scholarly journals Trimethylated H3K27, and Di- and Trimethylated H3K4 Proteomic Profiling Distinguishes Acute Lymphoid Leukemia (ALL) from Acute Myeloid Leukemia (AML) and Associates with Overall Survival and Tyrosine Kinase Inhibitor Sensitivity in Adult ALL

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1460-1460
Author(s):  
Anneke D. van Dijk ◽  
Fieke W Hoff ◽  
Yihua Qiu ◽  
Mary Figueroa ◽  
Joya Chandra ◽  
...  
Keyword(s):  
Tyrosine Kinase ◽  
Histone Methylation ◽  
Research Funding ◽  
Expression Profiles ◽  
Methylation Status ◽  
Unsupervised Clustering ◽  
Response To Therapy ◽  
Pathway Enrichment Analysis ◽  
Specific Gene ◽  
Repressive Mark

Background: Lineage-specific gene transcription signatures between AML and ALL are recognized, but post-translational phenotype-specific protein expression profiles remain undefined. We hypothesized that functional proteomic patterns vary between AML and ALL and that the activity state of cells correlates with response to therapy within subgroups, complementing cytogenetic and molecular data. Methods: Reverse phase protein arrays (RPPA) were generated using bone marrow (BM) and peripheral blood (PB) samples from newly diagnosed B-ALL (n=114), T-ALL (n=14), and AML (n=241) adult patients admitted at the MD Anderson Cancer Center. RPPA allowed simultaneous expression measurement of 229 highly validated protein antibodies including 3 Histone 3 (H3) post-translational methylation regulatory modifications; H3K4Me2, H3K4Me3 and H3K27Me3. Results: Unsupervised clustering of histone modification protein expressions distinguished AML from ALL in freshly prepared lysates from BM (n=241) and PB (n=127) as well as when BM and PB samples were combined (fig. 1A). The ALL-enriched cluster was dominated by high H3K27Me3. Elevated H3K27Me3 levels were found in the BM derived leukemic blasts compared to PB blasts in ALL (P < 0.001), but not AML (P = 0.35). Trimethylation of the repressive mark H3K27 is catalyzed by the polycomb group protein Ezh2. Oncogenic gain-of-functions of Ezh2 are seen in patients with lymphoid malignancies and others have shown that mutated Ezh2 increased H3K27Me3 in B-cells which associated with tumorigenesis. H3K27Me3 and Ezh2 antibody expressions were highly correlated in another RPPA of ALL and AML we created (R2=0.49, P < 0.001). Profiling of methylation marks using unsupervised clustering in ALL divided patients in 2 clusters that correlated with survival (fig. 1B-C, P = 0.02). Cluster 1 (C1) with higher H3K27Me3, H3K4Me2 and H3K4Me3 was associated with better outcome. In ALL, Ph+ historically associated with poor prognosis but outcomes have improved substantially with the use of tyrosine kinase inhibitors (TKI). In our cohort, 11/26 Ph+ ALL patients were treated with TKIs and it is notable that sensitivity to TKIs correlated with cluster membership; all C1 patients (high degree of methylation) were alive after 7 years of follow-up in contrast to none of the TKI-treated Ph+ ALL patients in cluster 2 (C2, low degree of methylation) (fig. 1D, P = 0.01). Recently, TKI resistance in Ph+ ALL has been proposed to associate with smoking due to altered DNA methylation patterns caused by chemical components of cigarette smoke. Retrospectively, we identified that 2 of 11 TKI treated patients were smokers. Both had membership in C2, were resistant against TKIs and died after 1 year. Thus, 2 out of 3 resistant TKI treated Ph+ ALL were smokers compared to none of the 8 responders. We then aimed to identify proteins that are potentially downregulated by increased expression of the repressive mark H3K27Me3. Pathway enrichment analysis of 59 significant negatively correlated proteins with H3K27Me3 revealed that these are involved in tyrosine kinase activity and resistance, including Jak/STAT and PI3K/Akt signaling pathways. If these pathways are less activated in patients with high H3K27Me3, then this can partially explain the increased sensitivity to TKIs in this subgroup. Clinically, no differences were found in age, BM and PB blast counts between TKI-treated C1 and C2 patients to provide an explanation for the higher death rate in C2. Conclusion: ALL and AML share some pathophysiology and the identification of differences in the functional activity of cells may contribute to a better understanding of the etiology of both diseases. Here we report that high H3K27Me3 protein levels in BM and PB distinguish ALL from AML and are related to TKI sensitivity in Ph+ ALL. Histone methylation status defines a group of Ph+ ALL patients that does not benefit from the addition of TKI therapy. The idea that smoking alters the epigenetic machinery in TKI resistant Ph+ ALL has been proposed and warrants further investigation. Fig. 1 A) Heatmap showing histone methylation levels in BM and PB from AML and ALL patients. B) Heatmap showing histone methylation levels in ALL BM and PB. Unsupervised clustering divided samples into 2 clusters. C) ALL patients in C1 survived longer than patients in C2 (P = 0.02). D) Increased long-term sensitivity for TKI therapy in C1 Ph+ ALL patients compared to C2 (100 vs. 0%, P = 0.01). Figure.1 Disclosures Jabbour: AbbVie: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Cyclacel LTD: Research Funding; Adaptive: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Amgen: Consultancy, Research Funding.

