FusionNW, a potential clinical impact assessment of kinases in pan-cancer fusion gene network.

Kinase fusion genes are the most active fusion gene group in human cancer fusion genes. To help choose the clinically significant kinase so that the cancer patients that have fusion genes can be better diagnosed, we need a metric to infer the assessment of kinases in pan-cancer fusion genes rather than relying on the sample frequency expressed fusion genes. Most of all, multiple studies assessed human kinases as the drug targets using multiple types of genomic and clinical information, but none used the kinase fusion genes in their study. The assessment studies of kinase without kinase fusion gene events can miss the effect of one of the mechanisms that enhance the kinase function in cancer. To fill this gap, in this study, we suggest a novel way of assessing genes using a network propagation approach to infer how likely individual kinases influence the kinase fusion gene network composed of ~5K kinase fusion gene pairs. To select a better seed of propagation, we chose the top genes via dimensionality reduction like a principal component or latent layer information of six features of individual genes in pan-cancer fusion genes. Our approach may provide a novel way to assess of human kinases in cancer.

FusionNeoAntigen: a resource of fusion gene-specific neoantigens.

Among the diverse sources of neoantigens (i.e. single-nucleotide variants (SNVs), insertions or deletions (Indels) and fusion genes), fusion gene-derived neoantigens are generally more immunogenic, have multiple targets per mutation and are more widely distributed across various cancer types. Therefore, fusion gene-derived neoantigens are a potential source of highly immunogenic neoantigens and hold great promise for cancer immunotherapy. However, the lack of fusion protein sequence resources and knowledge prevents this application. We introduce 'FusionNeoAntigen', a dedicated resource for fusion-specific neoantigens, accessible at https://compbio.uth.edu/FusionNeoAntigen. In this resource, we provide fusion gene breakpoint crossing neoantigens focused on ∼43K fusion proteins of ∼16K in-frame fusion genes from FusionGDB2.0. FusionNeoAntigen provides fusion gene information, corresponding fusion protein sequences, fusion breakpoint peptide sequences, fusion gene-derived neoantigen prediction, virtual screening between fusion breakpoint peptides having potential fusion neoantigens and human leucocyte antigens (HLAs), fusion breakpoint RNA/protein sequences for developing vaccines, information on samples with fusion-specific neoantigen, potential CAR-T targetable cell-surface fusion proteins and literature curation. FusionNeoAntigen will help to develop fusion gene-based immunotherapies. We will report all potential fusion-specific neoantigens from all possible open reading frames of ∼120K human fusion genes in future versions.

FusionPDB: a knowledgebase of human fusion proteins.

Tumorigenic functions due to the formation of fusion genes have been targeted for cancer therapeutics (i.e. kinase inhibitors). However, many fusion proteins involved in various cellular processes have not been studied for targeted therapeutics. This is because the lack of complete fusion protein sequences and their whole 3D structures has made it challenging to develop new therapeutic strategies. To fill these critical gaps, we developed a computational pipeline and a resource of human fusion proteins named FusionPDB, available at https://compbio.uth.edu/FusionPDB. FusionPDB is organized into four levels: 43K fusion protein sequences (14.7K in-frame fusion genes, Level 1), over 2300 + 1267 fusion protein 3D structures (from 2300 recurrent and 266 manually curated in-frame fusion genes, Level 2), pLDDT score analysis for the 1267 fusion proteins from 266 manually curated fusion genes (Level 3), and virtual screening outcomes for 68 selected fusion proteins from 266 manually curated fusion genes (Level 4). FusionPDB is the only resource providing whole 3D structures of fusion proteins and comprehensive knowledge of human fusion proteins. It will be regularly updated until it covers all human fusion proteins in the future.

Systematic investigation of the homology sequences around the human fusion gene breakpoints in pan-cancer - bioinformatics study for a potential link to MMEJ.

