Global Analysis Research Articles

ASpedia-R: a package to retrieve junction-incorporating features and knowledge-based functions of human alternative splicing events

Abstract Motivation Alternative splicing (AS) is a key regulatory mechanism that confers genetic diversity and phenotypic plasticity of human. The exons and their flanking regions include comprehensive junction-incorporating sequence features like splicing factor binding sites, and protein domains. These elements involve in exon usage, and finally contribute to isoform-specific biological functions. Splicing-associated sequence features are involved in the multilayered regulation encompassing DNA and proteins. However, most analysis applications have investigated limited sequence features, like protein domains. It is insufficient to explain the comprehensive cause and effect of exon-specific biological processes. With the advent of RNA-seq technology, global AS event analysis has deduced more precise results. As accumulating analysis results, it could be a challenge to identify multi-omics sequence features for AS events. Therefore, application to investigate multi-omics sequence features is useful to scan critical evidence. ASpedia-R is an R package to interrogate junction-incorporating sequence features for human genes. Our database collected the heterogeneous profile encompassed from DNA to protein. Additionally, knowledge-based splicing genes were collected using text-mining to test the association with specific pathway terms. Our package retrieves AS events for high-throughput data analysis results via AS event ID converter. Finally, result profile could be visualized and saved to multiple formats: sequence feature result table, genome track figure, protein-protein interaction network, and gene set enrichment test result table. Our package is a convenient tool to understand global regulation mechanisms by splicing. Availability and implementation The package source code is freely available to non-commercial users at https://github.com/ncc-bioinfo/ASpedia-R. Supplementary data are available at Bioinformatics Advances online.

KNOWLEDGE DISSEMINATION TRAJECTORY OF BIM IN CONSTRUCTION ENGINEERING APPLICATIONS

In recent years, the construction industry worldwide has shown significant interest in Building Information Modeling (BIM). This study aims to analyze the dissemination of knowledge about BIM in construction engineering applications using Main Path Analysis (MPA). The research sample comprises 3,761 papers related to BIM’s application in the construction industry, sourced from the ISI Web of Science database. Initially, we investigate trends in paper publications, conduct country and journal analyses, and examine author statistics. Subsequently, we calculate traversal counts along the search path links to reveal the development trajectory of BIM. The trajectory of BIM’s evolution in the construction industry can be divided into four stages as identified through the global key-route main path analysis: 1) BIM standardization; 2) Integration of completed building projects using BIM; 3) BIM applications in precast construction projects; and 4) BIM applications in land management. These findings provide a clear understanding of how BIM has been applied and evolved within the construction industry.

Analyses of industrial air pollution and long-term health risk using different dispersion models and WRF physics parameters

AbstractThis study consists of three main sections. The first section delves into a performance analysis centered around modeling PM10, NOx, and CO emissions from a cement factory. It examines the effectiveness of various factors, including meteorological data, physics models, and air quality dispersion models, in producing accurate results for atmospheric simulations. The second section covers the dispersion direction and concentrations obtained by visualizing the dispersion maps. The third section covers an analysis of heavy metals emitted from the facility, taking into account potential risks in the region such as cancer, acute and chronic effects, and long-term respiratory risks. This study made use of meteorological models (WRF, AERMET, and CALMET), air quality dispersion models (AERMOD and CALPUFF), a health risk analysis model (HARP), and various sub-models (MMIF and CALWRF). Satellite meteorological data were obtained from NCEP and ERA, with the majority of meteorological data based on the Global Data Assimilation System (GDAS)/Final Operational Global Analysis (FNL) from Global Tropospheric Analyses and Forecast Grids used for the WRF model. In the daily results, AERMOD showed the highest concentration values, but CALPUFF had greater concentrations throughout the annual period. The winter season had the highest concentrations of pollutants. Although there are differences among the physics models used in this research, the conclusions produced are consistent. Analysis of the data from the HARP model suggested that cancer risk levels exceeded the threshold of one person per million. However, the proportion of exceedance instances is rather small in comparison to the receptor points.

