Multi-scale models can facilitate whole plant simulations by linking gene networks, protein synthesis, metabolic pathways, physiology, and growth. Whole plant models can be further integrated with ecosystem, weather, and climate models to predict how various interactions respond to environmental perturbations. These models have the potential to fill in missing mechanistic details and generate new hypotheses to prioritize directed engineering efforts. Outcomes will potentially accelerate improvement of crop yield, sustainability, and increase future food security. It is time for a paradigm shift in plant modeling, from largely isolated efforts to a connected community that takes advantage of advances in high performance computing and mechanistic understanding of plant processes. Tools for guiding future crop breeding and engineering, understanding the implications of discoveries at the molecular level for whole plant behavior, and improved prediction of plant and ecosystem responses to the environment are urgently needed. The purpose of this perspective is to introduce Crops in silico (cropsinsilico.org), an integrative and multi-scale modeling platform, as one solution that combines isolated modeling efforts toward the generation of virtual crops, which is open and accessible to the entire plant biology community. The major challenges involved both in the development and deployment of a shared, multi-scale modeling platform, which are summarized in this prospectus, were recently identified during the first Crops in silico Symposium and Workshop.
Global population increase coupled with rising urbanization underlies the predicted need for 60% more food by 2050, but produced on the same amount of land as today. Improving photosynthetic efficiency is a largely untapped approach to addressing this problem. Here, we scale modelling processes from gene expression through photosynthetic metabolism to predict leaf physiology in evaluating acclimation of photosynthesis to rising atmospheric concentrations of CO2 ([CO2]). Model integration with the yggdrasil interface enabled asynchronous message passing between models. The multiscale model of soybean (Glycine max) photosynthesis calibrated to physiological measures at ambient [CO2] successfully predicted the acclimatory changes in the photosynthetic apparatus that were observed at 550 ppm [CO2] in the field. We hypothesized that genetic alteration is necessary to achieve optimal photosynthetic efficiency under global change. Flux control analysis in the metabolic system under elevated [CO2] identified enzymes requiring the greatest change to adapt optimally to the new conditions. This predicted that Rubisco was less limiting under elevated [CO2] and should be down-regulated allowing re-allocation of resource to enzymes controlling the rate of regeneration of ribulose-1,5-bisphosphate (RuBP). By linking the Gene Regulatory Network through protein concentration to the metabolic model, it was possible to identify transcription factors (TFs) that matched the up- and down-regulation of genes needed to improve photosynthesis. Most striking was TF Gm-GATA2, which down-regulated genes for Rubisco synthesis while up-regulating key genes controlling RuBP regeneration and starch synthesis. The changes predicted for this TF most closely matched the physiological ideotype that the modelling predicted as optimal for the future elevated [CO2] world.
Purpose: Objective documentation of airflow obstruction is often lacking in hospitalized patients treated for acute exacerbation of chronic obstructive pulmonary disease (AECOPD). The utility of spirometry performed in hospitalized patients to identify airflow obstruction, and thus a diagnosis of COPD, is unclear. Our aim was to compare inpatient spirometry, performed during an AECOPD, with outpatient spirometry. Methods: A retrospective analysis of data from patients enrolled in an AECOPD care plan was performed. As part of the plan, patients underwent inpatient spirometry to establish a COPD diagnosis and outpatient clinic spirometry within 4 weeks of hospital discharge to confirm it. Data analyzed included forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), slow vital capacity (SVC) and FEV1/ vital capacity (VC). Obstruction was defined by FEV1/VC<0.70. Results: A total of 159 patients (mean age 63.2 +/-10.5 years) had corresponding in-and outpatient spirometry. The median days between inpatient and outpatient spirometry was 12 (interquartile range [IQR] 9-16). Inpatient spirometry had a sensitivity of 94%, specificity of 24%, positive predictive value of 83% and negative predictive value of 53% for predicting outpatient obstruction. The area under curve for using inpatient spirometry was 0.82. The mean difference between inpatient and outpatient FEV1 was 0.44 +/-0.03 liters or 17.3 +/-1.13 % predicted (p<0.0001) for FEV1. Conclusions: Inpatient spirometry accurately predicts outpatient airflow obstruction, thus providing an opportunity to identify patients admitted with suspected AECOPD who have no prior spirometric documentation. AbstractAbbreviations: chronic obstructive pulmonary disease, COPD; acute exacerbation of COPD, AECOPD; forced expiratory volume in 1 second, FEV1; forced vital capacity, FVC; slow vital capacity, SVC; vital capacity, VC; interquartile range, IQR; International Classification of Diseases, ICD; Global initiative for chronic Obstructive Lung Disease, GOLD; body mass index, BMI Funding Support: None Date of Acceptance: December 15, 2017 Citation: Loh CH, Genese FA, Kannan KK, Lovings TM, Peters SP, Ohar JA. Spirometry in hospitalized patients with acute exacerbation of COPD accurately predicts post discharge airflow obstruction.
