Systems Biology Studies Research Articles

Development of vegetative oil sorghum: From lab‐to‐field

SummaryBiomass crops engineered to accumulate energy‐dense triacylglycerols (TAG or ‘vegetable oils’) in their vegetative tissues have emerged as potential feedstocks to meet the growing demand for renewable diesel and sustainable aviation fuel (SAF). Unlike oil palm and oilseed crops, the current commercial sources of TAG, vegetative tissues, such as leaves and stems, only transiently accumulate TAG. In this report, we used grain (Texas430 or TX430) and sugar‐accumulating ‘sweet’ (Ramada) genotypes of sorghum, a high‐yielding, environmentally resilient biomass crop, to accumulate TAG in leaves and stems. We initially tested several gene combinations for a ‘push‐pull‐protect’ strategy. The top TAG‐yielding constructs contained five oil transgenes for a sorghum WRINKLED1 transcription factor (‘push’), a Cuphea viscosissima diacylglycerol acyltransferase (DGAT; ‘pull’), a modified sesame oleosin (‘protect’) and two combinations of specialized Cuphea lysophosphatidic acid acyltransferases and medium‐chain acyl‐acyl carrier protein thioesterases. Though intended to generate oils with medium‐chain fatty acids, engineered lines accumulated oleic acid‐rich oil to amounts of up to 2.5% DW in leaves and 2.0% DW in stems in the greenhouse, 36‐fold and 49‐fold increases relative to wild‐type (WT) plants, respectively. Under field conditions, the top‐performing event accumulated TAG to amount to 5.5% DW in leaves and 3.5% DW in stems, 78‐fold and 58‐fold increases, respectively, relative to WT TX430. Transcriptomic and fluxomic analyses revealed potential bottlenecks for increased TAG accumulation. Overall, our studies highlight the utility of a lab‐to‐field pipeline coupled with systems biology studies to deliver high vegetative oil sorghum for SAF and renewable diesel production.

Protein catabolites as blood-based biomarkers of aging physiology: Findings from the Dog Aging Project.

Our understanding of age-related physiology and metabolism has grown through the study of systems biology, including transcriptomics, single-cell analysis, proteomics and metabolomics. Studies in lab organisms in controlled environments, while powerful and complex, fall short of capturing the breadth of genetic and environmental variation in nature. Thus, there is now a major effort in geroscience to identify aging biomarkers and to develop aging interventions that might be applied across the diversity of humans and other free-living species. To meet this challenge, the Dog Aging Project (DAP) is designed to identify cross-sectional and longitudinal patterns of aging in complex systems, and how these are shaped by the diversity of genetic and environmental variation among companion dogs. Here we surveyed the plasma metabolome from the first year of sampling of the Precision Cohort of the DAP. By incorporating extensive metadata and whole genome sequencing information, we were able to overcome the limitations inherent in breed-based estimates of genetic and physiological effects, and to probe the physiological and dietary basis of the age-related metabolome. We identified a significant effect of age on approximately 40% of measured metabolites. Among other insights, we discovered a potentially novel biomarker of age in the post-translationally modified amino acids (ptmAAs). The ptmAAs, which can only be generated by protein hydrolysis, covaried both with age and with other biomarkers of amino acid metabolism, and in a way that was robust to diet. Clinical measures of kidney function mediated about half of the higher ptmAA levels in older dogs. This work identifies ptmAAs as robust indicators of age in dogs, and points to kidney function as a physiological mediator of age-associated variation in the plasma metabolome.

A landmark federal interagency collaboration to promote data science in health care: Million Veteran Program-Computational Health Analytics for Medical Precision to Improve Outcomes Now.