OPERATE–H2: A tool for optimizing underground hydrogen storage

Underground hydrogen storage (UHS) is crucial in facilitating the transition to clean energy by reducing reliance on fossil fuels and ensuring a stable supply of emission-free energy. However, safe and efficient storage conditions require careful planning. While reservoir simulations could determine optimal storage conditions, exploring all possible scenarios can be computationally prohibitive and time-consuming. Reduced-order models (ROMs) can efficiently simulate reservoir performance and analyze multiple strategies with reduced computational cost, providing valuable insights for UHS decision-making. This paper introduces an innovative integrated assessment framework with a user-friendly graphical user interface (GUI), designed to optimize UHS operations. The GUI features several advanced functionalities, including (1) temporal evolution for visualizing and analyzing UHS performance over withdrawal cycles, (2) local sensitivity analysis to determine the impact of individual parameters on UHS sites while keeping other inputs constants, (3) uncertainty quantification to identify the key factors for effective decision-making via tornado plots, (4) global sensitivity analysis to comprehensively quantify the interactions between geologic and operational parameters and their effect on UHS performance, and (5) sites screening feature to identify the most prominent potential sites for UHS installations. The GUI is built based on ROMs and the PyQt5 packages in Python. With user-friendly design and advanced features, Optimization, Evaluation, and Risk-Assessment Techniques for Hydrogen Energy tool (OPERATE–H2) enables users to evaluate different UHS scenarios quickly and effectively, helping to accelerate the transition to a cleaner and more sustainable energy future.

PD-L1 intrinsically promotes the proliferation of breast cancer cells through the SKP2-p27/p21 axis

BackgroundPD-L1 intrinsically promotes tumor progression through multiple mechanisms, which potentially leads to resistance to anti-PD-1/PD-L1 therapies. The intrinsic effect of PD-L1 on breast cancer (BC) cell proliferation has not been fully elucidated.Methodswe used proteomics, gene expression knockdown (KD), quantitative immunofluorescence (qIF), western blots, functional assays including colony-forming assay (CFA) and real-time cell analyzer (RTCA), and in vivo data using immunohistochemistry in breast cancer patients.ResultsPD-L1 promoted BC cell proliferation by accelerating cell cycle entry at the G1-to-S phase transition. Global proteomic analysis of the differentially expressed nuclear proteins indicated the involvement of several proliferation-related molecules, including p21CIP1/WAF1. Western blotting and qIF demonstrated the higher expression of SKP2 and the lower expression of p21CIP1/WAF1 and p27Kip1 in PD-L1 expressing (PD-L1pos) cells as compared to PD-L1 KD (PD-L1KD) cells. Xenograft-derived cells and the TCGA BC dataset confirmed this relationship in vivo. Functionally, CFA and RTCA demonstrated the central role of SKP2 in promoting PD-L1-mediated proliferation. Finally, immunohistochemistry in 74 breast cancer patients confirmed PD-L1 and SKP-p21/p27 axis relationship, as it showed a highly statistically significant correlation between SKP2 and PD-L1 expression (p < 0.001), and both correlated significantly with the proliferation marker Ki-67 (p < 0.001). On the other hand, there was a statistically significant inverse relationship between PD-L1 and p21CIP1/WAF1 expression (p = 0.005). Importantly, double negativity for p21CIP1/WAF1 and p27Kip1 correlated significantly with PD-L1 (p < 0.001), SKP2 (p = 0.002), and Ki-67 (p = 0.002).Conclusionswe have demonstrated the role of the SKP2-p27/p21 axis in intrinsic PD-L1-enhanced cell cycle progression. Inhibitors of SKP2 expression can alleviate resistance to ICPIs.

Global Proteomic Analysis Reveals Alterations in Differentially Expressed Proteins between Cardiopathic Lamin A/C Mutations.

