BACKGROUND:Plasma concentration of PAI-1 (plasminogen activator inhibitor-1) correlates with arterial stiffness. Vascular smooth muscle cells (SMCs) express PAI-1, and the intrinsic stiffness of SMCs is a major determinant of total arterial stiffness. We hypothesized that PAI-1 promotes SMC stiffness by regulating the cytoskeleton and that pharmacological inhibition of PAI-1 decreases SMC and aortic stiffness. METHODS:PAI-039, a specific inhibitor of PAI-1, and small interfering RNA were used to inhibit PAI-1 expression in cultured human SMCs. Effects of PAI-1 inhibition on SMC stiffness, F-actin (filamentous actin) content, and cytoskeleton-modulating enzymes were assessed. WT (wild-type) and PAI-1-deficient murine SMCs were used to determine PAI-039 specificity. RNA sequencing was performed to determine the effects of PAI-039 on SMC gene expression. In vivo effects of PAI-039 were assessed by aortic pulse wave velocity. RESULTS:PAI-039 significantly reduced intrinsic stiffness of human SMCs, which was accompanied by a significant decrease in cytoplasmic F-actin content. PAI-1 gene knockdown also decreased cytoplasmic F-actin. PAI-1 inhibition significantly increased the activity of cofilin, an F-actin depolymerase, in WT murine SMCs, but not in PAI-1-deficient SMCs. RNA-sequencing analysis suggested that PAI-039 upregulates AMPK (AMP-activated protein kinase) signaling in SMCs, which was confirmed by Western blotting. Inhibition of AMPK prevented activation of cofilin by PAI-039. In mice, PAI-039 significantly decreased aortic stiffness and tunica media F-actin content without altering the elastin or collagen content. CONCLUSIONS:PAI-039 decreases intrinsic SMC stiffness and cytoplasmic stress fiber content. These effects are mediated by AMPK-dependent activation of cofilin. PAI-039 also decreases aortic stiffness in vivo. These findings suggest that PAI-1 is an important regulator of the SMC cytoskeleton and that pharmacological inhibition of PAI-1 has the potential to prevent and treat cardiovascular diseases involving arterial stiffening.

In recent years, there has been a growing interest in profiling multiomic modalities within individual cells simultaneously. One such example is integrating combined single-cell RNA sequencing (scRNA-seq) data and single-cell transposase-accessible chromatin sequencing (scATAC-seq) data. Integrated analysis of diverse modalities has helped researchers make more accurate predictions and gain a more comprehensive understanding than with single-modality analysis. However, generating such multimodal data is technically challenging and expensive, leading to limited availability of single-cell co-assay data. Here, we propose a model for cross-modal prediction between the transcriptome and chromatin profiles in single cells. Our model is based on a deep neural network architecture that learns the latent representations from the source modality and then predicts the target modality. It demonstrates reliable performance in accurately translating between these modalities across multiple paired human scATAC-seq and scRNA-seq datasets. Additionally, we developed CrossMP, a web-based portal allowing researchers to upload their single-cell modality data through an interactive web interface and predict the other type of modality data, using high-performance computing resources plugged at the backend.

Chronic kidney disease (CKD) is a leading cause of morbidity and mortality globally. Maternal obesity during pregnancy is linked to systemic inflammation and elevated levels of the pro-inflammatory cytokine interleukin-6 (IL-6). In our previous work, we demonstrated that increased maternal IL-6 during gestation impacts intrauterine development in mice. We hypothesized that IL-6-induced inflammation alters gene expression in the developing fetus. To test this, pregnant mice were administered IL-6 or saline during mid-gestation. Newborn mouse kidneys were analyzed using mRNA-seq, miRNA-seq and whole-genome bisulfite-seq (WGBS). A multi-omics approach was employed to quantify mRNA gene expression, miRNA expression and DNA methylation, using advanced bioinformatics and data integration techniques. Our analysis identified 19 key genes present in multiple omics datasets, regulated by epigenetics and miRNAs. We constructed a regulatory network for these genes, revealing disruptions in pathways such as Mannose type O-glycan biosynthesis, the cell cycle, apoptosis and FoxO signaling. Notably, the Atp7b gene was regulated by DNA methylation and miR-223 targeting, whereas the Man2a1 gene was controlled by DNA methylation affecting energy metabolism. These findings suggest that these genes may play a role in fetal programming, potentially leading to CKD later in life due to gestational inflammation.

