Hierarchical Principal Component Analysis Research Articles

Unraveling Heat Tolerance in Upland Cotton (Gossypium hirsutum L.) Using Univariate and Multivariate Analysis.

The ever-changing global environment currently includes an increasing ambient temperature that can be a devastating stress for organisms. Plants, being sessile, are adversely affected by heat stress in their physiology, development, growth, and ultimately yield. Since little is known about the response of biochemical traits to high-temperature ambiance, we evaluated eight parental lines (five lines and three testers) and their 15 F1 hybrids under normal and high-temperature stress to assess the impact of these conditions over 2 consecutive years. The research was performed under a triplicate randomized complete block design including a split-plot arrangement. Data were recorded for agronomic, biochemical, and fiber quality traits. Mean values of agronomic traits were significantly reduced under heat stress conditions, while hydrogen peroxide, peroxidase, total soluble protein, superoxide dismutase, catalase (CAT), carotenoids, and fiber strength displayed higher mean values under heat stress conditions. Under both conditions, high genetic advance and high heritability were observed for seed cotton yield (SCY), CAT, micronaire value, plant height, and chlorophyll-a and b content, indicating that an additive type of gene action controls these traits under both the conditions. For more insights into variation, Pearson correlation analysis and principal component analysis (PCA) were performed. Significant positive associations were observed among agronomic, biochemical, and fiber quality-related traits. The multivariate analyses involving hierarchical clustering and PCA classified the 23 experimental genotypes into four groups under normal and high-temperature stress conditions. Under both conditions, the F1 hybrid genotype FB-SHAHEEN × JSQ WHITE GOLD followed by Ghuari-1, CCRI-24, Eagle-2 × FB-Falcon, Ghuari-1 × JSQ White Gold, and Eagle-2 exhibited better performance in response to high-temperature stress regarding the agronomic and fiber quality-related traits. The mentioned genotypes could be utilized in future cotton breeding programs to enhance heat tolerance and improve cotton yield and productivity through resistance to environmental stressors.

Pattern Recognition for Steam Flooding Field Applications Based on Hierarchical Clustering and Principal Component Analysis

Pattern Recognition for Steam Flooding Field Applications Based on Hierarchical Clustering and Principal Component Analysis

Assessing the Quality of Calyx of Physalis alkekengi L. var. franchetii Based on Quantitative Analysis of Q-Marker Combined with Chemometrics and Machine Learning Algorithms

Physalis alkekengi L. var. franchetii (PALF) is a traditional Chinese medicine, which is well known for its antimicrobial, anti-inflammatory, antipyretic, and expectorant properties. Its fruits and fruiting calyxes are used as dietary supplements and traditional herbs in China. However, the quality of calyxes is uneven, and it is prone to getting moldy or moth-eaten during storage. High-performance liquid chromatography (HPLC) fingerprints and multivariate chemometric methods were combined to evaluate quality, and three representative compounds were chosen as the quality markers (Q-markers). Hierarchical cluster analysis (HCA) and principal component analysis (PCA) provided a clear discrimination of PALF samples. Through further verification by partial least squares discriminant analysis (PLS-DA), backpropagation artificial neural network (BP-ANN), machine learning, and combination with the determination of the content, biology, and pharmacology effect judgment, galuteolin, rutin, and physalin O could be used as Q-markers that their contents affect the quality of PALF grade evaluation. A simple method was established to rapidly assess the quality of PALF that is important for its clinical application and storage and provide a reference for evaluating the quality of materials used in Chinese medicine.

Diversity of the Seed Material of Selected Plant Species of Naturally Valuable Grassland Habitats in Terms of the Prognosis of Introduction Success