INTRODUCTION Diffuse large B-cell lymphoma (DLBCL) represents several distinct clinical pathologic entities recently identified by molecular profiling. Treatment with anti-CD19 chimeric antigen receptor (CAR) T-cell therapies is now standard for many patients with relapsed/refractory (R/R) disease. Although antigen loss of CD19 represents a known cause of late relapses, the majority of CAR-T cell treatment failure occurs very soon after treatment at which time the impact of molecular subtype and other somatic mutations of DLBCL is undefined. We sought to determine impact of molecular features of DLBCL tumors on clinical outcomes in a cohort of patients with R/R disease who were treated with axicabtagene ciloleucel (axi-cel) or tisagenlecleucel (tisa-cel) in order to provide insight into the mechanism of response or resistance to CAR-T cell therapy. METHODS We collected clinical data and formalin-fixed, paraffin embedded (FFPE) biopsy specimens from 121 DLBCL patients at the time of R/R disease after prior treatment with standard chemoimmunotherapy across 12 US academic medical centers who subsequently received commercial CAR-T cell treatment. Whole exome and transcriptome sequencing was performed on all cases to measure gene expression and gene copy number alterations. Genetic analysis was done on 96 patients with pre-treatment biopsies that passed sequencing quality filters, and expression analysis on 93 patients. Progression-free and overall survival (PFS, OS) measured from the day of CAR-T cell infusion were estimated using the Kaplan-Meier method and compared with the log-rank test. RESULTS Baseline demographics and treatment details of the patient population are shown in the Table (Panel A). Best overall response was CR in 43% of patients and PR in 10% patients. PFS and OS were significantly different based on best response to treatment (P<0,001 Figure, Panel B). At the time of R/R disease, the most commonly mutated genes were TP53 (25%), KMT2D (23%), CREBBP (23%), BCL2 (20%), BTG2 (12%), ARID1A (11%), CARD11 (11%), MYD88 (11%) and PIM1 (11%), (Panel C). Molecular subtyping based on the method of Wright, et al. revealed cases to be BN2 (N=16), A53 (N=13), EZB (N=14), MCD (N=13), N1 (N=4), ST2 (N=8) and unclassifiable (UC) in 28 cases. Cluster analysis as described by Chapuy et al. assigned cases to be C0 (N=6), C1 (N=18), C2 (N=14), C3 (N=27), C4 (N=17) and C5 (N=14). The impact of subgroups on of PFS are shown in Panels D and E. While not statistically significant different across all groups, there was a trend towards improved outcomes in C5/MCD as well as the C2/A53 subtypes and a trend towards inferior PFS in the C3/EZB subtypes. Inferior PFS was observed in patients with mutations in BCL2 (P=0.009) and MYC (P<0.001), but not BTG2 (P=0.095), MYD88 (P=0.106), or CD79B (P=0.086). An unbiased model comprising mutations in MYC, BCL2, CDKN2A, and KLHL6 was strongly associated with a lack of response to CAR-T therapy and a poor prognosis (HR=3.55, P<0.001, Panel F). Gene Set Enrichment Analysis (GSEA) identified a gene signature reflecting T-cell activation in the pre-treatment tumor biopsy as being associated with a higher likelihood of response to treatment (Panel G). CONCLUSIONS DLBCL patients whose tumors have molecular features that are predictive of inferior response to standard frontline treatment including the high-risk subgroups (C2/A53) and (C5/MCD) have favorable treatment outcomes with CAR-T cell therapy. In contrast, individual driver mutations including MYC and BCL2, CDKN2A, and KLHL6 are associated with inferior PFS with CAR-T cell therapy, while mutations in