Pregnant women are often prescribed or abuse opioid drugs. The placenta is likely the key to understanding how opioids cause adverse pregnancy outcomes. Maternal oxycodone (OXY) exposure of pregnant mice leads to disturbances in the layer of invasive parietal trophoblast giant cells (pTGC) that forms the interface between the placenta and uterus. These cells are analogous to extravillous trophoblasts of the human placenta. They are crucial to coordinating the metabolic needs of the conceptus with those of the mother and are primary participants in the placenta-brain axis. Their large nuclear size, however, has precluded both single- cell (sc) and single- nucleus (sn) RNA-seq analyses beyond embryonic day (E) 8.5. Here, we compared the transcriptomes of placentas from pregnant mice exposed to OXYwith unexposed controls at E12.5, with particular emphasis on the pTGC. The nonfluidic Parse snRNA-seq approach permitted characterization of the nuclear transcriptomes of all the major placental cell lineages and their presumed progenitors at E12.5. OXY exposure had a negligible effect on components of the placental labyrinth, including the two syncytial cell layers, but caused transcriptomic changes consistent with metabolic stress throughout the spongiotrophoblast. Most notably, there was loss of the majority of pTGC, whose normal gene expression is consistent with elevated energy demand relating to biosynthesis of multiple secretory products, especially hormones, and endoduplication of DNA. This unusual sensitivity of pTGC presumably puts the pregnancy and future health of the offspring at particular risk to OXY exposure.

The complexity of environmental factors affecting crops in the field is gradually increasing due to climate change-associated weather events, such as droughts or floods combined with heat waves, coupled with the accumulation of different environmental and agricultural pollutants. The impact of multiple stress conditions on plants was recently termed “multifactorial stress combination” (MFSC) and defined as the occurrence of 3 or more stressors that impact plants simultaneously or sequentially. We recently reported that with the increased number and complexity of different MFSC stressors, the growth and survival of Arabidopsis (Arabidopsis thaliana) seedlings declines, even if the level of each individual stress is low enough to have no significant effect on plants. However, whether MFSC would impact commercial crop cultivars is largely unknown. Here, we reveal that a MFSC of 5 different low-level abiotic stresses (salinity, heat, the herbicide paraquat, phosphorus deficiency, and the heavy metal cadmium), applied in an increasing level of complexity, has a significant negative impact on the growth and biomass of a commercial rice (Oryza sativa) cultivar and a maize (Zea mays) hybrid. Proteomics, element content, and mixOmics analyses of MFSC in rice identified proteins that correlate with the impact of MFSC on rice seedlings, and analysis of 42 different rice genotypes subjected to MFSC revealed substantial genetic variability in responses to this unique state of stress combination. Taken together, our findings reveal that the impacts of MFSC on 2 different crop species are severe and that MFSC may substantially affect agricultural productivity.

ABSTRACT Global warming, climate change, and industrial pollution are altering our environment subjecting crops to an increasing number and complexity of abiotic stress conditions, concurrently or sequentially. Recent studies revealed that a combination of 3 or more stresses simultaneously impacting a plant (termed ‘multifactorial stress combination’; MFSC) can cause a drastic decline in plant growth and survival, even if the level of each stress involved in the MFSC has a negligible effect on plants. However, the impacts of MFSC on crops are largely unknown. We subjected soybean plants to a MFSC of up to five different stresses (water deficit, salinity, low phosphate, acidity, and cadmium), in an increasing level of complexity, and conducted integrative transcriptomic-phenotypic analysis of reproductive and vegetative tissues. We reveal that MFSC has a negative cumulative effect on soybean yield, that each set of MFSC condition elicits a unique transcriptomic response (that is different between flowers and leaves), and that selected genes expressed in leaves or flowers are linked to the effects of MFSC on different vegetative, physiological, and/or reproductive parameters. We further reveal that the transcriptomic response of soybean and Arabidopsis to MFSC shares common features associated with reactive oxygen and iron/copper signaling/metabolism. Our study provides unique phenotypic and transcriptomic datasets for dissecting the mechanistic effects of MFSC on the vegetative, physiological, and reproductive processes of a crop plant.