The current conservation status of semi-natural grassland habitats in Poland indicates that actions for their restoration are necessary. Many of the degraded sites require the introduction of diaspores of representative species because other methods of improving their condition are not sufficiently effective. Meanwhile, little is known about the diaspores of native wild-flower species and the biology of their seeds. The aim of the present study was to find an answer to the question of which features of the seed material can guarantee the success of the introduction. The study covered 28 plant species of 4 non-forest natural habitats (codes: 6440, 6410, 6510, 6210) occurring in river valleys. Diaspores were collected in 2015–2017. Morphometric measurements of diaspores were performed, the weight of 1000 diaspores was determined and the germination capacity analysis was carried out in accordance with ISTA Rules. The analysis was made with the division into normal seedlings, abnormal seedlings, dead seeds and fresh ungerminated seeds. Species with similar parameters of seed material were selected using the method of hierarchical clustering and PCA analysis. Three groups of species were distinguished: (1) with good seed germination capacity (above 65%), which, regardless of weather conditions during the generative development, and despite the small size of diaspores, can guarantee successful introductions (Verbascum thapsus, Veronica longifolia, Daucus carota, Plantago lanceolata); (2) species of little suitability for introduction, due to the large proportion (over 50%) of dead seeds (Armeria maritima, Linaria vulgaris, Potentilla erecta, Centaurea stoebe, Sanguisorba officinalis, Cnidium dubium); (3) species with relatively large size of diaspores and low seed germination capacity, due to the high proportion of fresh ungerminated seeds which means dormant seeds (Lathyrus pratensis, Geranium pratense).

Morphological characterization of certain Jasminum sambac genotypes using principal component analysis

Jasminum sambac is one of the most important traditional flower crops of India especially Tamil Nadu. Understanding the genetic variability among the different genotypes of Jasminum sambac helps in its exploitation in breeding programmes. A total of 34 genotypes comprising of traditional landraces collected from all over Tamil Nadu were evaluated for 40 qualitative traits according to the DUS descriptors specified for Jasminum sambac (Kannan M. et al., Plant Variety Journal, 2016). In order to reduce the dimensionality of the data, examine the variation and the relative contribution of the traits for the total variability, Principal Component Analysis (PCA) method was adopted. PCA test in 34 genotypes for all the 40 characters resulted in fifteen Principal Components (PCs) with an eigenvalue more than one accounted for 97.21 per cent of the total variability and revealed that the traits leaf margin undulation, flower bud shape, flower shape, shape of corolla lobe, flower petal tip, leaf blade undulations, flower bud length and root suckers exhibited maximum variation. Agglomerative Hierarchical Clustering and PCA results showed that the genotypes Acc. Js- 11, Acc. Js- 12, Acc. Js- 13, Acc. Js- 14, Acc. Js- 20, Acc. Js- 25, Acc. Js- 27 and Acc. Js- 32 were found to be the most diverse genotypes.

Cognitive Biases on the Iran Stock Exchange: Unsupervised Learning Approach to Examining Feature Bundles in Investors’ Portfolios

This paper innovatively analyses the joint occurrence of cognitive biases in groups of stock exchange investors. It considers jointly a number of common fallacies: confirmation bias, loss aversion, gambler’s fallacy, availability cascade, hot-hand fallacy, bandwagon effect, and Dunning–Kruger effect, which have hitherto been studied separately. The paper aims to highlight the diverse range of investor’s profiles which are characterised by such fallacies, and the considerable differences observed based on their age, stock market experience and perception of market trends. The analysis is based on k-means and hierarchical clustering, feature importance and Principal Component Analysis, which were applied to data from the Tehran Stock Exchange. There are a few essential findings which contribute to the existing literature. Firstly, the results show that gender does not have a role to play in diversifying the investors’ profiles. Secondly, cognitive biases are bundled, and we distinguish four investors’ profiles; thus, they should be analysed jointly, not separately. Thirdly, the exposure to cognitive biases differs significantly due to the individual features of investors. The group most vulnerable to almost all analysed biases are inexperienced investors, who are pessimistic about market developments and have invested a large amount. Fourthly, the ages of investors are essential only in connection with other factors such as experience, market perception and investment exposure. Young (20–40 years), experienced investors with huge investments (+1000 mln rials/+24,000 USD) are mostly less exposed to all biases and much less risk-averse. Additionally, older (50+) and experienced investors (5–10 years) who are more optimistic about trends (hot hand bias) were affected much less by cognitive biases, only showing vulnerability to the Dunning–Kruger effect. Fifthly, more than 40% of investors apply consultation and technical analysis approaches to succeed in trading. Finally, from a methodological perspective, this study shows that unsupervised learning methods are effective in profiling investors and bundling similar behaviours.