Lamin A/C (LMNA) is an important component of nuclear lamina. Mutations cause arrhythmia, heart failure, and sudden cardiac death. While LMNA-associated cardiomyopathy typically has an aggressive course that responds poorly to conventional heart failure therapies, there is variability in severity and age of penetrance between and even within specific mutations, which is poorly understood at the cellular level. Further, this heterogeneity has not previously been captured to mimic the heterozygous state, nor have the hundreds of clinical LMNA mutations been represented. Herein, we have overexpressed cardiopathic LMNA variants in HEK cells and utilized state-of-the-art quantitative proteomics to compare the global proteomic profiles of (1) aggregating Q353 K alone, (2) Q353 K coexpressed with WT, (3) aggregating N195 K coexpressed with WT, and (4) nonaggregating E317 K coexpressed with WT to help capture some of the heterogeneity between mutations. We analyzed each data set to obtain the differentially expressed proteins (DEPs) and applied gene ontology (GO) and KEGG pathway analyses. We found a range of 162 to 324 DEPs from over 6000 total protein IDs with differences in GO terms, KEGG pathways, and DEPs important in cardiac function, further highlighting the complexity of cardiac laminopathies. Pathways disrupted by LMNA mutations were validated with redox, autophagy, and apoptosis functional assays in both HEK 293 cells and in induced pluripotent stem cell derived cardiomyocytes (iPSC-CMs) for LMNA N195 K. These proteomic profiles expand our repertoire for mutation-specific downstream cellular effects that may become useful as druggable targets for personalized medicine approach for cardiac laminopathies.

Spatial distribution of Japanese encephalitis cases and correlated geo-environmental factors in southern and northern counties of China

Japanese encephalitis (JE) is a mosquito-borne disease with a spatial distribution that is linked to geo-environmental factors. The spatial distribution of JE cases and correlated geo-environmental factors were investigated in two critical counties in southern and northern China. Based on maps, enhanced thematic mapper (ETM) remote sensing datasets from Landsat and spatial datasets of JE cases, spatial distribution and spatial cluster analyses of JE cases at the village scale were performed by using the standard deviational ellipse and Ripleys K-function. Global and regional spatial cluster analyses of JE cases were also performed by using Moran's index. Regression analysis was used to analyze the relationships between geo-environmental characteristics and the risk of JE cases. At the study sites, the JE cases were not spatially clustered at the village or district (global) level, whereas there was a spatial cluster at the district (local) level. Diversity-related features for JE patients at the district and village levels were detected at two sites. In the southern counties, the distance of a village from a road was related to the village-level JE risk (OR: 0.530, 95 CI: 0.297–0.947, P = 0.032), and the number of township-level JE cases was linked to the distance of the district center from the road (R =-0.467, P = 0.025) and road length (R = 0.516, P = 0.012) in the administrative area. In northern China, the modified normalized difference water index (MNDWI) in the 5 km buffer around the village was related to village-level JE risk (OR: 0.702, 95% CI: 0.524–0.940, P = 0.018), and the number of township-level JE cases was related to the MNDWI in the administrative region (R =-0.522, P = 0.038). This study elucidates the spatial distribution patterns of JE cases and risk, as well as correlated geo-environmental features, at various spatial scales. This study will significantly assist the JE control efforts of the local Centers for Disease Control and Prevention (CDC), which is the base-level CDC, particularly concerning the allocation of medicine and medical staff, the development of immunological plans, and the allocation of pesticides and other control measures for the mosquito vectors of JE.

Global marine phytoplankton dynamics analysis with machine learning and reanalyzed remote sensing.

Phytoplankton are the world's largest oxygen producers found in oceans, seas and large water bodies, which play crucial roles in the marine food chain. Unbalanced biogeochemical features like salinity, pH, minerals, etc., can retard their growth. With advancements in better hardware, the usage of Artificial Intelligence techniques is rapidly increasing for creating an intelligent decision-making system. Therefore, we attempt to overcome this gap by using supervised regressions on reanalysis data targeting global phytoplankton levels in global waters. The presented experiment proposes the applications of different supervised machine learning regression techniques such as random forest, extra trees, bagging and histogram-based gradient boosting regressor on reanalysis data obtained from the Copernicus Global Ocean Biogeochemistry Hindcast dataset. Results obtained from the experiment have predicted the phytoplankton levels with a coefficient of determination score (R2) of up to 0.96. After further validation with larger datasets, the model can be deployed in a production environment in an attempt to complement in-situ measurement efforts.

