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Acute myeloid leukemia (AML) often presents as an oligoclonal disease whereby multiple genetically distinct subclones can coexist within patients. Differences in signaling and drug sensitivity of such subclones complicate treatment and warrant tools to identify them and track disease progression. We previously identified >50 AML-specific plasma membrane (PM) proteins, and 7 of these (CD82, CD97, FLT3, IL1RAP, TIM3, CD25, and CD123) were implemented in routine diagnostics in patients with AML (n = 256) and myelodysplastic syndrome (n = 33). We developed a pipeline termed CombiFlow in which expression data of multiple PM markers is merged, allowing a principal component–based analysis to identify distinctive marker expression profiles and to generate single-cell t-distributed stochastic neighbor embedding landscapes to longitudinally track clonal evolution. Positivity for one or more of the markers after 2 courses of intensive chemotherapy predicted a shorter relapse-free survival, supporting a role for these markers in measurable residual disease (MRD) detection. CombiFlow also allowed the tracking of clonal evolution in paired diagnosis and relapse samples. Extending the panel to 36 AML-specific markers further refined the CombiFlow pipeline. In conclusion, CombiFlow provides a valuable tool in the diagnosis, MRD detection, clonal tracking, and understanding of clonal heterogeneity in AML.

The advent of single-cell sequencing opens new avenues for personalized treatment. In this paper, we address a two-level clustering problem of simultaneous subject subgroup discovery (subject level) and cell type detection (cell level) for single-cell expression data from multiple subjects. However, current statistical approaches either cluster cells without considering the subject heterogeneity or group subjects without using the single-cell information. To bridge the gap between cell clustering and subject grouping, we develop a nonparametric Bayesian model, Subject and Cell clustering for Single-Cell expression data (SCSC) model, to achieve subject and cell grouping simultaneously. SCSC does not need to prespecify the subject subgroup number or the cell type number. It automatically induces subject subgroup structures and matches cell types across subjects. Moreover, it directly models the single-cell raw count data by deliberately considering the data's dropouts, library sizes, and over-dispersion. A blocked Gibbs sampler is proposed for the posterior inference. Simulation studies and the application to a multi-subject iPSC scRNA-seq dataset validate the ability of SCSC to simultaneously cluster subjects and cells.

Objective: Lower grade gliomas (LGGs), classified as World Health Organization (WHO) grade II and grade III gliomas, comprise a heterogeneous group with a median survival time ranging from 4–13 years. Accurate prediction of the survival times of LGGs remains a major challenge in clinical practice. Methods: We reviewed the expression data of 865 LGG patients from 5 transcriptomics cohorts. The comparative profile of immune genes was analyzed for signature identification and validation. In-house RNAseq and microarray data from the Chinese Glioma Genome Atlas (CGGA) dataset were used as training and internal validation cohorts, respectively. The samples from The Cancer Genome Atlas (TCGA) and GSE16011 cohorts were used as external validation cohorts, and the real-time PCR of frozen LGG tissue samples (n = 36) were used for clinical validation. Results: A total of 2,214 immune genes were subjected to pairwise comparison to generate 2,449,791 immune-related gene pairs (IGPs). A total of 402 IGPs were identified with prognostic values for LGGs. The HOXA9-related and CRH-related scores facilitated identification of patients with different prognoses. An immune signature based on 10 IGPs was constructed to stratify patients into low and high risk groups, exhibiting different clinical outcomes. A nomogram, combining immune signature, 1p/19q status, and tumor grade, was able to predict the overall survival (OS) with c-indices of 0.85, 0.80, 0.80, 0.79, and 0.75 in the training, internal validation, external validation, and tissue sample cohorts, respectively. Conclusions: This study was the first to report a comparative profiling of immune genes in large LGG cohorts. A promising individualized immune signature was developed to estimate the survival time for LGG patients.