In 2016, the Department of Veterans Affairs (VA) and the Department of Energy (DOE) established an Interagency Agreement (IAA), the Million Veteran Program-Computational Health Analytics for Medical Precision to Improve Outcomes Now (MVP-CHAMPION) research collaboration. Oversight fell under the VA Office of Research Development (VA ORD) and DOE headquarters. An Executive Committee and 2 senior scientific liaisons work with VA and DOE leadership to optimize efforts in the service of shared scientific goals. The program supported centralized data management and genomic analysis including creation of a scalable approach to cataloging phenotypes. Cross-cutting methods including natural language processing, image processing, and reusable code were developed. The 79.6 million dollar collaboration has supported centralized data management and genomic analysis including a scalable approach to cataloging phenotypes and launched over 10 collaborative scientific projects in health conditions highly prevalent in veterans. A ground-breaking analysis on the Summit and Andes supercomputers at the Oak Ridge National Laboratory (ORNL) of the genetic underpinnings of over 2000 health conditions across 44 million genetic variants which resulted in the identification of 38270 independent genetic variants associating with one or more health traits. Of these, over 2000 identified associations were unique to non-European ancestry. Cross-cutting methods have advanced state-of-the-art artificial intelligence (AI) including large language natural language processing and a system biology study focused on opioid addiction awarded the 2018 Gordon Bell Prize for outstanding achievement in high-performance computing. The collaboration has completed work in prostate cancer, suicide prevention, and cardiovascular disease, and cross-cutting data science. Predictive models developed in these projects are being tested for application in clinical management. Eight new projects were launched in 2023, taking advantage of the momentum generated by the previous collaboration. A major challenge has been limitations in the scope of appropriated funds at DOE which cannot currently be used for health research. Extensive multidisciplinary interactions take time to establish and are essential to continued progress. New funding models for maintaining high-performance computing infrastructure at the ORNL and for supporting continued collaboration by joint VA-DOE research teams are needed.

A graph theoretical approach to experimental prioritization in genome-scale investigations

The goal of systems biology is to gain a network level understanding of how gene interactions influence biological states, and ultimately inform upon human disease. Given the scale and scope of systems biology studies, resource constraints often limit researchers when validating genome-wide phenomena and potentially lead to an incomplete understanding of the underlying mechanisms. Further, prioritization strategies are often biased towards known entities (e.g. previously studied genes/proteins with commercially available reagents), and other technical issues that limit experimental breadth. Here, heterogeneous biological information is modeled as an association graph to which a high-performance minimum dominating set solver is applied to maximize coverage across the graph, and thus increase the breadth of experimentation. First, we tested our model on retrieval of existing gene functional annotations and demonstrated that minimum dominating set returns more diverse terms when compared to other computational methods. Next, we utilized our heterogenous network and minimum dominating set solver to assist in the process of identifying understudied genes to be interrogated by the International Mouse Phenotyping Consortium. Using an unbiased algorithmic strategy, poorly studied genes are prioritized from the remaining thousands of genes yet to be characterized. This method is tunable and extensible with the potential to incorporate additional user-defined prioritizing information. The minimum dominating set approach can be applied to any biological network in order to identify a tractable subset of features to test experimentally or to assist in prioritizing candidate genes associated with human disease.

Yeast9: a consensus genome-scale metabolic model for S. cerevisiae curated by the community

Genome-scale metabolic models (GEMs) can facilitate metabolism-focused multi-omics integrative analysis. Since Yeast8, the yeast-GEM of Saccharomyces cerevisiae, published in 2019, has been continuously updated by the community. This has increased the quality and scope of the model, culminating now in Yeast9. To evaluate its predictive performance, we generated 163 condition-specific GEMs constrained by single-cell transcriptomics from osmotic pressure or reference conditions. Comparative flux analysis showed that yeast adapting to high osmotic pressure benefits from upregulating fluxes through central carbon metabolism. Furthermore, combining Yeast9 with proteomics revealed metabolic rewiring underlying its preference for nitrogen sources. Lastly, we created strain-specific GEMs (ssGEMs) constrained by transcriptomics for 1229 mutant strains. Well able to predict the strains’ growth rates, fluxomics from those large-scale ssGEMs outperformed transcriptomics in predicting functional categories for all studied genes in machine learning models. Based on those findings we anticipate that Yeast9 will continue to empower systems biology studies of yeast metabolism.