Microarray Analyses in a Case-Control Cohort of T-ALL Samples Identifies Gene Signature of Potential Prognostic Significance.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 1448-1448
Author(s):  
Stuart S. Winter ◽  
Hadya Khawaja ◽  
Zeyu Jiang ◽  
Charles Kooperberg ◽  
Adolfo Ferrando ◽  
...  
Keyword(s):  
Microarray Analysis ◽  
Target Genes ◽  
Expression Profiles ◽  
Prognostic Significance ◽  
Gene Signature ◽  
Case Control ◽  
Response To Therapy ◽  
Prognostic Indicators ◽  
Specific Gene ◽  
High Dose

Abstract Risk stratification remains limited for patients being treated for T-ALL due to a lack of biologic predictors of outcome. As a consequence, treatment assignment on modern protocols has been largely achieved through random assignment. Recent observations have suggested that overexpression of specific gene(s) may provide a reliable means to risk stratify patients. We hypothesized that microarray analysis may identify gene sets that distinguish both therapeutic response and patient outcome in T-ALL. We analyzed the gene expression profiles of 45 primary T-ALL samples (24 CCR, 21 relapse) from a matched, case control study with sufficient cRNA for microarray analysis (COG #8704). We performed oligonucleotide microarray analysis using Affymetrix U133Av.2 genechips which have approximately 54,000 target genes and ESTs. Following heirarchical clustering in dChip and R Language analyses (Chiaretti et al. Blood, 2004), but using RMA normalization, we identified 37 genes that serve as reliable predictors of CCR or relapse. Leave-one-out least discriminant analysis cross-over validation further constrained our prognostic gene identifiers to 21 genes of robust significance. These 21 genes predict 87 % of CCR and 82% of relapse accurately (p<0.0001, two-tailed Fisher’s exact test). These results were verified by qRT-PCR. Transcriptional factors previously described as having prognostic significance were not identified in our study. Twenty-six of the 45 cases received high dose L-asparaginase (16 CCR, 10 relapse) on the companion study. As a result, we examined whether a distinct signature could be also identified that distinguishes response to dose-intensified asparaginase treatment for patients with T-ALL. Using the same approach, a 27-member gene signature was identified that accurately predicted response in 24 of the 26 cases (92 %; 2 cases of relapse were misclassified). These results have identified two sets of genes that may be further pursued as prognostic indicators in T-ALL or as predictors of response to therapy.

scholarly journals Overcoming Adaptive Therapy Resistance in AML By Targeting Immune Response Pathways