Global stability analysis and modelling onchocerciasis transmission dynamics with control measures

ABSTRACT Background: Onchocerciasis infection is one of the neglected tropical diseases targeted for eradication by 2030. The disease is usually transmitted to humans through the bites of black flies. These black flies mostly breed near well-oxygenated fast-running water bodies. The disease is common in mostly remote agricultural villages near rivers and streams. Objective: In this study, a deterministic model describing the infection dynamics of human onchocerciasis disease with control measures is presented. Methods: We derived the model’s reproductive number and used a stability theorem of a Metzler matrix to show that disease-free equilibrium is both locally and globally asymptotically stable whenever the reproductive number is less than one. Parameter contribution was conducted using sensitivity analysis. The model endemic equation is shown to be a cubic polynomial in the presence of infected immigrants and a quadratic form in their absence. Results: When the inflow of infected immigrants is null, the model endemic equation may admit a unique equilibrium if the reproductive number is greater than one, or admits multiple endemic equilibria if the reproductive number is less than unity. We carried out a sensitivity analysis to identify the significant parameters that contribute to onchocerciasis spread. Conclusion: Onchocerciasis disease can be eradicated if the importation of infected immigrants is properly monitored. The integration of the One Health concept in the public health system is key in tackling the emergence and spread of diseases.

Role of Allee and Fear for Controlling Chaos in a Predator–Prey System with Circulation of Disease in Predator

This paper explores a predator–prey system featuring fear and disease within the predator population,utilizing the Rosenzweig–MacArthur model with Holling type-II functional response. The primary focus lies in investigating the impact of a fear factor, wherein the prey’s growth rate is hindered due to predator-induced fear. Additionally, the model accounts for the spread of disease among predators,leading to a division between susceptible and infected predator subpopulations. The inclusion of an Allee effect in the susceptible predator further enriches the model. The study involves a thorough examination, encompassing local and global stability analysis as well as Hopf bifurcation analysis around the interior equilibrium point. Numerical simulations underscore a noteworthy observation: an escalation in interaction force propels the system into chaotic dynamics,marked by stable focus, limit cycles and period-doubling phenomena. A noteworthy finding pertains to the influence of the Allee parameter ([Formula: see text]) on chaotic dynamics. As the Allee parameter values increase, the system tends to stable focus through a sequence of chaotic states, period-doubling and limit cycles. Subsequently, the paper introduces the role of another pivotal parameter, the fear factor, into the chaotic dynamics. Intriguingly, chaos transforms into stable focus through diverse nonlinear phenomena, including period-doubling and limit cycles. This nuanced exploration of parameters sheds light on the intricate dynamics governing the predator–prey system, offering a comprehensive understanding of the interplay between fear, disease and ecological factors. So our observation throughout this paper that how chaos behaves here after one by one injection of our new features: fear factor and Allee parameter?

Efficient global sensitivity analysis method for dynamic models in high dimensions

AbstractDynamic models generating time‐dependent model predictions are typically associated with high‐dimensional input spaces and high‐dimensional output spaces, in particular if time is discretized. It is computationally prohibitive to apply traditional global sensitivity analysis (SA) separately on each time output, as is common in the literature on multivariate SA. As an alternative, we propose a novel method for efficient global SA of dynamic models with high‐dimensional inputs by combining a new polynomial chaos expansion (PCE)‐driven partial least squares (PLS) algorithm with the analysis of variance. PLS is used to simultaneously reduce the dimensionality of the input and output variables spaces, by identifying the input and output latent variables that account for most of their joint variability. PCE is incorporated into the PLS algorithm to capture the non‐linear behavior of the physical system. We derive the sensitivity indices associated with each output latent variable, based on which we propose generalized sensitivity indices that synthesize the influence of each input on the variance of entire output time series. All sensitivities can be computed analytically by post‐processing the coefficients of the PLS‐PCE representation. Hence, the computational cost of global SA for dynamic models essentially reduces to the cost for estimating these coefficients. We numerically compare the proposed method with existing methods by several dynamic models with high‐dimensional inputs. The results show that the PLS‐PCE method can obtain accurate sensitivity indices at low computational cost, even for models with strong interaction among the inputs.