EPCO-04. RADIOTHERAPY IS ASSOCIATED WITH GLOBAL METHYLATION ALTERATIONS IN PATIENT DERIVED GLIOBLASTOMA CELL LINES

Abstract PURPOSE/OBJECTIVE(S) In glioblastoma, DNA methylation states are the most predictive marker of overall survival and response to therapy. Our understanding of how epigenetic states, such as DNA methylation, are “mis-repaired” after DNA damage repair is scant, hampering our ability to understand how treatment associated DNA methylation alterations may drive tumor resistance and growth. MATERIALS AND METHODS Three different patient derived IDH wild-type glioma stem cell (GSC) lines, in duplicates, were treated with radiation (20 Gray in 10 fractions vs. sham control) and allowed to recover prior to DNA methylation analysis with 850K methylation arrays. To analyze the methylation array data via bioinformatic methods we used RnBeads (version 2.4.0) and R (version 3.6.1) packages. We further focused our analysis to specific genomic regions, including CpG islands, promoters, gene bodies and CTCF motifs to understand how methylation alterations may differ between these and other genomic contexts following radiation. RESULTS There were widespread differential methylation (pre-treatment vs. radiation treatment) changes among the genomic regions examined. Interestingly, we found differential methylation changes at CTCF motifs, which play important DNA-methylation dependent roles in gene expression and chromatin architecture regulation. Hierarchical clustering, PCA and MDS analysis of DNA methylation status amongst CpG islands, promoters, gene bodies and CTCF domains revealed strong intra-sample differences, but not inter-sample differences (between GSC lines), suggesting radiation associated methylation alterations maybe loci and context dependent. CONCLUSION Radiation treatment is associated with wide-spread alterations of DNA methylation states in this patient derived glioblastoma model. Such alterations may drive gene expression changes or genomic architecture alterations that lead to treatment resistance, warranting further mechanistic investigation of the interplay between radiation induced DNA damage and local epigenetic state restoration following DNA damage repair.

Radiotherapy is Associated With Global Methylation Alterations in Patient Derived Glioblastoma Cell Lines

Despite maximal surgical resection, radiotherapy, chemotherapy and re-treatment at re-occurrence, median overall survival time of glioblastoma (WHO grade IV, IDH wild-type) is estimated to be ∼16 months. In glioma, DNA methylation states are the most predictive marker of overall survival and response to therapy. Our understanding of how epigenetic states, such as DNA methylation, are "mis-repaired" after DNA damage repair is scant, hampering our ability to understand how treatment associated DNA methylation alterations may drive tumor resistance and growth.Three different patient derived glioma stem cell (GSC) lines, in duplicates, were treated with 20 Gy in 10 fractions and allowed to recover prior to DNA methylation analysis with 850K methylation arrays. To analyze the methylation array data, we used RnBeads (version 2.4.0) and R (version 3.6.1) packages. We further focused our analysis to various genomic regions, including CpG islands, promoters, gene bodies and CTCF motifs to understand how methylation alterations may differ between these and other genomic contexts.We found differential methylation (pre-treatment vs. radiation treatment) changes among the genomic regions examined. Interestingly, we found differential methylation changes at CTCF motifs, which play important DNA-methylation dependent roles in gene expression and chromatin architecture regulation. Hierarchical clustering, PCA and MDS analysis amongst CpG islands, promoters, gene bodies and CTCF domains did not reveal strong inter-sample differences that segregated the samples on the basis of treatment status, suggesting radiation associated methylation alterations are context dependent.Radiation treatment is associated with wide-spread alterations of DNA methylation states in this patient derived glioblastoma model. Such alterations may drive gene expression changes, or genomic architecture alterations, that lead to treatment resistance in the recurrent setting.A.S. Modrek: None. R. Ezhilarasan: None. M. Snuderl: None. E.P. Sulman: None.

Differential expression of miRNA in histological subtype of Wilms tumor.

