Does collaborative farm-scale modelling address current challenges and future opportunities?

Resources required increasing, resources available decreasingFarm-scale modellers will need to make strategic decisionsSingle-owner modelsMay continue with additional resourcesRisk of ‘succession’ problemCommunity modelling is an alternativeNeed to continue building a community of farm modellersThe results will be published as a peer-reviewed article

Clinical and prognostic implications of phenomapping in patients with heart failure receiving cardiac resynchronization therapy

Despite having an indication for cardiac resynchronization therapy according to current guidelines, patients with heart failure with reduced ejection fraction who receive cardiac resynchronization therapy do not consistently derive benefit from it.; To determine whether unsupervised clustering analysis (phenomapping) can identify distinct phenogroups of patients with differential outcomes among cardiac resynchronization therapy recipients from routine clinical practice.; We used unsupervised hierarchical cluster analysis of phenotypic data after data reduction (55 clinical, biological and echocardiographic variables) to define new phenogroups among 328 patients with heart failure with reduced ejection fraction from routine clinical practice enrolled before cardiac resynchronization therapy. Clinical outcomes and cardiac resynchronization therapy response rate were studied according to phenogroups.; Although all patients met the recommended criteria for cardiac resynchronization therapy implantation, phenomapping analysis classified study participants into four phenogroups that differed distinctively in clinical, biological, electrocardiographic and echocardiographic characteristics and outcomes. Patients from phenogroups 1 and 2 had the most improved outcome in terms of mortality, associated with cardiac resynchronization therapy response rates of 81% and 78%, respectively. In contrast, patients from phenogroups 3 and 4 had cardiac resynchronization therapy response rates of 39% and 59%, respectively, and the worst outcome, with a considerably increased risk of mortality compared with patients from phenogroup 1 (hazard ratio 3.23, 95% confidence interval 1.9-5.5 and hazard ratio 2.49, 95% confidence interval 1.38-4.50, respectively).; Among patients with heart failure with reduced ejection fraction with an indication for cardiac resynchronization therapy from routine clinical practice, phenomapping identifies subgroups of patients with differential clinical, biological and echocardiographic features strongly linked to divergent outcomes and responses to cardiac resynchronization therapy. This approach may help to identify patients who will derive most benefit from cardiac resynchronization therapy in "individualized" clinical practice

Short communication:Identifying key parameters for modelling the impacts of livestock health conditions on greenhouse gas emissions

Improved animal health can reduce greenhouse gas (GHG) emissions intensity in livestock systems while increasing productivity. Integrated modelling of disease impacts on farm-scale emissions is important in identifying effective health strategies to reduce emissions. However, it requires that modellers understand the pathways linking animal health to emissions and how these might be incorporated into models. A key barrier to meeting this need has been the lack of a framework to facilitate effective exchange of knowledge and data between animal health experts and emissions modellers. Here, these two communities engaged in workshops, online exchanges and a survey to i) identify a comprehensive list of disease-related model parameters and ii) test its application to evaluating models. Fifty-six parameters were identified and proved effective in assessing the potential of farm-scale models to characterise livestock disease impacts on GHG emissions. Easy wins for the emissions models surveyed include characterising disease impacts related to feeding

Fusion of EML1 to ABL1 in T-cell acute lymphoblastic leukemia with cryptic t(9;14)(q34;q32)

The BCR-ABL1 fusion kinase is frequently associated with chronic myeloid leukemia and B-cell acute lymphoblastic leukemia but is rare in T-cell acute lymphoblastic leukemia (T-ALL). We recently identified NUP214-ABL1 as a variant ABL1 fusion gene in 6% of T-ALL patients. Here we describe the identification of another ABL1 fusion, EML1-ABL1, in a T-ALL patient with a cryptic t(9;14)(q34;q32) associated with deletion of CDKN2A (p16) and expression of TLX1 (HOX11). Echinoderm microtubule-associated protein-like 1-Abelson 1 (EML1-ABL1) is a constitutively phosphorylated tyrosine kinase that transforms Ba/F3 cells to growth factor-independent growth through activation of survival and proliferation pathways, including extracellular signal-related kinase 1/2 (Erk1/2), signal transducers and activators of transcription 5 (Stat5), and Lyn kinase. Deletion of the coiled-coil domain of EML1 abrogated the transforming properties of the fusion kinase. EML1-ABL1 and breakpoint cluster region (BCR)-ABL1 were equally sensitive to the tyrosine kinase inhibitor imatinib. These data further demonstrate the involvement of ABL1 fusions in the pathogenesis of T-ALL and identify EML1-ABL1 as a novel therapeutic target of imatinib

To what extent is climate change adaptation a novel challenge for agricultural modellers?

Modelling is key to adapting agriculture to climate change (CC), facilitating evaluation of the impacts and efficacy of adaptation measures, and the design of optimal strategies. Although there are many challenges to modelling agricultural CC adaptation, it is unclear whether these are novel or, whether adaptation merely adds new motivations to old challenges. Here, qualitative analysis of modellers’ views revealed three categories of challenge: Content, Use, and Capacity. Triangulation of findings with reviews of agricultural modelling and Climate Change Risk Assessment was then used to highlight challenges specific to modelling adaptation. These were refined through literature review, focussing attention on how the progressive nature of CC affects the role and impact of modelling. Specific challenges identified were: Scope of adaptations modelled, Information on future adaptation, Collaboration to tackle novel challenges, Optimisation under progressive change with thresholds, and Responsibility given the sensitivity of future outcomes to initial choices under progressive change

miR-15a-5p and miR-21-5p contribute to chemoresistance in cytogenetically normal acute myeloid leukaemia by targeting PDCD4, ARL2 and BTG2