MiRNA signature related to atherosclerotic lesion induced shear stress modifications in familial hypercholesterolemia patients with subclinical atherosclerosis: a bioinformatics systems biology study

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Institute of Health Carlos III (ISCIII) Background Patients with familial hypercholesterolemia (FH), even when appropriately treated, are at risk of premature clinical atherosclerotic cardiovascular disease (ASCVD) presentation. Although atherosclerosis (AT) is directly related to hemodynamic disturbances affecting the vascular geometry, there is scarce information on whether there is miRNA-mediated/flow-mediated modulation of atherosclerosis in FH. Purpose To identify a differential plasma miRNA signature related to fluid shear stress (FSS) in well controlled FH-patients with asymptomatic atherosclerosis. Methods FH-patients (N=49) with subclinical atherosclerosis, identified by computed tomographic angiography, and a reference cohort of non-FH subjects (N=32), both from a national FH-Registry, were included in the study. Coronary plaque volume and phenotype was analyzed by means of the QAngio CT imaging system. miRNAs associated to FSS were defined in silico by bioinformatics (KEGG, Cytoscape, GTEX portal, miRWalk, miRBase tools) and validated in plasma samples by RT-qPCR using TaqMan technology. Results By in silico analysis, 7 miRNAs targeting genes involved in features of functions (ROS production, oxidative stress, leukocyte transendothelial/migration-inflammation and vasodilation) of shear stress-induced cellular dysfunction and atherosclerosis (KEGG, hsa05418) were identified by miRWalk and miRBase databases. Of the 7 miRNAs, a 6-miRNA signature showed significantly lower plasma levels in FH-patients than in the non-FH group (P<0.01 for: miR-30e-5p, miR-378a-3p, let-7d-3p, and miR-550-5p; P=0.03 for miR-324-3p). In the FH-group, miR-324-3p, miR-30e-5p, miR-378a-3p and let-7d-3p significantly discriminated patients stratified by the plaque volume of non-calcified lesions (fibro-lipidic phenotype), using the median plaque volume as cut-off value. And even further, a high percentage (65-70%) of FH-subjects with low levels of the 4 miRNA had plaque volumes above the median. Interestingly, this miRNA-signature did not differentiate FH-patients stratified by the volume of calcified plaques. Conclusion FH-patients show a significantly lower content of an atherosclerosis/shear stress signature of 4 miRNAs targeting genes involved in subclinical atherosclerosis progression in FH.

Climate Crisis and Agricultural Response: Climate Resilient Crops for Sustainability in Food Production Systems

Climate change encompasses altered levels of temperature and humidity, variability in the rainfall pattern, fluctuations in weather parameters, rise in ambient CO2 levels, emission of greenhouse gases, global warming, etc. that results in extreme events and disasters as cyclones, floods, droughts, salinity, nutrient and heavy metal stress, change in arthropod diversity and emergence of new invasive pests. This results in un-usual effects in agro-ecosystems leading to changes in cropping patterns, crop diversity, and their interaction with biotic and abiotic stress factors, threatening livelihood, food, and nutritional security. Population displacement, declining food productivity, and vulnerable agro-ecosystems are the major consequences of altered meteorological events that occur due to climate change. Therefore, substitution of traditional crops with crops that exhibit resilience to climate crisis is the need of the hour. Smart breeding approaches and precision farming technologies as remote sensing and spectral analysis, artificial intelligence, machine learning, speed breeding, genome analysis, genetic manipulation, gene drive systems, system biology study, omics approaches, etc can make agricultural production climate resilient and sustainable. Response of biofortified crops under changing climate must also be assessed to improve the crop productivity and output.