Wilms tumor is the most common renal malignancy in children and difficult to differentiate from other paediatric abdominal tumors radiologically, necessitating an invasive procedure for diagnosis. Previous studies have shown the potential role of miRNA as biomarkers for diagnosis, histological subtyping and prognosis. In this study, we are exploring the role of miRNA in the histological subtyping of Wilms tumor in the Indian population. A total of 15 cases of Wilms tumor were evaluated for global miRNA expression analysis by microarray. Total RNA was extracted from fresh frozen tumor and miRNA expression analysis was performed using Agilent platform. Unsupervised clustering was done to analyse the data. Using unpaired student T test, top 10 significantly differentially expressed miRNA were selected which could differentiate among different histological subtypes by unsupervised hierarchical clustering and principal component analysis. The presence of necrosis, heterologous differentiation led to change in miRNA expression profile and led to a distinct cluster formation. A panel of 5 miRNAs (miR1, 133b, 299-3p, 499a-5p, 491-3p) could differentiate among different histological subtypes of Wilms tumor, thus avoiding an invasive procedure in children, however, further confirmation using real time PCR analysis will be needed.

Chemical Variability and In Vitro Anti-Inflammatory Activity of Leaf Essential Oil from Ivorian Isolona dewevrei (De Wild. & T. Durand) Engl. & Diels

The chemical variability and the in vitro anti-inflammatory activity of the leaf essential oil from Ivorian Isolona dewevrei were investigated for the first time. Forty-seven oil samples were analyzed using a combination of CC, GC(RI), GC-MS and 13C-NMR, thus leading to the identification of 113 constituents (90.8–98.9%). As the main components varied drastically from sample to sample, the 47 oil compositions were submitted to hierarchical cluster and principal components analyses. Three distinct groups, each divided into two subgroups, were evidenced. Subgroup I−A was dominated by (Z)-β-ocimene, β-eudesmol, germacrene D and (E)-β-ocimene, while (10βH)-1β,8β-oxido-cadina-4-ene, santalenone, trans-α-bergamotene and trans-β-bergamotene were the main compounds of Subgroup I−B. The prevalent constituents of Subgroup II−A were germacrene B, (E)-β-caryophyllene, (5αH,10βMe)-6,12-oxido-elema-1,3,6,11(12)-tetraene and γ-elemene. Subgroup II−B displayed germacrene B, germacrene D and (Z)-β-ocimene as the majority compounds. Germacrene D was the most abundant constituent of Group III, followed in Subgroup III−A by (E)-β-caryophyllene, (10βH)-1β,8β-oxido-cadina-4-ene, germacrene D-8-one, and then in Subgroup III−B by (Z)-β-ocimene and (E)-β-ocimene. The observed qualitative and quantitative chemical variability was probably due to combined factors, mostly phenology and season, then harvest site to a lesser extent. The lipoxygenase inhibition by a leaf oil sample was also evaluated. The oil IC50 (0.020 ± 0.005 mg/mL) was slightly higher than the non-competitive lipoxygenase inhibitor NDGA IC50 (0.013 ± 0.003 mg/mL), suggesting a significant in vitro anti-inflammatory potential.

Diversity analysis of moringa (Moringa oleifera Lam.) genotypes using DUS descriptors

Moringa is a crop with wide variability and is known as the miracle tree due to its application in various fields as food, medicine, plant growth stimulator and animal feed because of its nutritional, pharmacological, biotechnological potential. Understanding the genetic potential of the crop helps in the exploitation of existing variability in breeding programmes. Tamil Nadu, being the state pioneering in moringa cultivation with diverse genotypes, a total of 32 genotypes collected from different regions of the state were selected to study the variability in order to identify best performing genotypes. Principal component analysis for the selected 25 morphological characters elucidated that nine principal components with eigenvalue more than one accounted for 83.59 per cent of the total variability and also revealed that maximum variation was exhibited by the number of pods per cluster, the number of pods per branch, anthocyanin colouration in petiole, pod shape, pod length and plant growth habit whereas remaining traits exhibited low variability. Based on Agglomerative Hierarchical Clustering and PCA the genotypes CBE MO 1, CBE MO 13, CBE MO 19, CBE MO 28 and CBE MO 29 were identified as the most diverse genotypes. Key words: Moringa, PCA, cluster, variability, DUS, descriptors

Association between serum markers of the humoral immune system and inflammation in the Swedish AMORIS study