BTG2, MYD88, and CD79B are associated with a favorable PFS. In addition, gene expression analysis implicates a potential role for the microenvironment in modulating responses to CAR-T therapy. These findings suggest that predictive biomarkers for response to traditional chemoimmunotherapy and cellular immunotherapy are distinct. Our results provide insight into potentially targetable pathways for the development of rational treatment strategies that may augment response CAR-T cell therapy. Figure 1 Figure 1. Disclosures Hill: Celgene (BMS): Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria; Gentenech: Consultancy, Honoraria, Research Funding; Kite, a Gilead Company: Consultancy, Honoraria, Other: Travel Support, Research Funding; Beigene: Consultancy, Honoraria, Research Funding; AstraZenica: Consultancy, Honoraria; Epizyme: Consultancy, Honoraria; Incyte/Morphysis: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding; Karyopharm: Consultancy, Honoraria, Research Funding; AbbVie: Consultancy, Honoraria, Research Funding. McKinney: Novartis: Research Funding; Nordic Nanovector: Research Funding; Molecular Templates: Consultancy, Research Funding; Kite/Gilead: Honoraria, Speakers Bureau; Incyte: Research Funding; Genetech: Consultancy, Honoraria, Research Funding; Epizyme: Consultancy; Celgene: Consultancy, Research Funding; BTG: Consultancy; Beigene: Research Funding; ADC Therapeutics: Consultancy, Speakers Bureau; Pharmacyclics: Consultancy; Verastem: Consultancy. Neff: Spring Discovery: Consultancy, Ended employment in the past 24 months; EUSA Pharma: Speakers Bureau; Enzyvant: Consultancy. Reshef: BMS, Regeneron, TScan, Synthekine, Atara, Jasper, Bayer: Consultancy; ilead, BMS, Precision, Immatics, Atara, Takeda, Shire, Pharmacyclics, Incyte: Research Funding; Bayer: Consultancy; Gilead and Novartis: Honoraria. Oluwole: Janssen: Consultancy; Pfizer: Consultancy; Curio Science: Consultancy; Kite, a Gilead Company: Consultancy, Research Funding. Ghosh: Incyte: Consultancy, Honoraria; Pharmacyclics LLC, an AbbVie Company: Consultancy, Honoraria, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Seattle Genetics: Consultancy, Honoraria, Speakers Bureau; TG Therapeutics: Consultancy, Honoraria, Research Funding; Gilead: Consultancy, Honoraria, Research Funding, Speakers Bureau; Genmab: Consultancy, Honoraria; Epizyme: Honoraria, Speakers Bureau; Bristol Myers Squibb: Consultancy, Honoraria, Research Funding, Speakers Bureau; AstraZeneca: Consultancy, Honoraria, Speakers Bureau; ADC Therapeutics: Consultancy, Honoraria; Adaptive Biotech: Consultancy, Honoraria; AbbVie: Honoraria, Speakers Bureau; Karyopharma: Consultancy, Honoraria; Genentech: Research Funding. Chen: Actinium Pharmaceuticals: Other: Principal Investigator, SIERRA Trial, Actinium. Hernandez-Ilizaliturri: AbbVie: Other: Advisory Boards; Incyte: Other: Advisory Boards; Celgene: Other: Advisory Boards; BMS: Other: Advisory Boards; Pharmacyclics: Other: Advisory Boards; Amgen: Other: Advisory Boards; Kite: Other: Advisory Boards; Gilead: Other: Advisory Boards; Epyzime: Other: Advisory Boards. Shah: Lily: Consultancy, Honoraria, Research Funding; Miltenyi Biotec: Consultancy, Honoraria, Research Funding; Kite: Consultancy; Epizyme: Consultancy; Legend: Consultancy; Incyte: Consultancy; Umoja: Consultancy. Stephens: Epizyme: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees, Research Funding; Innate Pharma: Membership on an entity's Board of Directors or advisory committees; Beigene: Membership on an entity's Board of Directors or advisory committees; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; Adaptive: Membership on an entity's Board of Directors or advisory committees; JUNO: Research Funding; Novartis: Research Funding; Abbvie: Consultancy; AstraZeneca: Consultancy; CSL Behring: Consultancy; Celgene: Consultancy; Mingsight: Research Funding; Arqule: Research Funding. Patel: Janssen: Consultancy; Kite Pharma: Consultancy, Speakers Bureau; TG Therapeutics: Consultancy, Speakers Bureau; MEI Pharma: Consultancy; Abbvie: Consultancy; Genentech: Consultancy; Bristol Myers Squibb: Consultancy, Speakers Bureau; BeiGene: Consultancy; Morphosys: Consultancy; Pharmacyclics: Consultancy; AstraZeneca: Consultancy, Research Funding, Speakers Bureau; ADC Therapeutics: Consultancy; Lilly: Consultancy. Pagel: Gilead: Consultancy; Actinium Pharmaceuticals: Consultancy; Kite, a Gilead Company: Consultancy; Incyte/MorphoSys: Consultancy; AstraZeneca: Consultancy; Pharmacyclics/AbbVie: Consultancy; Epizyme: Consultancy; BeiGene: Consultancy; MEI Pharma: Consultancy. Hsi: AbbVie Inc, Eli Lilly: Research Funding. Goy: Genentech/Hoffman la Roche: Research Funding; AbbVie/Pharmacyclics: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Acerta: Consultancy, Research Funding; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Kite, a Gilead Company: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees; Medscape: Consultancy; AstraZeneca: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Physicians' Education Resource: Consultancy, Other: Meeting/travel support; Xcenda: Consultancy, Honoraria; Genomic Testing Cooperative: Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees, Other: Leadership role; LLC(Targeted Oncology): Consultancy; Hoffman la Roche: Consultancy; Vincerx: Honoraria, Membership on an entity's Board of Directors or advisory committees; Rosewell Park: Consultancy; Infinity/Verastem: Research Funding; MorphoSys: Honoraria, Other; Elsevier PracticeUpdate: Oncology: Consultancy, Honoraria; Xcenda: Consultancy; Bristol Meyers Squibb: Membership on an entity's Board of Directors or advisory committees; Vincerx pharma: Membership on an entity's Board of Directors or advisory committees; OncLive Peer Exchange: Honoraria; COTA (Cancer Outcome Tracking Analysis): Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees, Other: Leadership role; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Elsevier's Practice Update Oncology, Intellisphere, LLC(Targeted Oncology): Consultancy; Incyte: Honoraria; AbbVie/Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Michael J Hennessey Associates INC: Consultancy; Novartis: Consultancy, Honoraria; Janssen: Membership on an entity's Board of Directors or advisory committees; Janssen: Research Funding; Karyopharm: Research Funding; Bristol Meyers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Phamacyclics: Research Funding; Constellation: Research Funding; Hackensack Meridian Health, Regional Cancer Care Associates/OMI: Current Employment. Ohgami: Stemline Therapeutics: Research Funding. Andreadis: CRISPR Therapeutics: Research Funding; GenMAB: Research Funding; Novartis: Research Funding; Roche: Current equity holder in publicly-traded company, Ended employment in the past 24 months; Epizyme: Honoraria; Incyte: Honoraria; TG Therapeutics: Honoraria; Kite: Honoraria; Karyopharm: Honoraria; Atara: Consultancy, Honoraria; BMS: Research Funding; Merck: Research Funding. Thacker: Data Driven Bioscience: Current Employment. Rozzi: Data Driven Bioscience: Current Employment. Parker: Data Driven Bioscience: Current Employment. Happ: Data Driven Bioscience: Current Employment. Dave: Data Driven Bioscience: Current equity holder in publicly-traded company.