Abstract 6220: TRIP13 augments pancreatic cancer progression- An integrated systems biology study

Abstract Background- Pancreatic cancer (PanCa) is one of the most aggressive forms of cancer and its incidence rate is continuously increasing every year. It is expected that by 2030, PanCa will become the 2nd leading cause of cancer related deaths in the United States due to the lack of understanding of the complexity of disease, early diagnostic methods, and extremely poor survival. Despite great advancements in biomedical research, there are very limited modalities available for the early detection of PanCa and its treatment. Thus, understanding of disease biology and identification of newer diagnostic and therapeutic modalities are high priority research in the field. Our group has identified Thyroid Receptor Interacting Protein -13 (TRIP13) as an oncogenic protein which contributes to early events of PanCa. Methodology- High dimensional omics and structural elucidation of TRIP13 was conducted using publicly available databases like Consurf, Phosphosite, GTEx and GEPIA2. Molecular biology techniques were applied to cross validate all our bioinformatics data. qPCR, immunoblotting and IHC analysis was performed in progressive PDAC cell lines and human tissues to decipher the expression level of TRIP13 in various pathological staging including functional enrichment analysis using Linkdomics. Results- The structural elucidation analysis revealed that most of the functionally exposed residues, phosphorylation and ubiquitylation sites situated at AAA domain of TRIP13. As compared to normal, TRIP13 is significantly over expressed in pancreatic tumor samples. Pathological staging 2 and 1 (early stages) showed relatively higher expression of TRIP13 as compared advanced stage. Of the total 7 isoforms of TRIP13, only one isoform (TRIP13-001) has shown the over expression in pancreatic tumor samples and shown association with poor survival. In correlation studies, TRIP13 has shown positive association with other pancreatic cancer biomarkers (CEACAM5, S100A4, MUC1, MSLN, CA125). The functional enrichment analyses suggest that TRIP13 is involved in important patho-physiological pathways like DNA repair, viral carcinogenesis, cellular senescence, and cell cycle. Our wet lab experiments also support our computational biology observation. Conclusion- This integrated computational biology study suggests a potential role of TRIP13 in pancreatic cancer progression via modulation of important oncological pathways. Wet lab experimentations also validated the involvement of TRIP13 in pancreatic cancer progression. TRIP13 expression may be associated with patient prognosis. Thus, its inhibitory small molecules can be useful for boosting pancreatic cancer therapies in clinical settings. Key words- TRIP13, Pancreatic cancer, Early events of cancer, Integrative Biology, Transcriptomics, Computational biology Citation Format: Swati Dhasmana, Anupam Dhasmana, Stella Rios, Iris A. Perez, Sheema Khan, Farrukh Afaq, Upender Manne, Murali M. Yallapu, Subhash C. Chauhan. TRIP13 augments pancreatic cancer progression- An integrated systems biology study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6220.

The Dual Role of Sulforaphane-Induced Cellular Stress-A Systems Biological Study.

The endoplasmic reticulum (ER) plays a crucial role in cellular homeostasis. When ER stress is generated, an autophagic self-digestive process is activated to promote cell survival; however, cell death is induced in the case of excessive levels of ER stress. The aim of the present study was to investigate the effect of a natural compound called sulforaphane (SFN) upon ER stress. Our goal was to investigate how SFN-dependent autophagy activation affects different stages of ER stress induction. We approached our scientific analysis from a systems biological perspective using both theoretical and molecular biological techniques. We found that SFN induced the various cell-death mechanisms in a concentration- and time-dependent manner. The short SFN treatment at low concentrations promoted autophagy, whereas the longer treatment at higher concentrations activated cell death. We proved that SFN activated autophagy in a mTORC1-dependent manner and that the presence of ULK1 was required for its function. A low concentration of SFN pre- or co-treatment combined with short and long ER stress was able to promote cell survival via autophagy induction in each treatment, suggesting the potential medical importance of SFN in ER stress-related diseases.