BackgroundAlthough the onset of inflammatory cascades may profoundly influence the nature of antibody responses, the interplay between inflammatory and humoral (antibody) immune markers remains unclear. Thus, we explored the reciprocity between the humoral immune system and inflammation and assessed how external socio-demographic factors may influence these interactions. From the AMORIS cohort, 5513 individuals were identified with baseline measurements of serum humoral immune [immunoglobulin G, A & M (IgG, IgA, IgM)] and inflammation (C-reactive protein (CRP), albumin, haptoglobin, white blood cells (WBC), iron and total iron-binding capacity) markers measured on the same day. Correlation analysis, principal component analysis and hierarchical clustering were used to evaluate biomarkers correlation, variation and associations. Multivariate analysis of variance was used to assess associations between biomarkers and educational level, socio-economic status, sex and age.ResultsFrequently used serum markers for inflammation, CRP, haptoglobin and white blood cells, correlated together. Hierarchical clustering and principal component analysis confirmed the interaction between these main biological responses, showing an acute response component (CRP, Haptoglobin, WBC, IgM) and adaptive response component (Albumin, Iron, TIBC, IgA, IgG). A socioeconomic gradient associated with worse health outcomes was observed, specifically low educational level, older age and male sex were associated with serum levels that indicated infection and inflammation.ConclusionsThese findings indicate that serum markers of the humoral immune system and inflammation closely interact in response to infection or inflammation. Clustering analysis presented two main immune response components: an acute and an adaptive response, comprising markers of both biological pathways. Future studies should shift from single internal marker assessment to multiple humoral and inflammation serum markers combined, when assessing risk of clinical outcomes such as cancer.

FC 019PROTEOMIC PROFILING OF GLOMERULI FROM KIDNEYS WITH HYPERTENSIVE NEPHROPATHY REVEALS SIGNATURE OF DISEASE PROGRESSION

Abstract Background and Aims Hypertensive nephropathy (HN) often represents an unspecific clinical diagnosis applied to non-diabetic, chronic kidney disease (CKD) patients with low-level proteinuria and elevated blood pressure. Kidney biopsy-based findings are the diagnostic gold standard, but they are not entirely specific and there is a need for prognostic markers. We aimed at defining candidate markers that predict disease progression based on protein signatures. Method We included adult patients (n=17) with an eGFR >30 ml/min/1.73m2 and proteinuria <3g/d from the Norwegian Kidney Biopsy Registry (NKBR). Subjects were divided into two groups: stable patients (n=9) and subjects with HN progression (n=8) leading to end-stage renal disease (ESRD) within 20 years of follow-up. Glomerular cross-sections were microdissected from 10 μm whole archival kidney biopsy sections and processed for protein extraction. Proteomic analyses were performed at our local facility PROBE in Bergen using a Q-exactive HF mass spectrometer. Abundances of glomerular proteins were compared between the two groups. Results Amongst a total of 1870 quality filtered proteins, we identified 58 proteins with absolute fold change (FC) ≥1.5, p≤0.05, including 17 proteins with absolute FC ≥2, indicative of HN progression (highest FC: Cadherin 16 and UDP-glucuronosyl-transferase 2B7). Hierarchical cluster and principal component analysis (PCA) with the 17 proteins showed clear separation of samples into these two disease clusters of HN progressors and non-progressors. To find proteins as biomarkers for the identification of progressors, we employed unsupervised K nearest neighbors validation algorithm coupled with leave-one-out internal cross-validation. Thereby, a set of five proteins (incl. cadherin 16) performed best and separated the two groups in a PCA, as shown in Figure 1. This classifier classified 16 of 17 samples correctly (AUC 0.993), misclassifying only one progressor sample. Applying Geneset Enrichment Analysis (GSEA; The Broad Institute, USA), in general metabolic pathways were up-regulated in progressors and structural cell pathways up-regulated in non-progressors. Ingenuity Pathway Analysis (IPA; Qiagen, USA) identified Epithelial Adherens Junction Signaling as the most affected canonical pathway (p=1.95E-06); five of six member proteins were down-regulated in progressors. Conclusion Glomerular proteomic profiling can be used to distinct progressors from non-progressors in HN.