Does energy transition reduce carbon inequality? A global analysis.

Energy transition from fossil fuels to renewables is instrumental in mitigating climate change. Low-income countries have a higher share of renewable energy in their total energy consumption than rich countries (WDI, 2023). Thus, it is imperative to examine the role of energy transition in affecting relative CO2 emissions between rich and poor sections of the societies across income groups of the countries. In this context, our study contributes by constructing the carbon inequality models with renewable and non-renewable energy consumption as prime explanatory variables separately for 114 countries over a data period 1990-2019. The models are estimated individually for high-middle-low-income countries by controlling for foreign direct investment (FDI), economic growth, and innovations. Starting with preliminary econometric operations, we employ the dynamic simulated panel autoregressive distributed lag approach and Driscoll-Kraay standard error regression for empirical investigation. We find that energy transition reduces carbon inequality globally. Innovation has a negative impact, economic growth has a positive impact on carbon inequality, and FDI has an asymmetric impact based on the income level of the countries. The crucial global policy implications are discussed.

Reverse engineering morphogenesis through Bayesian optimization of physics-based models

Morphogenetic programs coordinate cell signaling and mechanical interactions to shape organs. In systems and synthetic biology, a key challenge is determining optimal cellular interactions for predicting organ shape, size, and function. Physics-based models defining the subcellular force distribution facilitate this, but it is challenging to calibrate parameters in these models from data. To solve this inverse problem, we created a Bayesian optimization framework to determine the optimal cellular force distribution such that the predicted organ shapes match the experimentally observed organ shapes. This integrative framework employs Gaussian Process Regression, a non-parametric kernel-based probabilistic machine learning modeling paradigm, to learn the mapping functions relating to the morphogenetic programs that maintain the final organ shape. We calibrated and tested the method on Drosophila wing imaginal discs to study mechanisms that regulate epithelial processes ranging from development to cancer. The parameter estimation framework successfully infers the underlying changes in core parameters needed to match simulation data with imaging data of wing discs perturbed with collagenase. The computational pipeline identifies distinct parameter sets mimicking wild-type shapes. It enables a global sensitivity analysis to support the regulation of actomyosin contractility and basal ECM stiffness to generate and maintain the curved shape of the wing imaginal disc. The optimization framework, combined with experimental imaging, identified that Piezo, a mechanosensitive ion channel, impacts fold formation by regulating the apical-basal balance of actomyosin contractility and elasticity of ECM. This workflow is extensible toward reverse-engineering morphogenesis across organ systems and for real-time control of complex multicellular systems.

Dolimandro’s ‘Turkish Knife’: Geopolitics and Narrative Style in Mary Wroth’s Urania

Abstract This essay examines Lady Mary Wroth’s engagement with early seventeenth-century Anglo-Ottoman relations in The Countess of Montgomery’s Urania. The Urania follows a group of rulers who create an international Christian coalition in what was historically part of the Ottoman Empire. Besides Bernadette Andrea, no other critic has seriously considered the Urania’s allusions to Ottoman peoples, places, and objects. I propose that Wroth’s prose, filled with interpretive cruxes resulting from syntactical and pronominal ambiguities, resonates with England’s ambiguously identificatory relationship with the Ottoman Empire to create a geopolitically inflected narrative style. Following critical work on early modern race as a measure of ‘proximity’, I identify English desires both to identify with and differentiate themselves from the Ottoman Empire within literary and diplomatic discourses. I demonstrate how members of the Sidney-Herbert circle negotiated overlaps in English and Ottoman political, legal, religious, and racial identity markers. I then argue that these overlaps synergize with Wroth’s use of what I term syntactical and pronominal ‘severalty’ of character voices, particularly those of her narrator and characters identified with Ottoman regions. I conclude by demonstrating how Wroth experiments with her narrative style to imagine a Christian empire that surpasses Ottoman authority. In identifying a dialectic between contemporary Anglo-Ottoman relations and Wroth’s narrative style, this essay expands critical approaches to the politics of the Urania beyond more global narratological analysis.