CloudProteoAnalyzer: scalable processing of big data from proteomics using cloud computing.

Shotgun proteomics is widely used in many system biology studies to determine the global protein expression profiles of tissues, cultures, and microbiomes. Many non-distributed computer algorithms have been developed for users to process proteomics data on their local computers. However, the amount of data acquired in a typical proteomics study has grown rapidly in recent years, owing to the increasing throughput of mass spectrometry and the expanding scale of study designs. This presents a big data challenge for researchers to process proteomics data in a timely manner. To overcome this challenge, we developed a cloud-based parallel computing application to offer end-to-end proteomics data analysis software as a service (SaaS). A web interface was provided to users to upload mass spectrometry-based proteomics data, configure parameters, submit jobs, and monitor job status. The data processing was distributed across multiple nodes in a supercomputer to achieve scalability for large datasets. Our study demonstrated SaaS for proteomics as a viable solution for the community to scale up the data processing using cloud computing. This application is available online at https://sipros.oscer.ou.edu/ or https://sipros.unt.edu for free use. The source code is available at https://github.com/Biocomputing-Research-Group/CloudProteoAnalyzer under the GPL version 3.0 license.

TMELand: An end-to-end Pipeline for Quantification and Visualization of Waddington's Epigenetic Landscape based on Gene Regulatory Network.

Waddington's epigenetic landscape is a framework depicting the processes of cell differentiation and reprogramming under the control of a gene regulatory network (GRN). Traditional model-driven methods for landscape quantification focus on the Boolean network or differential equation-based models of GRN, which need sophisticated prior knowledge and hence hamper their practical applications. To resolve this problem, we combine data-driven methods for inferring GRNs from gene expression data with model-driven approach to the landscape mapping. Specifically, we build an end-to-end pipeline to link data-driven and model-driven methods and develop a software tool named TMELand for GRN inference, visualizing Waddington's epigenetic landscape, and calculating state transition paths between attractors to uncover the intrinsic mechanism of cellular transition dynamics. By integrating GRN inference from real transcriptomic data with landscape modeling, TMELand can facilitate studies of computational systems biology, such as predicting cellular states and visualizing the dynamical trends of cell fate determination and transition dynamics from single-cell transcriptomic data. The source code of TMELand, a user manual, and model files of case studies can be downloaded freely from https://github.com/JieZheng-ShanghaiTech/TMELand.

Metabolomics: Challenges and Opportunities in Systems Biology Studies.

Metabolomics can provide diagnostic, prognostic, and therapeutic biomarker profiles of individual patients because a large number of metabolites can be simultaneously measured in biological samples in an unbiased manner. Minor stimuli can result in substantial alterations, making it a valuable target for analysis. Due to the complexity and sensitivity of the metabolome, studies must be devised to maintain consistency, minimize subject-to-subject variation, and maximize information recovery. This effort has been aided by technological advances in experimental design, rodent models, and instrumentation. Proton Nuclear Magnetic Resonance (1H-NMR) spectroscopy of biofluids, such as plasma, urine, and faeces provide the opportunity to identify biomarker change patterns that reflect the physiological or pathological status of an individual patient. Metabolomics has the ultimate potential to be useful in a clinical context, where it could be used to predict treatment response and survival and for early disease diagnosis. During drug treatment, an individual's metabolic status could be monitored and used to predict deleterious effects. Therefore, metabolomics has the potential to improve disease diagnosis, treatment, and follow-up care. In this chapter, we demonstrate how a metabolomics study can be used to diagnose a disease by classifying patients as either healthy or pathological, while accounting for individual variation.