Abstract PS5-34: Identification of proteomics-based biomarkers for ER+/HER2- breast cancer stratification: Implications on clinical outcome

Abstract Introduction: Improving stratification of breast cancer (BC) patients based on molecular signatures for treatment responses and clinical outcomes is a critical unmet need. Currently, endocrine therapy is first-line for hormone receptor positive (HR+) BC. Chemotherapy is added to patients with high-risk luminal BC. In practice, levels of Ki-67 are used to distinguish luminal tumors as low-risk luminal A (LA) and high-risk luminal B (LB) for adjuvant therapy decisions. Herein, proteomic tumor assessment from ER-positive HER2-negative (ER+/HER2-) BC patients was utilized to define molecular subtyping, estimate congruency between proteomic subtyping and traditionally used Ki67 marker, and define a new set of potential predictive and prognostic therapeutic biomarkers for ER+/HER2- BC patients. Method/Result: Clinical immunohistochemistry (IHC) subtyping of core biopsies was used to select a cohort of 86 BC patients with ER+/HER2- primary tumors from flash-frozen surgical samples. The positive/negative status of ER/PR/HER2 was defined using updated ASCO 2020 guidelines. Ki-67 status was determined using the 2011 St. Gallen’s International Expert Consensus recommendations. The cohort includes 28 LA (Ki67 < 14%) cases and 58 LB1 (Ki67 >= 14%) cases. Integrated consensus clustering algorithms with the most varying proteins in our cohort were applied to identify proteomic subtypes. Two distinct separations were observed from the analysis, resulting in one cluster enriched with LA (40 cases) and the other enriched with LB1 (46 cases) called by Fisher’s exact test. These clusters matched 100% with the clusters generated using 900+ proteins common to the 1500+ proteins used in the CPTAC-BC proteomics-based subtyping analysis (Mertins et al. Nature 2016). The differential analyses demonstrated that there is no significant difference between Ki67-defined subtypes and proteomics-defined subtypes (LA-enriched vs. LA cases, LB1-enriched vs. LB1 cases),indicating they are consistent in the molecular profile. Differential analysis was performed to compare LB1-enriched versus LA-enriched cases, resulting in 672 significantly differentially expressed proteins defined at false discovery rate (FDR) < 0.05 and |log2(fold change)|>1. 353 of the 672 proteins were correlated with mRNA at Pearson correlation > 0.39 as reported in the CPTAC-BC study or cBioPortal for Cancer Genome, and their coding genes were used for progression free interval (PFI) analysis based on TCGA RNA-seq data in the TCGA ER+/HER2- cases (662 cases, c.f. Huo et al. JAMA Oncology 2017). 90 of the 353 coding genes significantly associated with PFI were detected at p-value<0.05. Unsupervised hierarchical clustering method and principal component analysis (PCA) of the 90 genes were applied to our cohort to investigate the clustering performance and 94.2% of the cases were clustered correctly using support vector machine (SVM) method after PCA analysis. Biological process and molecular function GO term over-representation analyses of the 90 coding genes were performed separately. Some significant and biologically meaningful GO terms were identified at FDR<0.05. Conclusions: We identified a set of biomarkers that can be potentially employed as proteomic or gene signatures to stratify ER+/HER2- BC into low risk and high-risk groups. Disclaimers The contents of this publication are the sole responsibility of the author(s) and do not necessarily reflect the views, opinions, or policies of Uniformed Services University of the Health Sciences, The Henry M Jackson Foundation for the Advancement of Military Medicine Inc., the Department of Defense or the Departments of the Army, Navy or Air Force. Mention of trade names, commercial products, or organizations does not imply endorsement by the U.S. Government. Citation Format: Guisong Wang, Punit Shah, Rick Searfoss, J. Leigh Fantacone Campbell, Jeffrey A. Hooke, Brenda Deyarmin, Rebecca N. Zingmark, Stella Somiari, Jianfang Liu, Leonid Kvecher, Lori A. Sturtz, Praveen-Kumar Raj-Kumar, Elder Granger, Linda Vahdat, Mary L. Cutler, Rangaprasad Sarangarajan, Hai Hu, Michael A. Kiebish, Albert J. Kovatich, Niven R. Narain, Craig D. Shriver. Identification of proteomics-based biomarkers for ER+/HER2- breast cancer stratification: Implications on clinical outcome [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS5-34.