Obstructive sleep apnea (OSA) is a highly prevalent chronic disease affecting nearly a billion people globally and increasing the risk of multi-organ morbidity and overall mortality. However, the mechanisms underlying such adverse outcomes remain incompletely delineated. Extracellular vesicles (exosomes) are secreted by most cells, are involved in both proximal and long-distance intercellular communication, and contribute toward homeostasis under physiological conditions. A multi-omics integrative assessment of plasma-derived exosomes from adult OSA patients prior to and after 1-year adherent CPAP treatment is lacking. We conducted multi-omic integrative assessments of plasma-derived exosomes from adult OSA patients prior to and following 1-year adherent CPAP treatment to identify potential specific disease candidates. Fasting morning plasma exosomes isolated from 12 adult patients with polysomnographically-diagnosed OSA were analyzed before and after 12 months of adherent CPAP therapy (mean ≥ 6 h/night) (OSAT). Exosomes were characterized by flow cytometry, transmission electron microscopy, and nanoparticle tracking analysis. Endothelial cell barrier integrity, wound healing, and tube formation were also performed. Multi-omics analysis for exosome cargos was integrated. Exosomes derived from OSAT improved endothelial permeability and dysfunction as well as significant improvement in tube formation compared with OSA. Multi-omic approaches for OSA circulating exosomes included lipidomic, proteomic, and small RNA (miRNAs) assessments. We found 30 differentially expressed proteins (DEPs), 72 lipids (DELs), and 13 miRNAs (DEMs). We found that the cholesterol metabolism (has04979) pathway is associated with lipid classes in OSA patients. Among the 12 subjects of OSA and OSAT, seven subjects had complete comprehensive exosome cargo information including lipids, proteins, and miRNAs. Multi-omic approaches identify potential signature biomarkers in plasma exosomes that are responsive to adherent OSA treatment. These differentially expressed molecules may also play a mechanistic role in OSA-induced morbidities and their reversibility. Our data suggest that a multi-omic integrative approach might be useful in understanding how exosomes function, their origin, and their potential clinical relevance, all of which merit future exploration in the context of relevant phenotypic variance. Developing an integrated molecular classification should lead to improved diagnostic classification, risk stratification, and patient management of OSA by assigning molecular disease-specific therapies.

Data-intensive application workflows benefit by leveraging cloud services to decrease execution times and increase data sharing. Cloud service providers (CSPs) have distinct capabilities and policies, and performance/cost of the cloud services are amongst the prime factors for CSP selection. However, workflow users who need brokering of cloud resources often lack expert guidance to handle the problem of overwhelming choice in CSP selection, and optimization to compensate for service dynamics. In this paper, we address the optimal resource selection problem using a multi-cloud resource broker viz., OnTimeURB that uses knowledge-engineering of user requirements and service capabilities across multiple CSPs. OnTimeURB is powered by integer linear programming and a Naive Bayes classifier to recommend optimal cloud template solutions by weighting performance, agility, cost, and security (PACS) factors. We evaluate the OnTimeURB recommendations with a catalog of bioinformatics application workflows using four CSP resources featuring more than 300 different instance configurations. Our evaluation results show the efficacy of OnTimeURB in creating consistently cost-effective and agile solutions compared to a state-of-the-art k-nearest neighbors (k-NN) approach. We also show that OnTimeURB has 91% success rate improvement in workflow execution times via cloud template recommendations over approaches that do not use knowledge-engineered multi-CSP resource brokering.

Global warming, climate change, and industrial pollution are altering our environment subjecting plants, microbiomes, and ecosystems to an increasing number and complexity of abiotic stress conditions, concurrently or sequentially. These conditions, termed, "multifactorial stress combination" (MFSC), can cause a significant decline in plant growth and survival. However, the impacts of MFSC on reproductive tissues and yield of major crop plants are largely unknown. We subjected soybean (Glycine max) plants to a MFSC of up to five different stresses (water deficit, salinity, low phosphate, acidity, and cadmium), in an increasing level of complexity, and conducted integrative transcriptomic-phenotypic analysis of their reproductive and vegetative tissues. We reveal that MFSC has a negative cumulative effect on soybean yield, that each set of MFSC condition elicits a unique transcriptomic response (that is different between flowers and leaves), and that selected genes expressed in leaves or flowers of soybean are linked to the effects of MFSC on different vegetative, physiological, and/or reproductive parameters. Our study identified networks and pathways associated with reactive oxygen species, ascorbic acid and aldarate, and iron/copper signaling/metabolism as promising targets for future biotechnological efforts to augment the resilience of reproductive tissues of major crop plants to MFSC. In addition, we provide unique phenotypic and transcriptomic datasets for dissecting the mechanistic effects of MFSC on the vegetative, physiological, and reproductive processes of a crop plant.