Sensitivity analysis for knowledge discovery in scramjet intake design optimization using deep-learning flowfield prediction

Scramjet engines are a promising propulsion technology for future space transportation systems, and its design exploration represents a significant task toward development of high-performance scramjets. While sensitivity analysis is expected to facilitate acquisition of physical insights for effective scramjet design, the substantial computational cost incurred due to numerical simulations required for statistical analysis poses a challenge to the application, limiting the capability of this analysis. The present study has conducted sensitivity analysis using deep-learning-based flowfield prediction that can significantly reduce the computational cost for numerical simulations providing flowfield data. Global sensitivity analyses have been employed to investigate influential design variables on the flowfield and performance. These have allowed for identifying the design variables that dominantly influence or determine the performance parameters. Local sensitivity analyses have been performed to elucidate the design rationales and the characteristic flow structures for high-performance intake designs. The sensitivity analysis methods in conjunction with flowfield prediction have enabled generation of rich insights that would otherwise be difficult to acquire without this approach, demonstrating the capabilities of the proposed methodology.

Implementation of mechanistic modeling and global sensitivity analysis (GSA) for design, optimization, and scale-up of a roller compaction process

The pharmaceutical industry has been shifting towards the application of mechanistic modeling to improve process robustness, enable scale-up, and reduce time to market. Modeling approaches have been well-developed for processes such as roller compaction, a continuous dry granulation process. Several mechanistic models/approaches have been documented with limited application to high drug-loaded formulations. In this study, the Johanson model was employed to optimize roller compaction processing and guide its scale-up for a high drug loaded formulation. The model was calibrated using a pilot-scale Minipactor and was validated for a commercial-scale Macropactor. Global sensitivity analysis (GSA) was implemented to determine the impact of process parameter variations (roll force, gap, speed) on a quality attribute [solid fraction (SF)]. The throughput method, which estimates SF values of ribbons using granule production rate, was also studied. The model predicted SF values for all 14 Macropactor batches within ± 0.04 SF. The throughput method estimated SF with ± 0.06 SF for 7 out of 11 batches. GSA confirmed that roll force had the largest impact on SF. This case study represents a process modeling approach to build quality into the products/processes and expands the use of mechanistic modeling during drug product development.

Climate Change and Mental Health (A Global Analysis)

Climate change and its corresponding consequences have been recognized as one of the dominating issues of global concern. Many governments of various nations are currently making efforts to mitigate the undesirable consequences of climate change. The essay is focused on carrying out an analysis of the effect of climate change on mental health in the United Kingdom. The paper presented a methodological framework for the diverse and multiple channels by which climate change impacts on a person’s mental health. In connection with this, this methodological analysis led to the conceptualization of the variables that link or mediate the effect of climate change on human mental health and corresponding well-being. There was an exploration of evidence-based analysis of climate change modifies the variables that impact mental health; this will take care of the expression of the diverse pathways of effect. The essay recognizes that despite the mental health dangers and risks associated with climate change, adopting a strategic climate action can also be a window of opportunity to harness its impact and engage in proactive and preventable measures.