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 3934-3934
Author(s):  
Katelyn M. Melgar ◽  
LaQuita M Jones ◽  
Mackenzie Walker ◽  
Lyndsey C Bolanos ◽  
Kathleen Hueneman ◽  
...  
Keyword(s):  
Stress Response ◽  
Tyrosine Kinase ◽  
Board Of Directors ◽  
Small Molecule ◽  
Research Funding ◽  
Response To Therapy ◽  
Innate Immune ◽  
Advisory Committees ◽  
Adaptive Resistance ◽  
Flt3 Inhibitors

Targeted inhibitors to oncogenic kinases demonstrate encouraging clinical responses early in the treatment course, however, most patients will relapse due to target-dependent mechanisms that mitigate enzyme-inhibitor binding or through target-independent mechanisms, such as alternate activation of survival and proliferation pathways, known as adaptive resistance. One example involves the FMS-like receptor tyrosine kinase (FLT3). Activating mutations of FLT3 result in its autophosphorylation and initiation of intracellular signaling pathways, which induce abnormal survival and proliferation of leukemic cells.One of the most common mutations in acute myeloid leukemia (AML) involves the internal tandem duplication (ITD) of FLT3, which occurs in ~25% of all cases of newly diagnosed AML and confers a particularly poor prognosis. FLT3 inhibitors (FLT3i) evaluated in clinical studies as monotherapy and combination therapies have shown good initial response rates; however, patients eventually relapse with FLT3i-resistant disease. The absence of durable remission in patients treated with potent and selective FLT3i highlights the need to identify resistance mechanisms and develop additional treatment strategies. Several mechanisms contribute to resistance to selective FLT3i, including mutations in the tyrosine kinase domain of FLT3 (20-50%) or activation of parallel signaling mechanisms that bypass FLT3 signaling, referred to as adaptive resistance (30-50%). Here we describe mechanisms of adaptive resistance in FLT3-mutant AML by examining in-cell kinase and gene regulatory network responses after oncogenic signaling blockade by FLT3 inhibitors (FLT3i). Through this integrative approach, we identified activation of innate immune stress response pathways after treatment of FLT3-mutant AML cells with FLT3i. Utilizing genetic approaches, we demonstrated that innate immune pathway activation via IRAK1 and IRAK4 contributes to adaptive resistance in FLT3-mutant AML cells. The immediate nature of IRAK1/4 activation in adaptively resistant FLT3-ITD AML cells requires concomitant inhibition of these targets to avoid compensatory signaling and cell survival. Achieving optimal multi-drug combination regimens that yield extended overlapping exposure while avoiding unwanted toxicities is challenging. Therefore, we desired a small molecule inhibitor that simultaneously targeted the FLT3 and IRAK1/4 kinases to eradicate adaptively resistant FLT3-ITD AML. To overcome this adaptive resistance mechanism, we developed and optimized a novel small molecule that simultaneously inhibits FLT3 and IRAK1/4 kinases. The FLT3-IRAK1/4 inhibitor exhibited potent binding affinity for IRAK1 (KD= 2.9 nM), IRAK4 (KD= 0.3 nM), and FLT3 (KD= 0.3 nM), as well as acceptable pharmacokinetic properties in mice. Moreover, a high-resolution crystal structure demonstrates that the FLT3-IRAK1/4 inhibitor binds as a type I inhibitor (ATP-competitive binding to the active state). The FLT3-IRAK1/4 inhibitor eliminated adaptively resistant FLT3-mutant AML cell lines and patient-derived samples in vitro and in vivo, and displayed superior efficacy as compared to current targeted FLT3 therapies. Our study demonstrates that therapies that simultaneously inhibit FLT3 signaling and compensatory IRAK1/4 activation have the potential to improve the therapeutic efficacy in patients with FLT3-mutant AML. In conclusion, these findings reveal that inflammatory stress response pathways contribute to adaptive resistance in FLT3-mutant AML and suggests that this mechanism may extend to other malignant cells undergoing a stress-induced response to therapy. Disclosures Hoyt: Kurome Therapeutics: Consultancy. Berman:Astellas: Membership on an entity's Board of Directors or advisory committees, Research Funding. Levine:Qiagen: Membership on an entity's Board of Directors or advisory committees; Prelude Therapeutics: Research Funding; Amgen: Honoraria; Lilly: Honoraria; Gilead: Consultancy; C4 Therapeutics: Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy; Roche: Consultancy, Research Funding; Imago Biosciences: Membership on an entity's Board of Directors or advisory committees; Isoplexis: Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Research Funding; Loxo: Membership on an entity's Board of Directors or advisory committees. Rosenbaum:Kurome Therapeutics: Consultancy, Employment. Perentesis:Kurome Therapeutics: Consultancy. Starczynowski:Kurome Therapeutics: Consultancy.

