIMS (supporting two calibration methods: a standard and a new enhanced method) and (iii) linear calibration for Bruker timsTOF and non-linear calibration methods for traveling wave based-IMS in Waters Synapt and Structures for Lossless Ion Manipulations. We demonstrated that AutoCCS offers an accurate and reproducible determination of CCS for both standard and unknown analyte ions in various IMS-MS platforms, IMS-field methods, ionization modes and collision gases, without requiring manual processing. AVAILABILITY AND IMPLEMENTATION: https://github.com/PNNL-Comp-Mass-Spec/AutoCCS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Demo datasets are publicly available at MassIVE (Dataset ID: MSV000085979).

Transcriptomic analysis of field-droughted sorghum from seedling to maturity reveals biotic and metabolic responses.

Drought is the most important environmental stress limiting crop yields. The C4 cereal sorghum [Sorghum bicolor (L.) Moench] is a critical food, forage, and emerging bioenergy crop that is notably drought-tolerant. We conducted a large-scale field experiment, imposing preflowering and postflowering drought stress on 2 genotypes of sorghum across a tightly resolved time series, from plant emergence to postanthesis, resulting in a dataset of nearly 400 transcriptomes. We observed a fast and global transcriptomic response in leaf and root tissues with clear temporal patterns, including modulation of well-known drought pathways. We also identified genotypic differences in core photosynthesis and reactive oxygen species scavenging pathways, highlighting possible mechanisms of drought tolerance and of the delayed senescence, characteristic of the stay-green phenotype. Finally, we discovered a large-scale depletion in the expression of genes critical to arbuscular mycorrhizal (AM) symbiosis, with a corresponding drop in AM fungal mass in the plants' roots.

Molecular imaging of plant-microbe interactions on the Brachypodium seed surface.

Plant growth-promoting rhizobacteria (PGPR) play a crucial role in biological control and pathogenic defense on and within plant tissues, however the mechanisms by which plants associate with PGPR to elicit such beneficial effects need further study. Here, we present time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging of Brachypodium distachyon (Brachypodium) seeds with and without exposure to two model PGPR, i.e., Gram-negative Pseudomonas fluorescens SBW25 (P.) and Gram-positive Arthrobacter chlorophenolicus A6 (A.). Delayed image extraction was used to image PGPR-treated seed sections to reveal morphological changes. ToF-SIMS spectral comparison, principal component analysis (PCA), and two-dimensional (2D) imaging show that the selected PGPR have different effects on the host seed surface, resulting in changes in chemical composition and morphology. Metabolite products and biomarkers, such as flavonoids, phenolic compounds, fatty acids, and indole-3-acetic acid (IAA), were identified on the PGPR-treated seed surfaces. These compounds have different distributions on the Brachypodium seed surface for the two PGPR, indicating that the different bacteria elicit distinct responses from the host. Our results illustrate that ToF-SIMS is an effective tool to study plant-microbe interactions and to provide insightful information with submicrometer lateral resolution of the chemical distributions associated with morphological features, potentially offering a new way to study the mechanisms underlying beneficial roles of PGPR.

Top-down mass spectrometry of histone modifications in sorghum reveals potential epigenetic markers for drought acclimation.

Sorghum [Sorghum bicolor (L.) Moench] is an important cereal crop noted for its ability to survive water-limiting conditions. Herein, we present an analytical workflow to explore the changes in histone modifications through plant developmental stages and two drought stresses in two sorghum genotypes that differ in their response to drought. Top-down mass spectrometry (MS) is an ideal method to profile histone modifications and distinguish closely related histone proteoforms. We analyzed leaves of 48 plants and identified 26 unique histone proteins and 677 unique histone proteoforms (124 full-length and 553 truncated proteoforms). We detected trimethylation on nearly all H2B N-termini where acetylation is commonly expected. In addition, an unexpected modification on H2A histones was assigned to N-pyruvic acid 2-iminylation based on its unique neutral loss of CO2. Interestingly, some of the truncated histones, in particular H4 and H3.2, showed significant changes that correlated with the growth and water conditions. The histone proteoforms could serve as targets in search of chromatin modifiers and ultimately have important ramifications in future attempts of studying plant epigenetic reprogramming under stress.

Drought delays development of the sorghum root microbiome and enriches for monoderm bacteria.

Drought stress is a major obstacle to crop productivity, and the severity and frequency of drought are expected to increase in the coming century. Certain root-associated bacteria have been shown to mitigate the negative effects of drought stress on plant growth, and manipulation of the crop microbiome is an emerging strategy for overcoming drought stress in agricultural systems, yet the effect of drought on the development of the root microbiome is poorly understood. Through 16S rRNA amplicon and metatranscriptome sequencing, as well as root metabolomics, we demonstrate that drought delays the development of the early sorghum root microbiome and causes increased abundance and activity of monoderm bacteria, which lack an outer cell membrane and contain thick cell walls. Our data suggest that altered plant metabolism and increased activity of bacterial ATP-binding cassette (ABC) transporter genes are correlated with these shifts in community composition. Finally, inoculation experiments with monoderm isolates indicate that increased colonization of the root during drought can positively impact plant growth. Collectively, these results demonstrate the role that drought plays in restructuring the root microbiome and highlight the importance of temporal sampling when studying plant-associated microbiomes.

Correlative surface imaging reveals chemical signatures for bacterial hotspots on plant roots.