Cytarabine and daunorubicin are old drugs commonly used in the treatment of acute myeloid leukaemia (AML). Refractory or relapsed disease because of chemotherapy resistance is a major issue. microRNAs (miRNAs) were incriminated in resistance. This study aimed to identify miRNAs involved in chemoresistance in AML patients and to define their target genes. We focused on cytogenetically normal AML patients with wild-type NPM1 without FLT3-ITD as the treatment of this subset of patients with intermediate-risk cytogenetics is not well established. We analysed baseline AML samples by small RNA sequencing and compared the profile of chemoresistant to chemosensitive AML patients. Among the miRNAs significantly overexpressed in chemoresistant patients, we revealed miR-15a-5p and miR-21-5p as miRNAs with a major role in chemoresistance in AML. We showed that miR-15a-5p and miR-21-5p overexpression decreased apoptosis induced by cytarabine and/or daunorubicin. PDCD4, ARL2 and BTG2 genes were found to be targeted by miR-15a-5p, as well as PDCD4 and BTG2 by miR-21-5p. Inhibition experiments of the three target genes reproduced the functional effect of both miRNAs on chemosensitivity. Our study demonstrates that miR-15a-5p and miR-21-5p are overexpressed in a subgroup of chemoresistant AML patients. Both miRNAs induce chemoresistance by targeting three pro-apoptotic genes PDCD4, ARL2 and BTG2

Transient Responses to NOTCH and TLX1/HOX11 Inhibition in T-Cell Acute Lymphoblastic Leukemia/Lymphoma

To improve the treatment strategies of T-cell acute lymphoblastic leukemia/lymphoma (T-ALL), further efforts are needed to identify therapeutic targets. Dysregulated expression of HOX-type transcription factors occurs in 30–40% of cases of T-ALL. TLX1/HOX11 is the prototypical HOX-type transcription factor. TLX1 may be an attractive therapeutic target because mice that are deficient in TLX1 are healthy. To test this possibility, we developed a conditional doxycycline-regulated mouse model of TLX1-initiated T-ALL. TLX1 induced T-ALL after ∼5–7 months with penetrance of 15–60%. Similar to human TLX1-type T-ALLs, the TLX1-induced tumors were arrested at the cortical stage of T-cell development and acquired activating NOTCH1 mutations. Inhibition of NOTCH signaling abrogated growth of cell lines derived from the TLX1-induced tumors. NOTCH inhibition also transiently delayed leukemia progression in vivo. Suppression of TLX1 expression slowed the growth of TLX1 tumor cell lines. Suppression of TLX1 in vivo also transiently delayed leukemia progression. We have shown that TLX1 functions as a T-cell oncogene that is active during both the induction and the maintenance phases of leukemia. However, the effect of suppressing NOTCH or TLX1 was transient. The tumors eventually “escaped” from inhibition. These data imply that the biological pathways and gene sets impacted by TLX1 and NOTCH have largely lost their importance in the fully established tumor. They have been supplanted by stronger oncogenic pathways. Although TLX1 or NOTCH inhibitors may not be effective as single agents, they may still contribute to combination therapy for TLX1-driven acute leukemia

Combined use of expression and CGH arrays pinpoints novel candidate genes in Ewing sarcoma family of tumors

<p>Abstract</p> <p>Background</p> <p>Ewing sarcoma family of tumors (ESFT), characterized by t(11;22)(q24;q12), is one of the most common tumors of bone in children and young adults. In addition to <it>EWS/FLI1 </it>gene fusion, copy number changes are known to be significant for the underlying neoplastic development of ESFT and for patient outcome. Our genome-wide high-resolution analysis aspired to pinpoint genomic regions of highest interest and possible target genes in these areas.</p> <p>Methods</p> <p>Array comparative genomic hybridization (CGH) and expression arrays were used to screen for copy number alterations and expression changes in ESFT patient samples. A total of 31 ESFT samples were analyzed by aCGH and in 16 patients DNA and RNA level data, created by expression arrays, was integrated. Time of the follow-up of these patients was 5–192 months. Clinical outcome was statistically evaluated by Kaplan-Meier/Logrank methods and RT-PCR was applied on 42 patient samples to study the gene of the highest interest.</p> <p>Results</p> <p>Copy number changes were detected in 87% of the cases. The most recurrent copy number changes were gains at 1q, 2, 8, and 12, and losses at 9p and 16q. Cumulative event free survival (ESFT) and overall survival (OS) were significantly better (P < 0.05) for primary tumors with three or less copy number changes than for tumors with higher number of copy number aberrations. In three samples copy number imbalances were detected in chromosomes 11 and 22 affecting the <it>FLI1 </it>and <it>EWSR1 </it>loci, suggesting that an unbalanced t(11;22) and subsequent duplication of the derivative chromosome harboring fusion gene is a common event in ESFT. Further, amplifications on chromosomes 20 and 22 seen in one patient sample suggest a novel translocation type between <it>EWSR1 </it>and an unidentified fusion partner at 20q. In total 20 novel ESFT associated putative oncogenes and tumor suppressor genes were found in the integration analysis of array CGH and expression data. Quantitative RT-PCR to study the expression levels of the most interesting gene, <it>HDGF</it>, confirmed that its expression was higher than in control samples. However, no association between <it>HDGF </it>expression and patient survival was observed.</p> <p>Conclusion</p> <p>We conclude that array CGH and integration analysis proved to be effective methods to identify chromosome regions and novel target genes involved in the tumorigenesis of ESFT.</p