Tumor Suppressor FOXO1 Serves As a Critical Oncogenic Mediator in AML1-ETO Leukemia

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 264-264
Author(s):  
Shan Lin ◽  
Jinsong Zhang ◽  
James C. Mulloy
Keyword(s):  
Cell Growth ◽  
Tumor Suppressor ◽  
Dna Binding ◽  
Research Funding ◽  
Expression Profiles ◽  
Gene List ◽  
Pathway Enrichment Analysis ◽  
Human Umbilical Cord ◽  
General Function ◽  
Conditional Deletion

Abstract AML1-ETO (AE) fusion protein, produced by the chromosome translocation (8;21), accounts for 10%-15% of acute myeloid leukemia (AML). We use CD34+ human umbilical cord blood (CB) cells retrovirally transduced with AE to model AE leukemia (AE cells). This model represents the pre-leukemic stage of AE leukemia since AE by itself does not lead to overt disease but largely enhances the self-renewal and impedes the differentiation of CB cells. However the molecular mechanism by which AE deregulates the hematopoietic program is still largely unknown. The FOXO subfamily of forkhead transcription factors, which in mammals includes FOXO1, FOXO3, FOXO4, and FOXO6, are well known tumor suppressor proteins due to their ability to arrest cell cycle and induce apoptosis. Although FOXO1 is silenced in many types of solid tumors, unexpectedly, we found expression of FOXO1 (but not other family members) significantly upregulated in AE AML patient samples compared to other AML subtypes. Increased protein expression was confirmed by western blot in a cohort of patient samples and our AE cells. AE bound to a region within intron 1 of the FOXO1 gene as shown by chromatin immunoprecipitation, the sequence of which includes several AML1 binding motifs. The expression of FOXO1 was dependent on the presence of AE as shown by overexpression and knockdown studies. These findings suggest that FOXO1 could be a direct target of AE. Knockdown of FOXO1 by shRNA resulted in decreased proliferation of AE cells, and conditional deletion of Foxo1 in murine AE cells led to decreased CFU replating in methylcellulose compared to non-deleted control. In contrast, conditional deletion of Foxo3 using the same approach did not significantly influence the clonogenicity of AE cells. These data indicate FOXO1 is critical for AE cell growth. Interestingly, overexpression of FOXO1 in CD34+ CB cells promoted their long-term proliferation in liquid culture, inhibited erythroid differentiation while promoting myeloid lineage development and enhanced their replating ability in colony forming assays. FOXO1-expressing cells also achieved a higher engraftment level when transplanted into immunodeficient mice. These effects partially phenocopy the outcomes observed upon AE expression in CB cells. These effects require the transcriptional activity of FOXO1, as shown with a FOXO1 mutant lacking DNA binding capacity, and are not seen upon FOXO3 expression. Although inhibition of reactive oxygen species (ROS) production is regarded as a general function of FOXO family proteins, ROS regulation is unlikely to be the major downstream mechanism, since ROS levels were only transiently and slightly decreased upon FOXO1 expression. In addition, treatment of CB cells with the ROS scavenger N-acetyl-cysteine could not recapitulate FOXO1’s phenotype. To gain molecular insight into FOXO1’s function in CB cells, RNA-Seq experiments were performed in triplicate. CD34+ CB cells were transduced with empty vector (MIT), FOXO1 DNA-binding-deficient mutant (FOXO1 DB), wildtype FOXO1 (FOXO1 WT) or AE. The MIT and FOXO1 DB groups showed very similar gene expression profiles. 1349 genes were upregulated and 1113 genes downregulated in the AE group while FOXO WT showed 608 genes increased and 490 genes decreased (P<=0.01, fold change>=1.5). To our surprise, 282 upregulated and 231 downregulated genes were common targets of AE and FOXO WT, suggesting that about 20% of AE downstream targets are potentially regulated via FOXO1. More strikingly, pathway enrichment analysis showed that published AE gene signature datasets were enriched in the FOXO1-WT gene list. Among genes upregulated by FOXO1, several have been reported as AE transcriptional targets, such as POU4F1 and JUP. In addition, some FOXO1 target genes, SOX4 and HLF for example, have been implicated in leukemogenesis. In summary, we find FOXO1 is required for AE cell growth, and overexpression of FOXO1 in CB cells can partially recapitulate the AE phenotype at both the morphological and molecular level. Taken together, these data suggest that instead of acting as a tumor suppressor, FOXO1 is a critical oncogenic mediator of the AE leukemia program. It is possible that FOXO1 serves as one signaling hub of the AE molecular network, similar to the role MEIS1-HOXA9 play in MLL-fusion AML. Fully dissecting the AE-FOXO1 pathway will enhance our understanding of AE leukemogenesis and provide potential therapeutic targets. Disclosures Mulloy: Celgene: Research Funding; Seattle Genetics: Research Funding; Amgen: Research Funding; NovImmune: Research Funding.