Identification Biomarkers and Molecular Mechanisms Involved in Lung Transplant Rejection, and Drug Repurposing: A Systems Biology Study

Identification Biomarkers and Molecular Mechanisms Involved in Lung Transplant Rejection, and Drug Repurposing: A Systems Biology Study

Multilayer network analysis of Alzheimer disease links multiscale paths to the phenotype

AbstractBackgroundAlzheimer Disease (AD) cover a wide range of biological scales, from genes and proteins to cells and tissues, up to behavior. However, how these scales, the endotype, interact with one another to define the clinical phenotype remains poorly understood.MethodsHere we conducted a systems biology study of AD, based on multilayer network analysis and deep phenotyping that integrates genomics, CSF biomarkers, tau and amyloid‐beta PET, brain MRI, risk factors and clinical data, obtained from the ADNI collaboration. Multilayer networks were constructed using mutual information, a nonlinear measure of correlation, at each layer and between layers. Boolean simulations identified paths within and among all layers through which dynamic information is transmitted. Multiple linear regression was then used to predict the phenotype (clinical or imaging variables) from paths within the genetic, molecular and imaging layers.Resultsthe most prominent paths that significantly predicted the phenotype included hippocampus atrophy and tau levels from CSF and PET. Our results thus show that integrating the flow of information across biological scales reveals relevant paths for AD.Conclusionpaths identified by multilayer networks can be pursued as biomarkers or new therapeutic targets.

Key genes and immune infiltration in chronic spontaneous urticaria: a study of bioinformatics and systems biology.

Chronic spontaneous urticaria (CSU) is defined by the spontaneous occurrence of wheals and/or angioedema for >6 weeks. The pathogenesis involves skin mast cells, but the complex causes of their activation remain to be characterized in detail. To explore disease-driving genes and biological pathways in CSU. Two microarray data sets, e.g., GSE57178 and GSE72540, with mRNA information of skin from CSU patients, were downloaded from the Gene Expression Omnibus (GEO) database. An integrated bioinformatics pipeline including identification of differentially expressed genes (DEGs), functional enrichment analysis, protein-protein interaction (PPI) network analysis, co-expression and drug prediction analysis, and immune and stromal cells deconvolution analyses were applied to identify hub genes and key drivers of CSU pathogenesis. In total, we identified 92 up-regulated and 7 down-regulated genes in CSU lesions. These were significantly enriched in CSU-related pathways such as TNF, NF-κB, and JAK-STAT signaling. Based on PPI network modeling, four genes, i.e., IL-6, TLR-4, ICAM-1, and PTGS-2, were computationally identified as key pathogenic players in CSU. Immune infiltration analyses indicated that dendritic cells, Th2 cells, mast cells, megakaryocyte-erythroid progenitor, preadipocytes, and M1 macrophages were increased in lesional CSU skin. Our results offer new insights on the pathogenesis of CSU and suggest that TNF, NF-κB, JAK-STAT, IL-6, TLR-4, ICAM-1, and PTGS-2 may be candidate targets for novel CSU treatments.

Effects of Factors Influencing Scar Formation on the Scar Microbiome in Patients with Burns

Skin microbiome dysbiosis has deleterious effects, and the factors influencing burn scar formation, which affects the scar microbiome composition, are unknown. Therefore, we investigated the effects of various factors influencing scar formation on the scar microbiome composition in patients with burns. We collected samples from the burn scar center and margin of 40 patients with burns, subgrouped by factors influencing scar formation. Scar microbiome composition-influencing factors were analyzed using univariate and multivariate analyses. Skin graft, hospitalization period, intensive care unit (ICU) admission, burn degree, sex, age, total body surface area burned (TBSA), time post-injury, transepidermal water loss, the erythrocyte sedimentation rate, and C-reactive protein levels were identified as factors influencing burn scar microbiome composition. Only TBSA and ICU admission were associated with significant differences in alpha diversity. Alpha diversity significantly decreased with an increase in TBSA and was significantly lower in patients admitted to the ICU than in those not admitted to the ICU. Furthermore, we identified microorganisms associated with various explanatory variables. Our cross-sectional systems biology study confirmed that various variables influence the scar microbiome composition in patients with burns, each of which is associated with various microorganisms. Therefore, these factors should be considered during the application of skin microbiota for burn scar management.