Glioma progression seriously correlates to the epigenetic context. This study aims to identify glioma subtypes by clustering analysis of patients using the multi-omics data of N6-methyladenosine (m6A) methylation regulators and to construct a risk signature for investigating the role of m6A methylation regulators in the prognosis of glioma. Multi-omics data of glioma and normal control tissues were obtained through The Cancer Genome Atlas (TCGA) database. The clustering analysis of multi-omics data of patients was conducted using the R package iClusterPlus software. The risk model was constructed by univariate and multivariate Cox analysis, and the glioma expression data and related clinical data were obtained by Chinese Glioma Genome Atlas (CGGA) datasets to verify the risk model. By analyzing the glioma data in TCGA, we found that the risk signature could be constructed according to the eight genes with m6A methylation modification function, including ALKBH5, HNRNPA2B1, IGF2BP2, IGF2BP3, RBM15, WTAP, YTHDF1, and YTHDF2. Meanwhile, we found that IGF2BP2 and IGF2BP3 were highly expressed in glioma subtypes with high-risk scores and closely related to the prognosis of glioma patients. m6A methylation regulators, especially IGF2BP2 and IGF2BP3, play important roles in the malignant progression of glioma. The risk signature constructed by eight m6A methylation regulators can predict the prognosis of glioma. IGF2BP2 and IGF2BP3 may be the key regulatory factors of m6A methylation regulators involved in the occurrence and development of glioma, and can serve as molecular markers for the prognosis of glioma.

Though single cell RNA sequencing (scRNA-seq) technologies have been well developed, the acquisition of large-scale single cell expression data may still lead to high costs. Single cell expression profile has its inherent sparse properties, which makes it compressible, thus providing opportunities for solutions. Here, by computational simulation as well as experiment of 54 single cells, we propose that expression profiles can be compressed from the dimension of samples by overlapped assigning each cell into plenty of pools. And we prove that expression profiles can be inferred from these pool expression data with overlapped pooling design and compressed sensing strategy. We also show that by combining this approach with plate-based scRNA-seq measurement, it can maintain its superiorities in gene detection sensitivity and individual identity and recover the expression profile with high precision, while saving about half of the library cost. This method can inspire novel conceptions on the measurement, storage or computation improvements for other compressible signals in many biological areas.

Amaç: Kardiyovasküler hastalıklar ve kanser metastazında sıklıkla araştı-rılan matriks metalloproteinazlar (MMPs) ekstraselüler matriks düzenle-yicileridir. Çalışmamız, aort diseksiyonu olan hastalarda MMP9 genindeki spesifik polimorfizmleri incelemeyi ve MMP9'un aort diseksiyonu üzerin-deki etkisini ekspresyon veri setleri ile karşılaştırmayı amaçlamaktadır.Gereç ve Yöntem: Q279R ve P574R polimorfizmleri 44 aort diseksiyon tanısı almış ve 40 sağlıklı bireyde polimeraz zincir reaksiyonu - restrik-siyon parça uzunluğu polimorfizmi (PCR-RFLP) yöntemiyle çalışıldı. Q279R ve P574R prevalansı istatistiksel olarak hastaların tıbbi verileriyle karşılaştırıldı. Buna ek olarak, NCBI GEO veri tabanından aort diseksiyon veri setleri toplandı ve MMP9 ifadesindeki farklılıkları görmek amacıyla bu veri setleri GEO2R ve RStudio ile yeniden analiz edildi. Elde edilen sonuçların informatik analizi için çevrimiçi veri tabanları kullanıldı.Bulgular: CG alleli taşıyıcısı kadınların aort diseksiyonu geliştirme riski erkeklerden daha yüksek bulunmasına rağmen her iki çalışma grubun-da da allellerin genotipik dağılımı benzer bulunmuştur. MMP9'un pro-tein-protein etkileşim analizinin ve hastaların tıbbi verilerinin incelen-mesinin sonucu olarak, P574R hipertansiyonu olan hastalarda önemli bir bulgu olarak değerlendirilmiştir. Array verisi analizinde ise MMP9 ifa-desinde kritik bir değişim gözlemlenmemiş olup, birçok örnekte TIMPifade seviyelerinde azalma tespit edilmiştir. Ayrıca MMP9’u hedefleyen miRNA ekspresyon seviyelerinin aort dokusu ve kanda düşük olduğu saptanmıştır.Sonuç: Q279R ve P574R, MMP9 protein yapısını doğrudan etkilemeyen iki polimorfizmdir. İncelenen polimorfizmler ve gerçekleştirilen meta-a-nalizler, MMP9'un fenotipi doğrudan etkilemediği, ancak istatistiksel sonuçlarda görüldüğü gibi aort diseksiyonu gelişimi için zemin hazır-ladığını göstermektedir., Objective: Matrix metalloproteinases (MMPs) are the extracellular ma-trix regulators that frequently investigate cardiovascular diseases and cancer metastasis. Our study aimed to examine specific polymorphisms in the MMP9 gene in our patients with aortic dissection and compare the effect of MMP9 on aortic dissection with expression datasets.Materials and Methods: Q279R and P574R polymorphisms were analyzed in 44 aortic dissection patients and 40 healthy donors via polymerase chain reaction-restriction fragment length polymorphism. (PCR-RFLP) methods. Q279R and P574R prevalence was statistically compared with the medical data of the patients. Additionally, we col-lected datasets of aortic dissection from NCBI GEO to reanalyze GEO2R and RStudio to see metalloproteinase activity on samples. Later, enrich-ment analysis was processed on widely used databases.Results: Genotypic distribution of alleles was similar in the two study groups. In addition to this, female CG carriers had a higher risk of de-veloping aortic dissection than those of males. As the results of the protein-protein interaction analysis of MMP9 and patients’ clinical data, hypertension was found to be the significant outcome of P574R varia-tion in the patients. In array analysis, MMP9 expression did not change critically, but TIMPs had been downregulated in many samples. Also, MMP9 targeted miRNA expression levels were detected as low in aortic tissue and blood.Conclusion: Q279R and P574R are two polymorphisms that do not di-rectly affect MMP9 protein structure. Consequently, studied polymor-phisms and performed meta-analysis show that MMP9 does not spark off the phenotype but sets the stage for aortic dissection development as seen in the statistical results. Furthermore, enrichment analysis on datasets shows MMP9 was not a primary reason for vascular remodeling.