A miRNA Risk Score for the Prediction of Response to Lenalidomide in Multiple Myeloma (MM) Patients

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 987-987 ◽  
Author(s):  
Paola Neri ◽  
Andrew R Belch ◽  
Jordan Johnson ◽  
Kathy J Gratton ◽  
Li Ren ◽  
...  
Keyword(s):  
Risk Score ◽  
Mirna Expression ◽  
Research Funding ◽  
Plasma Cells ◽  
Expression Profiles ◽  
Response To Therapy ◽  
Differentially Expressed ◽  
P Value ◽  
Prediction Of Response ◽  
Mirnas Expression

Abstract Abstract 987 Background: Lenalidomide has demonstrated clinical activity in patients with newly diagnosed or relapsed MM, however nearly a third of relapsed patients fail to respond to it. While preclinical studies have reported variable biomarkers and pathways (Wnt-GSK3b-beta catenin; eIF4E-C/EBPb-IRF4; Cul4A-DDB1-Cereblon) as mediating the anti-MM effects of IMiDs, a comprehensive risk score for prediction of response to Lenalidomide is lacking. MiRNA are highly preserved non-coding RNAs that act post-transcriptionally to regulate gene expression by binding to the 3'UTR of mRNAs. To date studies have reported selective miRNAs expression in plasma cells at different stages of the clonal disease progression (MGUS to MM), correlated miRNA expression with distinct MM molecular subgroups and demonstrated a genome-wide elevated expression of miRNAs in high-risk MM. Herein, we have conducted a comprehensive profiling of miRNA and mRNA expression in Lenalidomide treated MM patients and established a miRNA-based risk score that is predictive of response to therapy. Methods and results: We have postulated that a miRNA signature in MM is predictive of response to Lenalidomide based therapy. To test this hypothesis, we analyzed in a testing cohort (n=20) the miRNA and mRNA signatures of Lenalidomide sensitive “S” and resistant “R” MM patients. In order to account for the role of the bone marrow environment in this disease, RNAs were extracted from 1mm punched biopsies of non-sorted and plasma cells enriched areas of the bone marrows collected immediately prior to initiating Lenalidomide. MiRNAs were hybridized to the miRNA Affymetrix gene-chip and raw miRNA expression values were log2 transformed and normalized (miRNA-QC tool, Affymetrix). Comparison of normalized miRNAs expression in “S” versus “R” patients (Anova testing) identified 29 differentially expressed miRNAs (Fold change < −2 or > 2 with a p value and FDR <0.01) between these two groups. In order to establish a risk score (RS) for prediction of response to Lenalidomide, we performed a stepwise canonical discriminant analysis with response to Lenalidomide as grouping variable and the 29 differentially expressed miRNA as independent variables. The discriminant