Functionally selective activation of the dopamine receptor D2 is mirrored by the protein expression profiles

The development of functionally selective or biased ligands is a promising approach towards drugs with less side effects. Biased ligands for G protein-coupled receptors can selectively induce G protein activation or β-arrestin recruitment. The consequences of this selective action on cellular functions, however, are not fully understood. Here, we investigated the impact of five biased and balanced dopamine D2 receptor agonists and antagonists on the global protein expression in HEK293T cells by untargeted nanoscale liquid chromatography–tandem mass spectrometry. The proteome analysis detected 5290 protein groups. Hierarchical clustering and principal component analysis based on the expression levels of 1462 differential proteins led to a separation of antagonists and balanced agonist from the control treatment, while the biased ligands demonstrated larger similarities to the control. Functional analysis of affected proteins revealed that the antagonists haloperidol and sulpiride regulated exocytosis and peroxisome function. The balanced agonist quinpirole, but not the functionally selective agonists induced a downregulation of proteins involved in synaptic signaling. The β-arrestin-preferring agonist BM138, however, regulated several proteins related to neuron function and the dopamine receptor-mediated signaling pathway itself. The G protein-selective partial agonist MS308 influenced rather broad functional terms such as DNA processing and mitochondrial translation.

Discrimination of responses of corn genotypes to drought through physiological, growth, and yield traits

Abstract: The objective of this work was to evaluate different traits of four corn (Zea mays) genotypes with contrasting responses to drought and to determine the main traits associated to such responses. The experiment was carried out in a greenhouse. The plants were grown in pots subjected to full irrigation. Drought was imposed to plants at 54 days after sowing and kept constant for 12 consecutive days; however, a group of plants remained under full irrigation. Traits related to leaf gas exchange, photochemical apparatus, growth, and yield were assessed, and data were subjected to hierarchical agglomerative clustering and principal component analysis. DKB 390 distinguishes from the other genotypes for growth and yield traits, while 2B-707 and DKB 390 discriminate from 'BRS 1030' and 'BRS 1010' for physiological traits. Ear length, kernel number per ear, above-ground dry matter, shoot dry matter, and plant height are the most important growth and yield traits to discriminate genotype-dependent drought tolerance. Among the physiological traits, the most important are: chlorophyll content, absorptivity, leaf temperature, maximum fluorescence in the dark-adapted state, minimum fluorescence in the dark-adapted state, water-use efficiency, and intercellular CO2 concentration.

Liquid Chromatography-High-Resolution Mass Spectrometry-Based In Vitro Toxicometabolomics of the Synthetic Cathinones 4-MPD and 4-MEAP in Pooled Human Liver Microsomes.

Synthetic cathinones belong to the most often seized new psychoactive substances on an international level. This study investigated the toxicometabolomics, particularly the in vitro metabolism of 2-(methylamino)-1-(4-methylphenyl)-1-pentanone (4-MPD) and 2-(ethylamino)-1-(4-methylphenyl)-1-pentanone (4-MEAP) in pooled human liver microsomes (pHLM) using untargeted metabolomics techniques. Incubations were performed with the substrates in concentrations ranging from 0, 12.5, and 25 µM. Analysis was done by means of high-performance liquid chromatography coupled to high-resolution mass spectrometry (HPLC-HRMS/MS) in full scan only and the obtained data was evaluated using XCMS Online and MetaboAnalyst. Significant features were putatively identified using a separate parallel reaction monitoring method. Statistical analysis was performed using Kruskal-Wallis test for prefiltering significant features and subsequent hierarchical clustering, as well as principal component analysis (PCA). Hierarchical clustering or PCA showed a distinct clustering of all concentrations with most of the features z-scores rising with the concentration of the investigated substances. Identification of significant features left many of them unidentified but revealed metabolites of both 4-MPD and 4-MEAP. Both substances formed carboxylic acids, were hydroxylated at the alkyl chain, and formed metabolites after combined hydroxylation and reduction of the cathinone oxo group. 4-MPD additionally formed a dihydroxy metabolite and a hydroxylamine. 4-MEAP was additionally found reduced at the cathinone oxo group, N-dealkylated, and formed an oxo metabolite. These findings are the first to describe the metabolic pathways of 4-MPD and to extend our knowledge about the metabolism of 4-MEAP. Findings, particularly the MS data of the metabolites, are essential for setting up metabolite-based toxicological (urine) screening procedures.