The False Hope of Stewardship in the Context of Controlling Shareholders: Making Sense Out of the Global Transplant of a Legal Misfit

Abstract In 2010, the United Kingdom issued the world’s first stewardship code. Since then, stewardship codes have been issued in many of the world’s leading economies and now exist in twenty jurisdictions on six continents, with more jurisdictions considering adopting them. In the United Kingdom, stewardship codes were promised to transform rationally passive institutional investors into actively engaged shareholders to prevent another global financial crisis (GFC). More recently, the new 2020 U.K. Stewardship Code (U.K. Code) has been promoted as a mechanism to save the planet by incentivizing institutional investors to pressure listed companies to focus on environmental, social, and corporate governance (ESG). There is a vigorous debate and developed literature on whether the U.K. Code will achieve these goals. However, what has been lost in this debate is that outside of the United Kingdom/United States it may not matter nearly as much if stewardship succeeds in changing the behavior of institutional investors. This is because, with the notable exception of the United Kingdom/United States, institutional investors are collectively minority shareholders in most listed companies in almost every jurisdiction in the world. Moreover, in almost every jurisdiction, with the notable exception of the United Kingdom/United States, most listed companies already have a rationally active—non-institutional—controlling shareholder as their “steward.” Why then have jurisdictions around the world adopted U.K.-style stewardship codes which are designed based on the assumption that institutional investors collectively control most listed companies? This Article answers this question by undertaking the first in-depth global comparative analysis of the curious transplant of U.K.-style stewardship codes into jurisdictions dominated by controlling shareholders and examines the role that stewardship plays in these jurisdictions. It does this by drawing on a unique collection of recent in-depth case studies on stewardship in twenty-two jurisdictions by leading corporate law experts, hand-collected data analyzing the content of every stewardship code that has ever been issued, and fresh hand-compiled data on shareholder ownership structures in listed companies around the world. It reveals that stewardship has been coopted by governments and institutional investors to serve their own diverse purposes—a troubling trend which will likely be exacerbated post-Covid-19, when an authentic focus on an inclusive society and the environment will be more critical than ever.

Nitrosative stress under microaerobic conditions triggers inositol metabolism in Pseudomonas extremaustralis.

Bacteria are exposed to reactive oxygen and nitrogen species that provoke oxidative and nitrosative stress which can lead to macromolecule damage. Coping with stress conditions involves the adjustment of cellular responses, which helps to address metabolic challenges. In this study, we performed a global transcriptomic analysis of the response of Pseudomonas extremaustralis to nitrosative stress, induced by S-nitrosoglutathione (GSNO), a nitric oxide donor, under microaerobic conditions. The analysis revealed the upregulation of genes associated with inositol catabolism; a compound widely distributed in nature whose metabolism in bacteria has aroused interest. The RNAseq data also showed heightened expression of genes involved in essential cellular processes like transcription, translation, amino acid transport and biosynthesis, as well as in stress resistance including iron-dependent superoxide dismutase, alkyl hydroperoxide reductase, thioredoxin, and glutathione S-transferase in response to GSNO. Furthermore, GSNO exposure differentially affected the expression of genes encoding nitrosylation target proteins, encompassing metalloproteins and proteins with free cysteine and /or tyrosine residues. Notably, genes associated with iron metabolism, such as pyoverdine synthesis and iron transporter genes, showed activation in the presence of GSNO, likely as response to enhanced protein turnover. Physiological assays demonstrated that P. extremaustralis can utilize inositol proficiently under both aerobic and microaerobic conditions, achieving growth comparable to glucose-supplemented cultures. Moreover, supplementing the culture medium with inositol enhances the stress tolerance of P. extremaustralis against combined oxidative-nitrosative stress. Concordant with the heightened expression of pyoverdine genes under nitrosative stress, elevated pyoverdine production was observed when myo-inositol was added to the culture medium. These findings highlight the influence of nitrosative stress on proteins susceptible to nitrosylation and iron metabolism. Furthermore, the activation of myo-inositol catabolism emerges as a protective mechanism against nitrosative stress, shedding light on this pathway in bacterial systems, and holding significance in the adaptation to unfavorable conditions.