analysis identified a RS based on the log2-scale expression of 4 miRNAs using the following equation: RS= ((1.068*hsa-miR-21) + (1.367*hsa-miR-26b) +(1.761* has-miR-3147)+(3.523*has-miR-34a) – 36.692). This univariate summary (ie, RS) of the miRNA expression profiles for each patient enabled accurate (100%) prediction of response to Lenalidomide. All patients with a RS < 0 were sensitive to Lenalidomide with a mPFS and mOS of 21.4 and 39.5 months respectively. In contrast, all patients with a RS > 0 were resistant to Lenalidomide with a mPFS and mOS of 4.1 and 24 months respectively. Validation of this RS was also performed in an independent cohort (n=20) of MM patients treated with lenalidomide. In this validation cohort the miRNA RS accurately predicted response to therapy in 90% of the cases. The mPFS and mOS were 32.0 and 43.1 months in patients with a RS < 0 (predicted as responders) as opposed to a mPFS and mOS of 6.6 and 7.6 months in patients with a RS > 0 (predicted as non-responders) (Figure below). mRNA profiling (U133A Plus2 array chip) was also performed on the plasma cells enriched bone marrow sections with 554 genes identified as differentially expressed (Fold change < −2 or > 2 with a p value and FDR <0.005) between Lenalidomide S and R patients. Using the TargetScan miRNA target mRNA prediction tool, combinatory analysis of miRNA and mRNA expression profiles of these MM patients identified positive and negative correlations (p<0.05) between differentially expressed miRNA and mRNAs. Lastly in a multivariate Cox regression analysis that included ISS stage, FISH cytogenetics ((del17p and t(4;14)) and the 4 miRNA RS, these variables were independent predictors of survival post Lenalidomide based therapy. Conclusion: We believe that this miRNA Risk Score provides a robust method of predicting sensitivity or resistance to Lenalidomide in MM patients and warrants further validation in a larger prospective study. The biological functions of these 4 miRNAs and their regulation of MM cells sensitivity to Lenalidomide is currently being investigated in vitro in a library of MM cell lines. Disclosures: Neri: Celgene: Honoraria, Research Funding. Belch:Celgene: Research Funding; Onyx: Research Funding. Bahlis:Celgene: Honoraria, Speakers Bureau.

scholarly journals Gene co-expression analysis and tissue-specific gene identification in Liriodendron chinense via hybrid sequencing

2020 ◽  
Author(s):  
zhonghua tu ◽  
Yufang Shen ◽  
Shaoying Wen ◽  
Huanhuan Liu ◽  
Lingmin Wei ◽  
...  
Keyword(s):  
Shoot Apex ◽  
Expression Profiles ◽  
Plant Defence ◽  
Pathway Enrichment Analysis ◽  
Specific Gene ◽  
Mads Box ◽  
Mads Box Genes ◽  
Vegetative Organs ◽  
Tissue Specific ◽  
Liriodendron Chinense