A comparative study of chromatographic lipophilicity and bioactivity parameters of selected spirohydantoins

In this study, lipophilicity of 21 cycloalkylspiro-5-hidantoins was assessed. The overall goal of lipophilicity evaluation is preliminary investigation which should result in a decrease of the traditionally high attrition rates for compounds entering clinical trials. Lipophilicity assessment was done by reversed-phase thin-layer chromatography and in silico methods. Chromatographic analyses were performed on C-18 modified thin-layer of silica gel with a two-component mobile phase consisting of water and organic solvent (acetonitrile, acetone, dioxane, or tetrahydrofuran) in different ratios. The chromatographic lipophilicities (R M 0) were discussed and compared with computational log Ps calculated with various algorithms as well as in silico ADMET descriptors using linear regression and multivariate approach (hierarchical cluster analysis and principal component analysis). A high correlation was obtained between R M 0 values and calculated log P indicating a strong relationship between the variables. Multivariate data analysis enabled the comparison of the chemical structures, lipophilicity, pharmacokinetic predictors and toxicity of the investigated compounds.

Abstract 13549: GP130 Signaling Promotes Right Ventricular Dysfunction in Pulmonary Arterial Hypertension via Microtubules

Introduction: Pulmonary arterial hypertension (PAH) is a lethal pulmonary arterial vasculopathy that results in right ventricle dysfunction (RVD). We showed that microtubule-mediated junctophilin-2 (MT-JPH2 pathway) downregulation promotes t-tubule disruption and RVD; however, the upstream regulators of this pathway are unknown. GP130 signaling via its downstream mediator, STAT3 promotes microtubule remodeling in neonatal cardiomyocytes. However, the relationship between GP130 signaling and the MT-JPH2 pathway in RVD are unknown. Methods: Immunoblots of RV extracts were probed for the GP130 and the MT-JPH2 pathways in MCT rats treated with a GP130 antagonist (2 weeks after MCT injection). Quantitative mass spectrometry analyzed the microtubule associated protein (MAP) fraction of RV extracts. Echocardiography and invasive closed-chest right heart catheterization quantified RV function and pulmonary vascular disease. Pulmonary vasculature remodeling was assessed by H&E histology. Finally, the relationship between the GP130 agonist, interleukin (IL)-6, and RVD in PAH patients was examined. Results: GP130 antagonist treatment blunted RV STAT3 activation, normalized the MT-JPH2 pathway, and restored t-tubule architecture. Hierarchical cluster analysis and principal component analysis of 2842 proteins identified using mass spectrometry revealed normalization of the MAP-fraction with GP130 antagonist treatment. These molecular changes manifested as improved RV function, improved RV-pulmonary artery coupling, blunted RV hypertrophy, and improved survival despite no differences in pulmonary vascular disease severity. In PAH patients, higher IL-6 levels were associated with higher N-terminal pro-brain natriuretic peptide and lower RV fractional area change despite no differences in pulmonary vascular disease burden. Conclusions: Small molecular inhibition of GP130 enhances RV function, and improves survival in MCT PAH via modulation of the MT-JPH2 pathway.

Diversity of Sambucus nigra pollen within Slovakia in selected morphological characters by SEM study

This study analyzed pollen morphology and characteristics of 20 Sambucus nigra L. genotypes from populations of various ecotypes and geographical localities from the territory of Slovakia. We used principal component analysis to explore variability in pollen grain size (polar and equatorial diameter), shape, aperture type, and exine ornamentation by scanning electron microscopy. The combination of these morphological characteristics and ultrastructure allows us to determine the differences or similarities between the same and various species and genotypes, which may be a useful tool for systematics with significant diagnostic value. The findings confirmed small differences among the genotypes in measured traits with polar and equatorial diameters in the range from 22.30 to 26.64 μm and from 12.81 to 14.45 μm, respectively. Shape index (P/E ratio) depending on elongation or roundness of pollen grains varied from 1.66 to 2.02. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) of morphological data helped to compare evaluated morphometric parameters and identified three closely related groups. It was noted that the diversity of surface sculpturing of pollen grains in combination with their shape and size enables us to use a complex of fine morphologic signs for S. nigra pollen identification. Pollen data combined with other morphological evidence (e.g., floral characters) have more recently become an important indicator of which genotypes may be the best representatives of species.