Multiomics Analysis Provides Novel Pathways Related to Progression of Heart Failure

BackgroundDespite major advances in pharmacological treatment for patients with heart failure, residual mortality remains high. This suggests that important pathways are not yet targeted by current heart failure therapies. ObjectivesWe sought integration of genetic, transcriptomic, and proteomic data in a large cohort of patients with heart failure to detect major pathways related to progression of heart failure leading to death. MethodsWe used machine learning methodology based on stacked generalization framework and gradient boosting algorithms, using 54 clinical phenotypes, 403 circulating plasma proteins, 36,046 transcript expression levels in whole blood, and 6 million genomic markers to model all-cause mortality in 2,516 patients with heart failure from the BIOSTAT-CHF (Systems BIOlogy Study to TAilored Treatment in Chronic Heart Failure) study. Results were validated in an independent cohort of 1,738 patients. ResultsThe mean age of the patients was 70 years (Q1-Q3: 61-78 years), 27% were female, median N-terminal pro–B-type natriuretic peptide was 4,275 ng/L (Q1-Q3: 2,360-8,486 ng/L), and 7% had heart failure with preserved ejection fraction. During a median follow-up of 21 months, 657 (26%) of patients died. The 4 major pathways with a significant association to all-cause mortality were: 1) the PI3K/Akt pathway; 2) the MAPK pathway; 3) the Ras signaling pathway; and 4) epidermal growth factor receptor tyrosine kinase inhibitor resistance. Results were validated in an independent cohort of 1,738 patients. ConclusionsA systems biology approach integrating genomic, transcriptomic, and proteomic data identified 4 major pathways related to mortality. These pathways are related to decreased activation of the cardioprotective ERBB2 receptor, which can be modified by neuregulin.

The Time-Resolved Salt Stress Response of Dunaliella tertiolecta-A Comprehensive System Biology Perspective.

Algae-driven processes, such as direct CO2 fixation into glycerol, provide new routes for sustainable chemical production in synergy with greenhouse gas mitigation. The marine microalgae Dunaliella tertiolecta is reported to accumulate high amounts of intracellular glycerol upon exposure to high salt concentrations. We have conducted a comprehensive, time-resolved systems biology study to decipher the metabolic response of D. tertiolecta up to 24 h under continuous light conditions. Initially, due to a lack of reference sequences required for MS/MS-based protein identification, a high-quality draft genome of D. tertiolecta was generated. Subsequently, a database was designed by combining the genome with transcriptome data obtained before and after salt stress. This database allowed for detection of differentially expressed proteins and identification of phosphorylated proteins, which are involved in the short- and long-term adaptation to salt stress, respectively. Specifically, in the rapid salt adaptation response, proteins linked to the Ca2+ signaling pathway and ion channel proteins were significantly increased. While phosphorylation is key in maintaining ion homeostasis during the rapid adaptation to salt stress, phosphofructokinase is required for long-term adaption. Lacking β-carotene, synthesis under salt stress conditions might be substituted by the redox-sensitive protein CP12. Furthermore, salt stress induces upregulation of Calvin-Benson cycle-related proteins.

Association between PROMIS10, SF-36 and NeuroQoL in persons with multiple sclerosis