Advances in next-generation sequencing and other high-throughput technologies have facilitated multiomics research, such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, and phenomics. The resultant emerging multiomics data have brought new challenges as well as opportunities, as seen in the plant and agriculture science domains. We reviewed several bioinformatic and computational methods, models, and platforms, and we have highlighted some of our in-house developed efforts aimed at multiomics data analysis, integration, and management issues faced by the research community. A case study using multiomics datasets generated from our studies of maize nodal root growth under water deficit stress demonstrates the power of these datasets and some other publicly available tools. This analysis also sheds light on the landscape of such applied bioinformatic tools currently available for plant and crop science studies and introduces emerging trends and how they may affect the future.

This is the first work to show in a rodent model that in utero exposure to an opioid drug can lead to longstanding effects on the gut microbiota when examined at adulthood. Further, such bacterial changes are associated with metabolic host responses. Given the similarities between rodent and human microbiomes, it raises cause for concern that similar effects may become evident in children born to mothers taking oxycodone and other opioid drugs.

The ongoing surge of progressively more infectious SARS-CoV-2 variants seen worldwide continues to have a significant impact on population health.1 The Centers for Disease Controls SARS-CoV-2 genomic surveillance program provides state level estimates of variant proportions, but reporting is delayed nearly one month and does not provide the level of granularity needed for local estimates. Genomics Surveillance Data Portal Variants of concern (VOCs) and variants of interest (VOIs), as defined by CDC, are actively monitored in our pipeline to estimate local prevalence in Missouri. Since the establishment of the Genomic Surveillance Architecture in January 2021, the Alpha variant (B.1.1.7) emerged as a dominant SARS-CoV-2 variant in Missouri through April 2021, followed by the Delta variant dominance (B.1.617.2) until December 2021, with its first detection in Missouri as early as March 2021. Summary The SARS-CoV-2 Genomics Surveillance Architecture and COVID-19 Genomics Surveillance Data Portal is an exemplar of the interdisciplinary partnerships needed to establish a successful genomics surveillance program capable of providing timely identification of virus variants essential to informed decision-making and proper public health response.

Modern soybean [Glycine max (L.) Merr] cultivars have low overall genetic variation due to repeated bottleneck events that arose during domestication and from selection strategies typical of many soybean breeding programs. In both public and private soybean breeding programs, the introgression of wild soybean (Glycine soja Siebold and Zucc.) alleles is a viable option to increase genetic diversity and identify new sources for traits of value. The objectives of our study were to examine the genetic architecture responsible for seed protein and oil using a recombinant inbred line (RIL) population derived from hybridizing a G. max line (‘Osage’) with a G. soja accession (PI 593983). Linkage mapping identified a total of seven significant quantitative trait loci on chromosomes 14 and 20 for seed protein and on chromosome 8 for seed oil with LOD scores ranging from 5.3 to 31.7 for seed protein content and from 9.8 to 25.9 for seed oil content. We analyzed 3,015 single F4:9 soybean plants to develop two residual heterozygotes derived near isogenic lines (RHD-NIL) populations by targeting nine SNP markers from genotype-by-sequencing, which corresponded to two novel quantitative trait loci (QTL) derived from G. soja: one for a novel seed oil QTL on chromosome 8 and another for a novel protein QTL on chromosome 14. Single marker analysis and linkage analysis using 50 RHD-NILs validated the chromosome 14 protein QTL, and whole genome sequencing of RHD-NILs allowed us to reduce the QTL interval from ∼16.5 to ∼4.6 Mbp. We identified two genomic regions based on recombination events which had significant increases of 0.65 and 0.72% in seed protein content without a significant decrease in seed oil content. A new Kompetitive allele-specific polymerase chain reaction (KASP) assay, which will be useful for introgression of this trait into modern elite G. max cultivars, was developed in one region. Within the significantly associated genomic regions, a total of eight genes are considered as candidate genes, based on the presence of gene annotations associated with the protein or amino acid metabolism/movement. Our results provide better insights into utilizing wild soybean as a source of genetic diversity for soybean cultivar improvement utilizing native traits.