Purpose: This study aims to uncover potential biomarkers associated with cutaneous melanoma (CM) metastasis.Methods: The mRNA and microRNA (miRNA) expression data from the metastatic CM and non-met...

Background Given expression data, gene regulatory network(GRN) inference approaches try to determine regulatory relations. However, current inference methods ignore the inherent topological characters of GRN to some extent, leading to structures that lack clear biological explanation. To increase the biophysical meanings of inferred networks, this study performed data-driven module detection before network inference. Gene modules were identified by decomposition-based methods. Results ICA-decomposition based module detection methods have been used to detect functional modules directly from transcriptomic data. Experiments about time-series expression, curated and scRNA-seq datasets suggested that the advantages of the proposed ModularBoost method over established methods, especially in the efficiency and accuracy. For scRNA-seq datasets, the ModularBoost method outperformed other candidate inference algorithms. Conclusions As a complicated task, GRN inference can be decomposed into several tasks of reduced complexity. Using identified gene modules as topological constraints, the initial inference problem can be accomplished by inferring intra-modular and inter-modular interactions respectively. Experimental outcomes suggest that the proposed ModularBoost method can improve the accuracy and efficiency of inference algorithms by introducing topological constraints.

Abiotic stress responses are regulated critically at the transcriptional level. Clarifying the intricate mechanisms that regulate gene expression in response to abiotic stress is crucial and challenging. For this purpose, the factors that regulate gene expression and their binding sites in DNA should be determined. By using bioinformatics tools, the differentially expressed probe sets were studied. A meta-analysis of transcriptomic responses to several abio¬tic stresses in barley was performed. Motif enrichments revealed that AP2/ERF (APETALA2/Ethylene-Res¬pon¬sive Factor) has the most frequent binding sites. We found that the bHLH transcription factor family has the high¬est number of transcription factor members. Moreover, network construction revealed that AP2 has the highest number of connections with other genes, which indicates its critical role in abiotic stress responses. The present research further predicted 49 miRNAs belonging to 23 miRNA families. This study identified the probable conserved and enriched motifs, which might have a role in the regulation of differentially expressed genes under abiotic stresses. In addition to shedding light on gene expression regulation, a toolbox of available promoters for genetic engineering of crop plants under such abiotic stresses was developed.