Microhomology-mediated end joining (MMEJ), an error-prone DNA damage repair mechanism, frequently leads to chromosomal rearrangements due to its ability to engage in promiscuous end joining of genomic instability and also leads to increasing mutational load at the sequences flanking the breakpoints (BPs). In this study, we systematically investigated the homology sequences around the genomic breakpoint area of human fusion genes, which were formed by the chromosomal rearrangements initiated by DNA double-strand breakage. Since the RNA-seq data is the typical data set to check the fusion genes, for the known exon junction fusion breakpoints identified from RNA-seq data, we have to infer the high chance of genomic breakpoint regions. For this, we utilized the high feature importance score area calculated from our recently developed fusion BP prediction model, FusionAI and identified 151 K microhomologies among ~24 K fusion BPs in 20 K fusion genes. From our multiple bioinformatics studies, we found a relationship between sequence homologies and the immune system. This in-silico study will provide novel knowledge on the sequence homologies around the coded structural variants.

Computational design of DNA binding domain-retained fusion proteins and virtual screening against FDA-approved drugs.

Even though the transcription factors (TFs) are not regarded as good drug targets, mutated or dysregulated TFs can be a unique class of drug targets. Specifically, the TF fusion protein, which is the translated structural variants including TFs may affect downstream to promote tumorigenesis. To date, we lack the fusion protein sequence information and 3D structure information in identifying the potential drugs of fusion proteins. In this study, we predicted the 3D structures of 732 transcription factor fusion proteins (TFFPs). For the top five most frequent TFFPs, we performed the virtual screening across the FDA-approved drugs. Our study will provide an initial platform to develop novel therapeutic targets in the transcription factor fusion proteins.

FusionAI, a DNA-sequence-based deep learning protocol reduces the false positives of human fusion gene prediction.

Even though there were many tool developments of fusion gene prediction from NGS data, too many false positives are still an issue. Wise use of the genomic features around the fusion gene breakpoints will be helpful to identify reliable fusion genes efficiently. For this aim, we developed FusionAI, a deep learning pipeline predicting human fusion gene breakpoints from DNA sequence. FusionAI is freely available via https://compbio.uth.edu/FusionGDB2/FusionAI. For complete details on the use and execution of this protocol, please refer to Kim et al. (2021b).

Prediction of Hanwoo Cattle Phenotypes from Genotypes Using Machine Learning Methods.

Hanwoo was originally raised for draft purposes, but the increase in local demand for red meat turned that purpose into full-scale meat-type cattle rearing; it is now considered one of the most economically important species and a vital food source for Koreans. The application of genomic selection in Hanwoo breeding programs in recent years was expected to lead to higher genetic progress. However, better statistical methods that can improve the genomic prediction accuracy are required. Hence, this study aimed to compare the predictive performance of three machine learning methods, namely, random forest (RF), extreme gradient boosting method (XGB), and support vector machine (SVM), when predicting the carcass weight (CWT), marbling score (MS), backfat thickness (BFT) and eye muscle area (EMA). Phenotypic and genotypic data (53,866 SNPs) from 7324 commercial Hanwoo cattle that were slaughtered at the age of around 30 months were used. The results showed that the boosting method XGB showed the highest predictive correlation for CWT and MS, followed by GBLUP, SVM, and RF. Meanwhile, the best predictive correlation for BFT and EMA was delivered by GBLUP, followed by SVM, RF, and XGB. Although XGB presented the highest predictive correlations for some traits, we did not find an advantage of XGB or any machine learning methods over GBLUP according to the mean squared error of prediction. Thus, we still recommend the use of GBLUP in the prediction of genomic breeding values for carcass traits in Hanwoo cattle.

RNA seq analyses of chicken reveals biological pathways involved in acclimation into different geographical locations.

Transcriptome expression reflects genetic response in diverse conditions. In this study, RNA sequencing was utilized to profile multiple tissues such as liver, breast, caecum, and gizzard of Korean commercial chicken raised in Korea and Kyrgyzstan. We analyzed ten samples per tissue from each location to identify candidate genes which are involved in the adaptation of Korean commercial chicken to Kyrgyzstan. At false discovery rate (FDR) < 0.05 and fold change (FC) > 2, we found 315, 196, 167 and 198 genes in liver, breast, cecum, and gizzard respectively as differentially expressed between the two locations. GO enrichment analysis showed that these genes were highly enriched for cellular and metabolic processes, catalytic activity, and biological regulations. Similarly, KEGG pathways analysis indicated metabolic, PPAR signaling, FoxO, glycolysis/gluconeogenesis, biosynthesis, MAPK signaling, CAMs, citrate cycles pathways were differentially enriched. Enriched genes like TSKU, VTG1, SGK, CDK2 etc. in these pathways might be involved in acclimation of organisms into diverse climatic conditions. The qRT-PCR result also corroborated the RNA-Seq findings with R2 of 0.76, 0.80, 0.81, and 0.93 for liver, breast, caecum, and gizzard respectively. Our findings can improve the understanding of environmental acclimation process in chicken.