Abstract PO2-16-04: Proteogenomic characterization of non-luminal A versus luminal A breast cancer tumor cells enriched by laser microdissection

Abstract Introduction: Breast cancer (BC) is classified into four widely-accepted intrinsic subtypes based on PAM (Prediction Analysis of Microarray) 50 gene expression profiles: Luminal A (LumA), Luminal B (LumB), Her2-enriched (Her2) and basal-like (Basal). Recent multi-omic studies of human BC have identified many potential therapeutic biomarkers for these subtypes and have also revealed the extensive molecular heterogeneity of the disease. Patients with LumA tumors generally have better outcomes because these tumors are typically slower-growing and responsive to hormone therapy. However, there is still considerable variation in the clinical outcomes of patients with non-LumA tumors. Here, we strive to understand the proteogenomic characteristics of intrinsically-defined non-LumA tumors in reference to LumA tumors. Methods: A total of 117 retrospectively-collected, untreated primary breast tumor specimens, with a focus on non-LumA subtypes, were selected from the Clinical Breast Care Project and consented using a HIPAA-compliant, IRB-approved protocol. The study cohort had a median patient follow-up of 9.3 years, enabling clinical outcome-related analyses. Immunohistochemistry (IHC) subtyping was used to enrich the cohort with non-LumA subtypes. Breast tumors were embedded in OCT (Optimal Cutting Temperature) compound and processed by laser microdissection (LMD) to enrich for tumor cells. DNA, RNA, and protein were simultaneously extracted from each tumor using the illustra triplePrep kit. Paired-end RNA sequencing and whole genome sequencing (WGS) were performed for 117 and 99 tumors, respectively, using the Illumina HiSeq platform. Quantitative global proteomics and phosphoproteomic analyses were performed on 112 and 50 tumors, respectively, using isobaric TMT 6-plex labeling with the “universal reference” strategy. Results: We observed significantly lower stromal, immune and microenvironment scores in non-basal-like tumors, especially the LumA subtype from LMD-processed samples, compared to that of the same tumors but bulk-processed in TCGA-breast cancer study. There was also significantly lower stromal gene expression in LMD LumA compared to TCGA LumA. Unlike a recent report on proteomics clustering of bulk-processed tumors, we did not observe a stromal-enriched cluster, probably because the use of LMD minimized stromal components. Many common patterns of somatic mutations were observed among non-LumA tumors, such as dominant TP53 mutations and 5q deletion, suggesting a potentially common cell-of-origin for non-LumA tumors, such as an ER negative cancer stem cell or progenitor cell for these tumor subtypes. Cell proliferation-associated genes were amplified and proliferation pathways strongly enriched in non-LumA tumors. In addition to this, we identified two distinct phosphoproteomic profiles for relapsed and relapse-free basal-like BC. Moreover, we also identified 17 differentially expressed phosphopeptides that could identify cases of relapsed and relapse-free basal-like BC with a significant difference in progression-free interval of surviving cases. Conclusion: This study provides an integrated proteogenomic characterization of non-LumA vs LumA tumor specimens enriched for cancer cells. We identified many common features of non-LumA tumors and also identified phosphopeptides that could serve as potential biomarkers for less aggressive basal-like BC and possibly guide treatment selections. Disclaimer: The contents of this publication are the sole responsibility of the author(s) and do not necessarily reflect the views, opinions or policies of USUHS, HJF, the DoD or the Departments of the Army, Navy or Air Force or the DOE or PNNL. Mention of trade names, commercial products, or organizations does not imply endorsement by the U.S. Government. Citation Format: Praveen Kumar Raj Kumar, Xiaoying Lin, Tao Liu, Lori Sturtz, Marina Gritsenko, Vladislav Petyuk, Tyler Sagendorf, Brenda Deyarmin, Jianfang Liu, Anupama Praveen-Kumar, Guisong Wang, Jason McDermott, Anil Shukla, Ronald Moore, Matthew Monroe, Bobbie-Jo Webb-Robertson, Jeffrey Hooke, Leigh Fantacone-Campbell, Brad Mostoller, Leonid Kvecher, Jennifer Kane, Jennifer Melley, Stella Somiari, Patrick Soon-Shiong, Richard Smith, Richard Mural, Karin Rodland, Craig Shriver, Albert Kovatich, Hai Hu. Proteogenomic characterization of non-luminal A versus luminal A breast cancer tumor cells enriched by laser microdissection [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-16-04.