Graphical abstract, Heart failure with preserved ejection fraction (HFpEF) is associated with multiple etiologic and pathophysiologic factors. HFpEF leads to significant cardiovascular morbidity and mortality. There are various reasons that fail to identify effective therapeutic interventions for HFpEF, primarily due to its clinical heterogeneity causing significant difficulties in determining physiologic and prognostic implications for this syndrome. Thus, identifying clinical subtypes using multi-omics data has great implications for efficient treatment and prognosis of HFpEF patients. Here we proposed to integrate mRNA, DNA methylation and microRNA (miRNA) expression data of HFpEF with a similarity network fusion (SNF) method following a network enhancement (ne-SNF) denoising technique to form a fused network. A spectral clustering method was then used to obtain clusters of patient subtypes. Experiments on HFpEF datasets demonstrated that ne-SNF significantly outperforms single data subtype analysis and other integrated methods. The identified subgroups were shown to have statistically significant differences in survival. Two HFpEF subtypes were defined: a high-risk group (16.8%) and a low-risk group (83.2%). The 5-year mortality rates were 63.3% and 33.0% for the high- and low-risk group, respectively. After adjusting for the effects of clinical covariates, HFpEF patients in the high-risk group were 2.43 times more likely to die than the low-risk group. A total of 157 differentially expressed (DE) mRNAs, 2199 abnormal methylations and 121 DE miRNAs were identified between two subtypes. They were also enriched in many HFpEF-related biological processes or pathways. The ne-SNF method provides a novel pipeline for subtype identification in integrated analysis of multi-omics data.

Despite a growing interest in multi-omic research, individual investigators may struggle to collect large-scale omic data, particularly from human subjects. Publicly available datasets can help to address this problem, including those sponsored by the NIH Common Fund, such as the Genotype-Tissue Expression (GTEx) database. This database contains genotype and expression data obtained from 54 non-diseased tissues in human subjects. But these data are often underutilized, because users may find the browsing tools to be counterintuitive or have difficulty navigating the procedures to request controlled data access. Furthermore, there is limited knowledge of these resources among nurse scientists interested in incorporating such information into their programs of research. This article outlines the procedures for using the GTEx database. Next, we provide one exemplar of using this resource to enhance existing research by investigating expression of dopamine receptor type 2 ( DRD2) across brain tissues in human subjects.

Objective: Hard clustering approaches may cause some of the relationships to be overlooked due to their nature of algorithms especially in genetic datasets. But hidden relationships can be revealed by fuzzy approaches. Purpose of this study was evaluating effect of microRNAs (miRNA) on children with acute lymphoblastic leukaemia (ALL) by using miRNA expression data obtained from bone marrow samples with sets containing different numbers of elements of fuzzy Cmeans (FCM). Material and Methods: miRNA expression levels of 43 newly diagnosed ALL patients and 14 healthy subjects were analysed via FCM. Clusters containing different numbers of miRNAs were evaluated, common properties in messenger RNA (mRNA) pathways were investigated and new pathways associated with ALL and cancer were described via miRNA target prediction tools. Results: Significant miRNA profile was compared to control cases. Only 46 out of 108 miRNAs were found to be significantly upregulated or downregulated. Of forty six miRNAs: 8 miRNAs were labelled as tumour suppressor (17.4%), 17 miRNAs were labelled as onco-miR (37.0%) and 21 miRNAs could not be labelled (45.6%) for hematological malignancy. Fourteen (%30.4) miRNAs were found to be apoptosis-related, 27 miRNAs were in leukemia-related (58.7%) and 15 labelled miRNAs were related with cancer pathways (32.6%). hsa-miR-181b, hsa-miR- 146a, hsa-miR-155, hsa-miR-181c-5p, hsa-miR-7-1-3p, hsa-miR-708- 5p onco-miRs constituted a set. These miRNAs targeted 801 common mRNAs (p

The present study investigates the role of network topology in lung adenocarcinoma (LUAD) development. Analysis of sex- and stage-specific whole-genome expression data revealed that co-expressed and highly connected prognostic genes common to all cancer stages form a small-world network in each stage of LUAD. These small-world networks are present within stage-specific scale-free networks, conserved across the cancer stages, and linked to cancer-specific events. The presence of small-world networks across the cancer stages presents a synchronized system in the disordered environment of cancer, resulting in the evolution of malignancy. Our study reported that these small-world networks are resilient to random and systematic attacks, indicating the least opportunity to introduce perturbations by drugs as a therapeutic intervention. We concluded that highly clustered small-world networks could be controlled through transcriptional modulation for the improved treatment of LUAD.