Multi-cloud resource brokering is becoming a critical requirement for applications that require high scale, diversity, and resilience. Applications demand timely selection of distributed data storage and computation platforms that span local private cloud resources as well as resources from multiple cloud service providers (CSPs). The distinct capabilities and policies, as well as performance/cost of the cloud services, are amongst the prime factors for CSP selection. However, application owners who need suitable cyber resources in community/public clouds, often have preliminary knowledge and preferences of certain CSPs. They also lack expert guidance to handle the problem of overwhelming resource choice from CSPs, and optimization to compensate for service dynamics. In this paper, we address this challenge of optimal resource selection while also leveraging limited user's expertise and preferences towards CSPs through multi-level fuzzy logic modeling based on convoluted factors of performance, agility, cost, and security. We evaluate the efficiency of our fuzzy-engineered resource brokering in improving allocation of resources as well as user satisfiability by using case studies and independent validations of CSPs evaluation.

Conventional wisdom is that Sprouty2 (SPRY2), a suppressor of Receptor Tyrosine Kinase (RTK) signaling, functions as a tumor suppressor and is downregulated in many solid tumors. We reported, for the first time, that increased expression of SPRY2 augments cancer phenotype and Epithelial-Mesenchymal-Transition (EMT) in colorectal cancer (CRC). In this report, we assessed epigenetic DNA modifications that regulate SPRY2 expression in CRC. A total of 4 loci within SPRY2 were evaluated for 5mC using Combined Bisulfite Restriction Analysis (COBRA). Previously sequenced 5hmC nano-hmC seal data within SPRY2 promoter and gene body were evaluated in CRC. Combined bioinformatics analyses of SPRY2 CRC transcripts by RNA-seq/microarray and 450K methyl-array data archived in The Cancer Genome Atlas (TCGA) and GEO database were performed. SPRY2 protein in CRC tumors and cells was measured by Western blotting. Increased SPRY2 mRNA was observed across several CRC datasets and increased protein expression was observed among CRC patient samples. For the first time, SPRY2 hypomethylation was identified in adenocarcinomas in the promoter and gene body. We also revealed, for the first time, increases of 5hmC deposition in the promoter region of SPRY2 in CRC. SPRY2 promoter hypomethylation and increased 5hmC may play an influential role in upregulating SPRY2 in CRC.

Due to their antimicrobial properties, silver nanoparticles (AgNPs) are used in a wide range of consumer products that includes topical wound dressings, coatings for biomedical devices, and food-packaging to extend the shelf-life. Despite their beneficial antimicrobial effects, developmental exposure to such AgNPs may lead to gut dysbiosis and long-term health consequences in exposed offspring. AgNPs can cross the placenta and blood–brain-barrier to translocate in the brain of offspring. The underlying hypothesis tested in the current study was that developmental exposure of male and female mice to AgNPs disrupts the microbiome–gut–brain axis. To examine for such effects, C57BL6 female mice were exposed orally to AgNPs at a dose of 3 mg/kg BW or vehicle control 2 weeks prior to breeding and throughout gestation. Male and female offspring were tested in various mazes that measure different behavioral domains, and the gut microbial profiles were surveyed from 30 through 120 days of age. Our study results suggest that developmental exposure results in increased likelihood of engaging in repetitive behaviors and reductions in resident microglial cells. Echo-MRI results indicate increased body fat in offspring exposed to AgNPs exhibit. Coprobacillus spp., Mucispirillum spp., and Bifidobacterium spp. were reduced, while Prevotella spp., Bacillus spp., Planococcaceae , Staphylococcus spp., Enterococcus spp., and Ruminococcus spp. were increased in those developmentally exposed to NPs . These bacterial changes were linked to behavioral and metabolic alterations. In conclusion, developmental exposure of AgNPs results in long term gut dysbiosis, body fat increase and neurobehavioral alterations in offspring.

Single Nucleotide Polymorphisms (SNPs) and insertions/deletions (Indels) are the most common biological markers widely spread across all genome chromosomes. Owing to the large amount of SNPs and Indels data that have become available during the last ten years, it is a challenge to intuitively integrate, compare, or visualise them and effectively perform analysis across multiple samples simultaneously. Genome-Wide Association Studies (GWAS) is an approach to find genetic variants associated with a trait, but it lacks an efficient way of investigating genomic variant functions. To tackle these issues, we developed SNPViz v2.0, a web-based tool designed to visualise large-scale haplotype blocks with detailed SNPs and Indels grouped by their chromosomal coordinates, along with their overlapping gene models, phenotype to genotype accuracies, Gene Ontology (GO), protein families (Pfam), and their functional effects. SNPViz v2.0 is available in both SoyKB and KBCommons. For soya bean only, the SNPViz v2.0 is available online at: http://soykb.org/SNPViz2/. For other plants such as Arabidopsis thaliana and Zea mays, SNPViz v2.0 in their respective knowledge bases is available online at: https://kbcommons.org.