BackgroundPatient reported outcome measures (PROs) are considered promising tools for use in clinical settings to measure the impact of disease on physical, mental and social well-being from the patient's perspective. The Patient Reported Outcome Measurement Information System Scale v1.1-Global Health (PROMIS-10) is a measure that is well-suited to clinical practice, but the relationships between this measure and longer PRO measures used in multiple sclerosis (MS) research are unknown. MethodsSubjects enrolled in SysteMS: A Systems Biology Study of Clinical, Radiological, and Molecular Markers in Subjects with MS at the Brigham and Women's Hospital were eligible to contribute to the study. 349 subjects completed three PRO measures at study entry: PROMIS-10, Medical Outcomes Study Short-Form 36 (SF-36), and Quality of Life in Neurological Disorders (Neuro-QoL™). All questions and global scores from PROMIS-10 were correlated with all domain and summary component scores for SF-36 and all domain scores for Neuro-QoL using Pearson's correlation coefficient. Further, the global scores from PROMIS-10 were correlated with the expanded disability status scale (EDSS) and compared between disease categories (relapsing vs progressive MS). ResultsStrong correlations were observed between PROMIS-10 questions and SF-36 domains aimed at measuring the same construct. Further, the PROMIS-10 Global Physical Health score was correlated with the Physical Component Score from the SF-36 (r = 0.798), and the PROMIS Global Mental Health score was correlated with the Mental Component Score from the SF-36 (r = 0.726). Strong correlations between PROMIS-10 questions and two Neuro-QoL domains (fatigue and lower extremity function) were observed, but other Neuro-QoL domains were not strongly correlated with PROMIS-10 questions. PROMIS-10 Global Physical Health had stronger relationship to EDSS and disease category compared to the Global Mental Health. ConclusionsPROMIS-10 questions and global scores are highly correlated with the corresponding domains of SF-36 in PwMS. Neuro-QoL provides different information regarding HRQOL since different domains are being measured.

Mechanistic insights into sulfur source-driven physiological responses and metabolic reorganization in the fuel-biodesulfurizing Rhodococcus qingshengii IGTS8.

Comparative proteomics and untargeted metabolomics were combined to study the physiological and metabolic adaptations of Rhodococcus qingshengii IGTS8 under biodesulfurization conditions. After growth in a chemically defined medium with either dibenzothiophene (DBT) or MgSO4 as the sulfur source, many differentially produced proteins and metabolites associated with several metabolic and physiological processes were detected including the metabolism of carbohydrates, amino acids, lipids, nucleotides, vitamins, protein synthesis, transcriptional regulation, cell envelope biogenesis, and cell division. Increased production of the redox cofactor mycofactocin and associated proteins was one of the most striking adaptations under biodesulfurization conditions. While most central metabolic enzymes were less abundant in the presence of DBT, a key enzyme of the glyoxylate shunt, isocitrate lyase, was up to 26-fold more abundant. Several C1 metabolism and oligotrophy-related enzymes were significantly more abundant in the biodesulfurizing culture. R. qingshengii IGTS8 exhibited oligotrophic growth in liquid and solid media under carbon starvation. Moreover, the oligotrophic growth was faster on the solid medium in the presence of DBT compared to MgSO4 cultures. In the DBT culture, the cell envelope and phospholipids were remodeled, with lower levels of phosphatidylethanolamine and unsaturated and short-chain fatty acids being the most prominent changes. Biodesulfurization increased the biosynthesis of osmoprotectants (ectoine and mannosylglycerate) as well as glutamate and induced the stringent response. Our findings reveal highly diverse and overlapping stress responses that could protect the biodesulfurizing culture not only from the associated sulfate limitation but also from chemical, oxidative, and osmotic stress, allowing efficient resource management. IMPORTANCE Despite decades of research, a commercially viable bioprocess for fuel desulfurization has not been developed yet. This is mainly due to lack of knowledge of the physiology and metabolism of fuel-biodesulfurizing bacteria. Being a stressful condition, biodesulfurization could provoke several stress responses that are not understood. This is particularly important because a thorough understanding of the microbial stress response is essential for the development of environmentally friendly and industrially efficient microbial biocatalysts. Our comparative systems biology studies provide a mechanistic understanding of the biology of biodesulfurization, which is crucial for informed developments through the rational design of recombinant biodesulfurizers and optimization of the bioprocess conditions. Our findings enhance the understanding of the physiology, metabolism, and stress response not only in biodesulfurizing bacteria but also in rhodococci, a precious group of biotechnologically important bacteria.