Abstract Background Liriodendron chinense (Hemsl.) Sarg. is an economically and ecologically important deciduous tree species that has been studied for many years. Although the complete L. chinense genome has been sequenced, the gene co-expression modules and tissue-specific genes of L. chinense remain unknown. Results Here, we used the bracts, petals, sepals, stamens, pistils, leaves, and the shoot apex of L. chinense as materials and analysed their gene co-expression modules and tissue-specific genes via hybrid sequencing. We identified 3,032 DEGs between the floral and vegetative tissues and 2,126 tissue-specific genes. By using WGCNA analysis, we identified 13 gene co-expression modules, and KEGG pathway enrichment analysis revealed that tissue-specific genes and genes from different modules were enriched in different pathways. Genes associated with plant defence were highly expressed in the bracts, genes participating in plant hormone signal transduction were highly expressed in the shoot apex, and genes participating in photosynthesis were highly expressed in the leaves, petals and sepals. Moreover, we identified 10 MIKC-type MADS-box genes that were classified as member of the AP3/PI, SVP, SEP, AG/SHP/STK, AGL12, SOC1 and TM8 subfamily. Phylogenetic analysis showed that the expression profiles of these ten genes were consistent with those reported in Arabidopsis and Populus , indicating that these genes are highly conserved evolutionarily and related to floral and vegetative tissue development. The small number of MIKC-type MADS-box genes in L. chinense was probably owing to its incomplete genome annotation. Conclusions In this work, we provided a reference transcriptome for L. chinense research by using hybrid sequencing. We identified 2,126 tissue-specific genes and 3,032 DEGs that contributed greatly to the functional differences between vegetative organs and floral organs. By using WGCNA analysis, 13 gene co-expression modules and 52 hub genes from six co-expression modules of interest were identified. Moreover, we identified 10 MIKC-type MADS-box genes that might be related to the development and growth regulation of floral and vegetative organs. These findings will improve our understanding of gene co-expression, tissue specific genes and flower development model of L. chinense .

Insights into therapeutic targets and biomarkers using integrated multi-‘omics’ approaches for dilated and ischemic cardiomyopathies

2021 ◽  
Author(s):  
Austė Kanapeckaitė ◽  
Neringa Burokienė
Keyword(s):  
Machine Learning ◽  
Single Cell ◽  
Learning Algorithm ◽  
Expression Profiles ◽  
Therapeutic Targets ◽  
Development Stage ◽  
Biological Data ◽  
Specific Gene ◽  
Tissue Remodelling ◽  
Pharmacological Management

Abstract At present, heart failure (HF) treatment only targets the symptoms based on the left ventricle dysfunction severity; however, the lack of systemic ‘omics’ studies and available biological data to uncover the heterogeneous underlying mechanisms signifies the need to shift the analytical paradigm towards network-centric and data mining approaches. This study, for the first time, aimed to investigate how bulk and single cell RNA-sequencing as well as the proteomics analysis of the human heart tissue can be integrated to uncover HF-specific networks and potential therapeutic targets or biomarkers. We also aimed to address the issue of dealing with a limited number of samples and to show how appropriate statistical models, enrichment with other datasets as well as machine learning-guided analysis can aid in such cases. Furthermore, we elucidated specific gene expression profiles using transcriptomic and mined data from public databases. This was achieved using the two-step machine learning algorithm to predict the likelihood of the therapeutic target or biomarker tractability based on a novel scoring system, which has also been introduced in this study. The described methodology could be very useful for the target or biomarker selection and evaluation during the pre-clinical therapeutics development stage as well as disease progression monitoring. In addition, the present study sheds new light into the complex aetiology of HF, differentiating between subtle changes in dilated cardiomyopathies (DCs) and ischemic cardiomyopathies (ICs) on the single cell, proteome and whole transcriptome level, demonstrating that HF might be dependent on the involvement of not only the cardiomyocytes but also on other cell populations. Identified tissue remodelling and inflammatory processes can be beneficial when selecting targeted pharmacological management for DCs or ICs, respectively.

scholarly journals Transcriptomic phases of periodontitis lesions using the nonhuman primate model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jeffrey L. Ebersole ◽  
Radhakrishnan Nagarajan ◽  
Sreenatha Kirakodu ◽  
Octavio A. Gonzalez
Keyword(s):  
Gene Expression ◽  
Nonhuman Primate ◽  
Gingival Crevicular Fluid ◽  
Inflammatory Responses ◽  
Expression Profiles ◽  
Lesion Detection ◽  
Gingival Tissue ◽  
Specific Gene ◽  
Nonhuman Primate Model ◽  
Primate Model