Single nucleotide polymorphisms (SNPs) and insertions/deletions (Indels) are widely spread across all chromosomes of the genome and act as biological markers, which aid in identification of genes associated with traits or phenotypes. With the advances in next-generation sequencing (NGS) technology, large amounts of SNPs and Indels data have become available, making it difficult to effectively perform analysis across multiple samples and intuitively integrate, compare and/or visualize them simultaneously. Genome-wide association studies (GWAS) is a widely used method to find genetic variations associated with a trait, but it lacks an efficient way to investigate genomic variant functions. To tackle these issues, we have developed SNPViz v2.0, a web-based tool to visualize large-scale haplotype blocks with detailed SNPs and Indels grouped by their chromosomal coordinates, along with their overlapping gene models, phenotype to genotype accuracies, Gene Ontology (GO) annotations, protein families (Pfam) annotations, genomic variant annotations, and their functional effects. Moreover, SNPViz v2.0 integrates several large scale soybean SNPs and Indels datasets including G. Soja, GWAS, NAM41, USB-15x, USB-40x, MSMC and Zhou302, available from multiple studies. SNPViz v2.0 is deployed for all organisms and available in both SoyKB and KBCommons frameworks. For soybean data only, the SNPViz 2.0 is publicly available at http://soykb.org/SNPViz2/. For other organisms such as Arabidopsis thaliana, Mus musculus and Zea mays, SNPViz 2.0 is publicly available in their respective knowledge bases at https://kbcommons.org.

Enhancer of zeste homolog 2 (EZH2) is a histone methyltransferase that suppresses gene expression. Previously, we developed a conditional null model where EZH2 is knocked out in uterus. Deletion of uterine EZH2 increased proliferation of luminal and glandular epithelial cells. Herein, we used RNA-Seq in wild-type (WT) and EZH2 conditional knockout ( Ezh2cKO) uteri to obtain mechanistic insights into the gene expression changes that underpin the pathogenesis observed in these mice. Ovariectomized adult Ezh2cKO mice were treated with vehicle (V) or 17β-estradiol (E2; 1 ng/g). Uteri were collected at postnatal day (PND) 75 for RNA-Seq or immunostaining for epithelial proliferation. Weighted gene coexpression network analysis was used to link uterine gene expression patterns and epithelial proliferation. In V-treated mice, 88 transcripts were differentially expressed (DEG) in Ezh2cKO mice, and Bmp5, Crabp2, Lgr5, and Sprr2f were upregulated. E2 treatment resulted in 40 DEG with Krt5, Krt15, Olig3, Crabp1, and Serpinb7 upregulated in Ezh2cKO compared with control mice. Transcript analysis relative to proliferation rates revealed two module eigengenes correlated with epithelial proliferation in WT V vs. Ezh2cKO V and WT E2 vs. Ezh2cKO E2 mice, with a positive relationship in the former and inverse in the latter. Notably, the ESR1, Wnt, and Hippo signaling pathways were among those functionally enriched in Ezh2cKO females. Current results reveal unique gene expression patterns in Ezh2cKO uterus and provide insight into how loss of this critical epigenetic regulator assumingly contributes to uterine abnormalities.

Big data computation tools are vital for researchers and educators from various domains such as plant science, animal science, biomedical science and others. With the growing computational complexity of biology big data, advanced analytic systems, known as prescriptive systems, are being built using machine learning models to intelligently predict optimum computation solutions for users for better data analysis. However, lack of user-friendly prescriptive systems poses a critical roadblock to facilitating informed decision-making by users. In this paper, we detail a formative usability study to address the complexities faced by users while using prescriptive systems. Our usability research approach considers bioinformatics workflows and community cloud resources in the KBCommons framework’s science gateway. The results show that recommendations from usability studies performed in iterations during the development of prescriptive systems can improve user experience, user satisfaction and help novice as well as expert users to make decisions in a well-informed manner.