Drug repositioning is an important area of biomedical research. The drug repositioning studies have shifted to computational approaches. Large-scale perturbation databases, such as the Connectivity Map and the Library of Integrated Network-Based Cellular Signatures, contain a number of chemical-induced gene expression profiles and provide great opportunities for computational biology and drug repositioning. One reason is that the profiles provided by the Connectivity Map and the Library of Integrated Network-Based Cellular Signatures databases show an overall view of biological mechanism in drugs, diseases and genes. In this article, we provide a review of the two databases and their recent applications in drug repositioning.

Candidatus Liberibacter asiaticus (CLas) has been associated with Huanglongbing, a lethal vector-borne disease affecting citrus crops worldwide. While comparative genomics has provided preliminary insights into the metabolic capabilities of this uncultured microorganism, a comprehensive functional characterization is currently lacking. Here, we reconstructed and manually curated genome-scale metabolic models for the six CLas strains A4, FL17, gxpsy, Ishi-1, psy62, and YCPsy, in addition to a model of the closest related culturable microorganism, L. crescens BT-1. Predictions about nutrient requirements and changes in growth phenotypes of CLas were confirmed using in vitro hairy root-based assays, while the L. crescens BT-1 model was validated using cultivation assays. Host-dependent metabolic phenotypes were revealed using expression data obtained from CLas-infected citrus trees and from the CLas-harboring psyllid Diaphorina citri Kuwayama. These results identified conserved and unique metabolic traits, as well as strain-specific interactions between CLas and its hosts, laying the foundation for the development of model-driven Huanglongbing management strategies.

This study aimed at exploring the genes that may be related to the prognosis of primary breast cancer (BC) patients. The gene expression microarray data, together with sample survival data were acquired from The Cancer Genome Atlas database. The top 20% genes according to expression value variance were subjected to hierarchical cluster analysis. Bootstrap methods were utilized to assess the stability of cluster. Cox regression was applied to screen genes related to the survival time of patients with BC, and the Beta-Uniform Mixture model was applied to adjust the significance of numerous tests. Further, ingenuity pathway analysis (IPA) was carried out to analyze the functions of the potential prognostic genes. Cluster analysis revealed that there were at least five stable BC subtypes, each with specific gene expression. Further, 42 survival time-associated genes were found (

Background:Gene Ontology (GO) is a major bioinformatic resource used for analysis of large biomedical datasets, for example from genome-wide association studies, applied universally across biological fields, including Alzheimer’s disease (AD) research.Objective:We aim to demonstrate the applicability of GO for interpretation of AD datasets to improve the understanding of the underlying molecular disease mechanisms, including the involvement of inflammatory pathways and dysregulated microRNAs (miRs).Methods:We have undertaken a systematic full article GO annotation approach focused on microglial proteins implicated in AD and the miRs regulating their expression. PANTHER was used for enrichment analysis of previously published AD data. Cytoscape was used for visualizing and analyzing miR-target interactions captured from published experimental evidence.Results:We contributed 3,084 new annotations for 494 entities, i.e., on average six new annotations per entity. This included a total of 1,352 annotations for 40 prioritized microglial proteins implicated in AD and 66 miRs regulating their expression, yielding an average of twelve annotations per prioritized entity. The updated GO resource was then used to re-analyze previously published data. The re-analysis showed novel processes associated with AD-related genes, not identified in the original study, such as ‘gliogenesis’, ‘regulation of neuron projection development’, or ‘response to cytokine’, demonstrating enhanced applicability of GO for neuroscience research.Conclusions:This study highlights ongoing development of the neurobiological aspects of GO and demonstrates the value of biocuration activities in the area, thus helping to delineate the molecular bases of AD to aid the development of diagnostic tools and treatments.