The rhizosphere is arguably the most complex microbial habitat on Earth, comprising an integrated network of plant roots, soil and a highly diverse microbial community (the rhizosphere microbiome). Understanding, predicting and controlling plant-microbe interactions in the rhizosphere will allow us to harness the plant microbiome as a means to increase or restore plant ecosystem productivity, improve plant responses to a wide range of environmental perturbations, and mitigate the effects of climate change by designing ecosystems for long-term soil carbon storage. To this end, it is imperative to develop new molecular approaches with high spatial resolution to capture interactions at the plant-microbe, microbe-microbe, and plant-plant interfaces. In this work, we designed an imaging sample holder that allows integrated surface imaging tools to map the same locations of a plant root-microbe interface with submicron lateral resolutions, providing novel in vivo analysis of root-microbe interactions. Specifically, confocal fluorescence microscopy, time-of-flight secondary ion mass spectrometry (ToF-SIMS), X-ray photoelectron spectroscopy (XPS), and scanning electron microscopy (SEM) were used for the first time for the correlative imaging of the Brachypodium distachyon root and its interaction with Pseudomonas SW25, a typical plant growth-promoting soil bacterium. Imaging data suggest that the root surface is inhomogeneous and that the interaction between Pseudomonas and Brachypodium roots was confined to only a few spots along the sampled root segments and that the bacterial attachment spots were enriched in Na- and S-related and high-mass organic species. We conclude that the attachment of the Pseudomonas cells to the root surface is outcompeted by strong root-soil mineral interactions but facilitated by the formation of extracellular polymeric substances (EPS).

Association mapping by aerial drone reveals 213 genetic associations for Sorghum bicolor biomass traits under drought.

BACKGROUND: Sorghum bicolor is the fifth most commonly grown cereal worldwide and is remarkable for its drought and abiotic stress tolerance. For these reasons and the large size of biomass varieties, it has been proposed as a bioenergy crop. However, little is known about the genes underlying sorghum's abiotic stress tolerance and biomass yield. RESULTS: To uncover the genetic basis of drought tolerance in sorghum at a genome-wide level, we undertook a high-density phenomics genome wide association study (GWAS) in which 648 diverse sorghum lines were phenotyped at two locations in California once per week by drone over the course of a growing season. Biomass, height, and leaf area were measured by drone for individual field plots, subjected to two drought treatments and a well-watered control. The resulting dataset of ~ 171,000 phenotypic data-points was analyzed along with 183,989 genotype by sequence markers to reveal 213 high-quality, replicated, and conserved GWAS associations. CONCLUSIONS: The genomic intervals defined by the associations include many strong candidate genes, including those encoding heat shock proteins, antifreeze proteins, and other domains recognized as important to plant stress responses. The markers identified by our study can be used for marker assisted selection for drought tolerance and biomass. In addition, our results are a significant step toward identifying specific sorghum genes controlling drought tolerance and biomass yield.

Moving beyond the van Krevelen Diagram: A New Stoichiometric Approach for Compound Classification in Organisms.

van Krevelen diagrams (O/C vs H/C ratios of elemental formulas) have been widely used in studies to obtain an estimation of the main compound categories present in environmental samples. However, the limits defining a specific compound category based solely on O/C and H/C ratios of elemental formulas have never been accurately listed or proposed to classify metabolites in biological samples. Furthermore, while O/C vs H/C ratios of elemental formulas can provide an overview of the compound categories, such classification is inefficient because of the large overlap among different compound categories along both axes. We propose a more accurate compound classification for biological samples analyzed by high-resolution mass spectrometry based on an assessment of the C/H/O/N/P stoichiometric ratios of over 130 000 elemental formulas of compounds classified in 6 main categories: lipids, peptides, amino sugars, carbohydrates, nucleotides, and phytochemical compounds (oxy-aromatic compounds). Our multidimensional stoichiometric compound classification (MSCC) constraints showed a highly accurate categorization of elemental formulas to the main compound categories in biological samples with over 98% of accuracy representing a substantial improvement over any classification based on the classic van Krevelen diagram. This method represents a signficant step forward in environmental research, especially ecological stoichiometry and eco-metabolomics studies, by providing a novel and robust tool to improve our understanding of the ecosystem structure and function through the chemical characterization of biological samples.

Climate-smart crops with enhanced photosynthesis.

The potential of enhanced photosynthetic efficiency to help achieve the sustainable yield increases required to meet future demands for food and energy has spurred intense research towards understanding, modeling, and engineering photosynthesis. These current efforts, largely focused on the C3 model Arabidopsis thaliana or crop plants (e.g. rice, sorghum, maize, and wheat), could be intensified and broadened using model systems closely related to our food, feed, and energy crops and that allow rapid design-build-test-learn cycles. In this outlooking Opinion, we advocate for a concerted effort to expand our understanding and improve our ability to redesign carbon uptake, allocation, and utilization. We propose two specific research directions that combine enhanced photosynthesis with climate-smart metabolic attributes: (i) engineering pathways for flexible (facultative) C3-C4 metabolism where plants will operate either C3 or C4 photosynthesis based on environmental conditions such as temperature, light, and atmospheric CO2 levels; and (ii) increasing rhizospheric sink strength for carbon utilization, including strategies that allow for augmented transport of carbon to the soil for improved soil properties and carbon storage without jeopardizing aboveground crop biomass. We argue that such ambitious undertakings be first approached and demonstrated by exploring the full genomic potential of two model grasses, the C3Brachypodium distachyon and the C4Setaria viridis. The development of climate-smart crops could provide novel and bold solutions to increase crop productivity while reducing atmospheric carbon and nitrogen emissions.