RNA-seq has proven to be a powerful tool to unravel various aspects of the transcriptome, especially the quantification of alternative splicing (AS) that leads to isoform diversity. The honey bee ( Apis mellifera ) is an important model organism for studying the molecular underpinnings of behavioral plasticity and social behavior, and recent RNA-seq studies of honey bees have revealed AS patterns and their regulation by DNA methylation. However, tissue-specific AS patterns have not been fully explored. In this paper, we characterized AS patterns in two different honey bee tissue types, and also explored their conservation and regulation. We used the RNA-seq data from brain and fat body to improve the existing models of honey bee genes and identified tissue-specific AS patterns. We found that AS genes show high conservation between honey bee and Drosophila melanogaster . We also confirmed and extended previous findings of a correlation between gene body DNA methylation and AS patterns, providing further support for the role of DNA methylation in regulating AS. In addition, our analysis suggests distinct functional roles for tissue-specific alternatively spliced genes. Taken together, our work provides new insights into the conservation and dynamics of AS patterns across different tissue types.
Background Metastatic epithelioid sarcoma (EPS) remains a largely unmet clinical need in children, adolescents and young adults despite the advent of EZH2 inhibitor tazemetostat. Methods In order to realise consistently effective drug therapies, a functional genomics approach was used to identify key signalling pathway vulnerabilities in a spectrum of EPS patient samples. EPS biopsies/surgical resections and cell lines were studied by next‐generation DNA exome and RNA deep sequencing, then EPS cell cultures were tested against a panel of chemical probes to discover signalling pathway targets with the most significant contributions to EPS tumour cell maintenance. Results Other biologically inspired functional interrogations of EPS cultures using gene knockdown or chemical probes demonstrated only limited to modest efficacy in vitro. However, our molecular studies uncovered distinguishing features (including retained dysfunctional SMARCB1 expression and elevated GLI3, FYN and CXCL12 expression) of distal, paediatric/young adult‐associated EPS versus proximal, adult‐associated EPS. Conclusions Overall results highlight the complexity of the disease and a limited chemical space for therapeutic advancement. However, subtle differences between the two EPS subtypes highlight the biological disparities between younger and older EPS patients and emphasise the need to approach the two subtypes as molecularly and clinically distinct diseases.
Glioblastoma (GBM) is a heterogeneous tumor made up of cell states that evolve over time. Here, we modeled tumor evolutionary trajectories during standard-of-care treatment using multi-omic single-cell analysis of a primary tumor sample, corresponding mouse xenografts subjected to standard of care therapy, and recurrent tumor at autopsy. We mined the multi-omic data with single-cell SYstems Genetics Network AnaLysis (scSYGNAL) to identify a network of 52 regulators that mediate treatment-induced shifts in xenograft tumor-cell states that were also reflected in recurrence. By integrating scSYGNAL-derived regulatory network information with transcription factor accessibility deviations derived from single-cell ATAC-seq data, we developed consensus networks that modulate cell state transitions across subpopulations of primary and recurrent tumor cells. Finally, by matching targeted therapies to active regulatory networks underlying tumor evolutionary trajectories, we provide a framework for applying single-cell-based precision medicine approaches to an individual patient in a concurrent, adjuvant, or recurrent setting.
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