Background and Aim We evaluated the clinicopathological and prognostic significance of serum p53 (s‐p53‐Abs) and serum NY‐ESO‐1 autoantibodies (s‐NY‐ESO‐1‐Abs) in esophageal squamous cell carcinoma (ESCC), gastric cancer and hepatocellular carcinoma (HCC). Patients and Methods A total of 377 patients, 85 patients with ESCC, 248 patients with gastric cancer, and 44 patients with HCC were enrolled to measure s‐p53‐Abs and s‐NY‐ESO‐1‐Abs titers by the enzyme‐linked immunosorbent assay before treatment. The clinicopathological significance and prognostic impact of the presence of autoantibodies were evaluated. Expression data based on the Cancer Genome Atlas and the prognostic impact of gene expression was also examined for discussion. Results The positive rates of s‐p53‐Abs were 32.9% in ESCC, 15% in gastric cancer, and 4.5% in HCC. The positive rates of s‐NY‐ESO‐1‐Abs were 29.4% in ESCC, 9.7% in gastric cancer, and 13.6% in HCC. The presence of s‐p53‐Abs was not associated with tumor progression in these three cancer types. On the other hand, the presence of s‐NY‐ESO‐1‐Abs was significantly associated with tumor progression in ESCC and gastric cancer. The presence of s‐p53‐Abs and/or s‐NY‐ESO‐1‐Abs was significantly associated with poor prognosis in gastric cancer but not in ESCC nor HCC. Conclusions The presence of s‐p53‐Abs and/or s‐NY‐ESO‐1‐Abs was associated with tumor progression in ESCC and gastric cancer. These autoantibodies might have poor prognostic impacts on gastric cancer (UMIN000014530)., NY‐ESO‐1 antibodies were significantly associated with tumor progression in gastric cancer and esophageal squamous cell carcinoma, while the presence of p53 and/or NY‐ESO‐1 antibodies was significantly associated with poor prognosis only in gastric cancer.

An operon is a set of neighboring genes in a genome that is transcribed as a single polycistronic message. Genes that are part of the same operon often have related functional roles or participate in the same metabolic pathways. The majority of all bacterial genes are co-transcribed with one or more other genes as part of a multi-gene operon. Thus, accurate identification of operons is important in understanding co-regulation of genes and their functional relationships. Here, we present a computational system that uses RNA-seq data to determine operons throughout a genome. The system takes the name of a genome and one or more files of RNA-seq data as input. Our method combines primary genomic sequence information with expression data from the RNA-seq files in a unified probabilistic model in order to identify operons. We assess our method's ability to accurately identify operons in a range of species through comparison to external databases of operons, both experimentally confirmed and computationally predicted, and through focused experiments that confirm new operons identified by our method. Our system is freely available at https://cs.wellesley.edu/~btjaden/Rockhopper/.

With the assembly of increasing numbers of plant genomes, it is becoming accepted that a single reference assembly does not reflect the gene diversity of a species. The production of pangenomes, which reflect the structural variation and polymorphisms in genomes, enables in depth comparisons of variation within species or higher taxonomic groups. In this review, we discuss the current and emerging approaches for pangenome assembly, analysis and visualisation. In addition, we consider the potential of pangenomes for applied crop improvement, evolutionary and biodiversity studies. To fully exploit the value of pangenomes it is important to integrate broad information such as phenotypic, environmental, and expression data to gain insights into the role of variable regions within genomes.

Plant lipid transfer proteins (LTPs) are small basic proteins that play important roles in the regulation of various plant biological processes as well as the response to biotic and abiotic stresses. However, knowledge is limited on how this family of proteins is regulated in response to nematode infection in cucumber. In the present study, a total of 39 CsLTP_2 genes were identified by querying databases for cucumber-specific LTP_2 using a Hidden Markov Model approach and manual curation. The family has a five-cysteine motif (5CM) with the basic form CC-Xn-CXC-Xn-C, which differentiates it from typical nsLTPs. The members of CsLTP_2 were grouped into six families according to their structure and their phylogenetic relationships. Expression data of CsLTP_2 genes in 10 cucumber tissues indicated that they were tissue-specific genes. Two genes showed significant expression change in roots of resistant and susceptible lines during nematode infection, indicating their involvement in response to Meloidogyne incognita. This systematic analysis provides a foundation of knowledge for future studies of the biological roles of CsLTP_2 genes in cucumber in response to nematode infection and may help in the efforts to improve M. incognita-resistance breeding in cucumber.