AbstractWe used a nonhuman primate model of ligature-induced periodontitis to identify patterns of gingival transcriptomic after changes demarcating phases of periodontitis lesions (initiation, progression, resolution). A total of 18 adult Macaca mulatta (12–22 years) had ligatures placed (premolar, 1st molar teeth) in all 4 quadrants. Gingival tissue samples were obtained (baseline, 2 weeks, 1 and 3 months during periodontitis and at 5 months resolution). Gene expression was analyzed by microarray [Rhesus Gene 1.0 ST Array (Affymetrix)]. Compared to baseline, a large array of genes were significantly altered at initiation (n = 6049), early progression (n = 4893), and late progression (n = 5078) of disease, with the preponderance being up-regulated. Additionally, 1918 genes were altered in expression with disease resolution, skewed towards down-regulation. Assessment of the genes demonstrated specific profiles of epithelial, bone/connective tissue, apoptosis/autophagy, metabolism, regulatory, immune, and inflammatory responses that were related to health, stages of disease, and tissues with resolved lesions. Unique transcriptomic profiles occured during the kinetics of the periodontitis lesion exacerbation and remission. We delineated phase specific gene expression profiles of the disease lesion. Detection of these gene products in gingival crevicular fluid samples from human disease may contribute to a better understanding of the biological dynamics of the disease to improve patient management.

scholarly journals Gene Expression Profiles Associated with Radio-Responsiveness in Locally Advanced Rectal Cancer

Biology ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 500
Author(s):  
Jeeyong Lee ◽  
Junhye Kwon ◽  
DaYeon Kim ◽  
Misun Park ◽  
KwangSeok Kim ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Expression Profiles ◽  
Cell Cycle Control ◽  
Methylation Status ◽  
Locally Advanced ◽  
Gene Expression Profiles ◽  
Advanced Rectal Cancer ◽  
Bioinformatic Analysis ◽  
Locally Advanced Rectal Cancer ◽  
Genes Encoding

LARC patients were sorted according to their radio-responsiveness and patient-derived organoids were established from the respective cancer tissues. Expression profiles for each group were obtained using RNA-seq. Biological and bioinformatic analysis approaches were used in deciphering genes and pathways that participate in the radio-resistance of LARC. Thirty candidate genes encoding proteins involved in radio-responsiveness–related pathways, including the immune system, DNA repair and cell-cycle control, were identified. Interestingly, one of the candidate genes, cathepsin E (CTSE), exhibited differential methylation at the promoter region that was inversely correlated with the radio-resistance of patient-derived organoids, suggesting that methylation status could contribute to radio-responsiveness. On the basis of these results, we plan to pursue development of a gene chip for diagnosing the radio-responsiveness of LARC patients, with the hope that our efforts will ultimately improve the prognosis of LARC patients.

scholarly journals Bayesian approach for predicting responses to therapy from high-dimensional time-course gene expression profiles

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Arika Fukushima ◽  
Masahiro Sugimoto ◽  
Satoru Hiwa ◽  
Tomoyuki Hiroyasu
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Response To Therapy ◽  
Hcv Infection ◽  
Entire Treatment Period ◽  
Time Points ◽  
Time Course Data ◽  
Conventional Methods

Abstract Background Historical and updated information provided by time-course data collected during an entire treatment period proves to be more useful than information provided by single-point data. Accurate predictions made using time-course data on multiple biomarkers that indicate a patient’s response to therapy contribute positively to the decision-making process associated with designing effective treatment programs for various diseases. Therefore, the development of prediction methods incorporating time-course data on multiple markers is necessary. Results We proposed new methods that may be used for prediction and gene selection via time-course gene expression profiles. Our prediction method consolidated multiple probabilities calculated using gene expression profiles collected over a series of time points to predict therapy response. Using two data sets collected from patients with hepatitis C virus (HCV) infection and multiple sclerosis (MS), we performed numerical experiments that predicted response to therapy and evaluated their accuracies. Our methods were more accurate than conventional methods and successfully selected genes, the functions of which were associated with the pathology of HCV infection and MS. Conclusions The proposed method accurately predicted response to therapy using data at multiple time points. It showed higher accuracies at early time points compared to those of conventional methods. Furthermore, this method successfully selected genes that were directly associated with diseases.

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