Protein-protein interactions (PPIs) play an essential role in cellular regulatory processes. Despite, in-depth studies to uncover the mystery of PPI-mediated regulations are still lacking. Here, an integrative interactome network (MePPI-Ux) was obtained by incorporating expression data into the improved genome-scale interactome network of cassava (MePPI-U). The MePPI-U, constructed by both interolog- and domain-based approaches, contained 3,638,916 interactions and 24,590 proteins (59% of proteins in the cassava AM560 genome version 6). After incorporating expression data as information of state, the MePPI-U rewired to represent condition-dependent PPIs (MePPI-Ux), enabling us to envisage dynamic PPIs (DPINs) that occur at specific conditions. The MePPI-Ux was exploited to demonstrate timely PPIs of cassava under various conditions, namely drought stress, brown streak virus (CBSV) infection, and starch biosynthesis in leaf/root tissues. MePPI-Uxdrought and MePPI-UxCBSV suggested involved PPIs in response to stress. MePPI-UxSB,leaf and MePPI-UxSB,root suggested the involvement of interactions among transcription factor proteins in modulating how leaf or root starch is synthesized. These findings deepened our knowledge of the regulatory roles of PPIs in cassava and would undeniably assist targeted breeding efforts to improve starch quality and quantity.

—We have analyzed the gene expression dynamics in floral transition in the CDC Frontier chickpea cultivar. We provide a model, in several versions, to predict the expression dynamics of five flowering genes, taking the expression of their regulators as an input. The models were trained using the random forest method on the previously published expression data for ten flowering genes under the short- and long-day growing conditions. The resulting models correctly predict the dynamics of the average expression levels under long days. We show that the models for CDC Frontier mainly reproduce the regulatory interactions between the key genes described for the Arabidopsis thaliana model plant. Based on the analysis, we hypothesize that the short-day data and the long-day data contain qualitatively different information, which may be due to different regulatory modules that function in different conditions. For the regulators of the flower meristem identity genes AP1 and LFY, our models predict FTa3 as the main activator and TFL1c as the main repressor under long days.

Background discussion: Genomic signatures are revolutionizing the definition, identification, and treatment of breast cancer. To precisely stratify breast cancers into actionable subgroups, full genome (FG) expression data and matching clinical data must be aggregated into a large, real-world dataset. Such a dataset will accelerate research and discovery, especially for smaller patient subsets that are not as widely represented within the current body of literature. Trial design and aims: FLEX is a multicenter, prospective, population-based, observational trial for patients with Stage I, II, and III breast cancer. All patients with stage I to III breast cancer who receive MammaPrint (MP), with or without BluePrint (BP) on a primary breast tumor are eligible for enrollment. The study’s primary aim is to create a large scale, population-based registry of full genome expression data matched with clinical data to investigate new gene associations with prognostic and/or predictive value in a real-world setting. Secondary objectives include utilizing the shared study infrastructure to examine and generate hypotheses for targeted subset analyses and/or trials based on full genome expression data. The design of FLEX allows targeted sub-studies and sub-analyses to be added as appendices after the initial baseline study is opened. Data collection follows patients from diagnosis through 10 years of follow-up. Patients enrolled in the initial study are also eligible for inclusion in sub-studies for which they meet all criteria and additional consent is not required. Additional clinical data will be collected as specified in the appendix protocols. The FLEX collaborative platform allows participating investigators the opportunity to author their own sub-study protocols, as approved by the FLEX Steering Committee of their peers. 14 sub-studies have already been identified and approved (Table 1). Eligibility and accrual: The study will enroll a minimum of 10000 patients aged ≥18 years with histologically proven invasive stage I, II, or III breast cancer. Since April 2017 over 2362 patients have been enrolled. Trial contact information: NCT03053193 FLEX@agendia.com TABLE 1PIINSTITUTIONTYPEADDITIONAL CONSENTARM(S)DESCRIPTIONMehran HabibiJohns HopkinsLocal SubstudyYesNeoadjuvantComprehensive gene expression profiling of breast cancer in patients receiving short-course endocrine therapy prior to surgeryMehran HabibiJohns HopkinsLocal SubstudyYesAllCorrelation of the microbiome with breast cancer gene expressionThomas LomisValley Breast ClinicCollaborative group substudyYesNeoadjuvantComplementary data collection for patients participating in the ODM-201 trial. FLEX will provide gene expression profiling for exploratory and signature discovery purposesIan GradyNorth Valley Breast CareSubstudyNoAllImpact of genomic risk classification on travel time to receive breast cancer care: a QOL studyPat WhitworthNashville Breast CenterSubstudyNoNeoadjuvantGenomic reclassification of large tumors in patients eligible to receive neoadjuvant therapyAdam BrufskyUniversity of Pittsburgh Medical CenterSubstudyNoNeoadjuvant, adjuvantResponse to standard chemotherapy regimens in clinically ER+/PR+/HER2+ (triple positive) patients according to BluePrint molecular subtypesAdam BrufskyUniversity of Pittsburgh Medical CenterSubstudyNoAllExpression signatures by response to bisphosphonates in ER+ patients receiving adjuvant therapy (SOC option), or for osteoporosis after primary treatmentAdam BrufskyUniversity of Pittsburgh Medical CenterSubstudyNoAllGene expression in breast cancer patients with obesityJennifer A. CrozierBaptist MD Anderson Cancer CenterSubstudyNoAllMP and BP in male breast cancerJennifer A. CrozierBaptist MD Anderson Cancer CenterSubstudyNoAllMP and BP evaluation in breast cancer patients ≥70Jennifer A. CrozierBaptist MD Anderson Cancer CenterSubstudyNoAllFG evaluation in invasive lobular carcinomaJennifer A. CrozierBaptist MD Anderson Cancer CenterSubstudyNoAllMP and BP relation to clinical PR positivityJennifer A. CrozierBaptist MD Anderson Cancer CenterSubstudyNoAllMP and BP relation to clinical Ki67 scoreJennifer A. CrozierBaptist MD Anderson Cancer CenterSubstudyNoAllMP and BP in metaplastic breast cancer Citation Format: Jennifer A Crozier, Gordan Srkalovic, Adam Brufsky, Mehran Habibi, Pat Whitworth, Charles Cox, Ian Grady, Thomas Lomis, Nina D'Abreo, Erin B Yoder, Sarah Untch, Heather Kling, William Audeh, Bastiaan Van der Baan, FLEX Investigators Group. The FLEX real world data platform explores new gene expression profiles and investigator-initiated protocols in early stage breast cancer [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr OT3-17-02.

Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney tumor. Previous studies have shown that the interaction between tumor cells and microenvironment has an important impact on prognosis. Immune and stromal cells are two vital components of the tumor microenvironment. Our study aimed to better understand and explore the genes involved in immune/stromal cells on prognosis. We used the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data algorithm to calculate immune/stromal scores. According to the scores, we divided ccRCC patients from The Cancer Genome Atlas database into low and high groups and identified the genes which were differentially expressed and significantly associated with prognosis. The result of functional enrichment analysis and protein-protein interaction networks indicated that these genes mainly were involved in extracellular matrix and regulation of cellular activities. Then another independent cohort from the International Cancer Genome Consortium database was used to validate these genes. Finally, we acquired a list of microenvironment-related genes that can predict prognosis for ccRCC patients.

Causality inference is the use of computational techniques to predict possible causal relationships for a set of variables, thereby forming a directed network. Causality inference in Gene Regulatory Networks (GRNs) is an important, yet challenging task due to the limits of available data and lack of efficiency in existing causality inference techniques. A number of techniques have been proposed and applied to infer causal relationships in various domains, although they are not specific to regulatory network inference. In this paper, we assess the effectiveness of methods for inferring causal GRNs. We introduce seven different inference methods and apply them to infer directed edges in GRNs. We use time-series expression data from the DREAM challenges to assess the methods in terms of quality of inference and rank them based on performance. The best method is applied to Breast Cancer data to infer a causal network. Experimental results show that Causation Entropy is best, however, highly time-consuming and not feasible to use in a relatively large network. We infer Breast Cancer GRN with the second-best method, Transfer Entropy. The topological analysis of the network reveals that top out-degree genes such as SLC39A5 which are considered central genes, play important role in cancer progression.

With the advancements in machine and deep learning algorithms, the envision of various critical real-life applications in computer vision becomes possible. One of the applications is facial sentiment analysis. Deep learning has made facial expression recognition the most trending research fields in computer vision area. Recently, deep learning-based FER models have suffered from various technological issues like under-fitting or over-fitting. It is due to either insufficient training and expression data. Motivated from the above facts, this paper presents a systematic and comprehensive survey on current state-of-art Artificial Intelligence techniques (datasets and algorithms) that provide a solution to the aforementioned issues. It also presents a taxonomy of existing facial sentiment analysis strategies in brief. Then, this paper reviews the existing novel machine and deep learning networks proposed by researchers that are specifically designed for facial expression recognition based on static images and present their merits and demerits and summarized their approach. Finally, this paper also presents the open issues and research challenges for the design of a robust facial expression recognition system.