Compositional and Functional Characteristics of Swine Slurry Microbes through 16S rRNA Metagenomic Sequencing Approach.

Traditionally slurry is used as source of nitrogen, phosphorous, and potassium in bio fertilizers to improve crop production. However, poorly managed slurry causes a hazardous effect to the environment by producing greenhouse gases, causing the eutrophication of water bodies, and polluting the groundwater. It has been largely reported that the microbial presence in slurry causing a diverse effect on its storage and disposal system. However, the diversity of bacterial populations in pig slurries remains largely unexplored. Here we report the bacterial diversity present in the slurry from slurry pits, and the effect of storage time on bacterial population. We collected 42 samples from three different pig slurry pits, as three replicates from each one until the 14th week. We used the 16S rRNA, Quantitative Insights Into Microbial Ecology (QIIME) and Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) protocols for the metagenomic downstream analysis. Taxonomic annotation using the Greengenes metagenomic database indicated that on an average 76.2% Firmicutes, 14.4% Bacteroidetes, 4.9% Proteobacteria, etc. microbial populations were present. Comparative microbial analysis showed that the population of Firmicutes decreased from the first to the 14th week, whereas the population of Bacteroidetes increased from the first to the 14th week. Through principal coordinate analysis (PCoA), (linear discriminant analysis effect size (LEfSe), and Pearson's correlation analysis, we found microbial biomarkers according to the storage time point. All bacterial populations were well clustered according to the early, middle, and last weeks of storage. LEfSe showed that Actinobacteria, Lachnospiraceae, Ruminococcaceae, and Bacteroidia are dominantly present in first, seventh, ninth, and 14th week, respectively. Lachnospiraceae and Ruminococcaceae are ubiquitous gastrointestinal non-pathogenic bacteria. KEGG pathways, such as membrane transport, carbohydrate and amino acid metabolism, genetic replication and repair, were significant among all samples. Such a KEGG pathway may indicate the association between the host organism's metabolic activity and the microbes present in the gastro intestinal tract (GIT).

Genome-Wide Transcriptome and Metabolome Analyses Provide Novel Insights and Suggest a Sex-Specific Response to Heat Stress in Pigs.

Heat stress (HS) negatively impacts pig production and swine health. Therefore, to understand the genetic and metabolic responses of pigs to HS, we used RNA-Seq and high resolution magic angle spinning (HR-MAS) NMR analyses to compare the transcriptomes and metabolomes of Duroc pigs (n = 6, 3 barrows and 3 gilts) exposed to heat stress (33 °C and 60% RH) with a control group (25 °C and 60% RH). HS resulted in the differential expression of 552 (236 up, 316 down) and 879 (540 up, 339 down) genes and significant enrichment of 30 and 31 plasma metabolites in female and male pigs, respectively. Apoptosis, response to heat, Toll-like receptor signaling and oxidative stress were enriched among the up-regulated genes, while negative regulation of the immune response, ATP synthesis and the ribosomal pathway were enriched among down-regulated genes. Twelve and ten metabolic pathways were found to be enriched (among them, four metabolic pathways, including arginine and proline metabolism, and three metabolic pathways, including pantothenate and CoA biosynthesis), overlapping between the transcriptome and metabolome analyses in the female and male group respectively. The limited overlap between pathways enriched with differentially expressed genes and enriched plasma metabolites between the sexes suggests a sex-specific response to HS in pigs.

Corrigendum: Cardiac and Skeletal Muscle Transcriptome Response to Heat Stress in Kenyan Chicken Ecotypes Adapted to Low and High Altitudes Reveal Differences in Thermal Tolerance and Stress Response.

[This corrects the article DOI: 10.3389/fgene.2019.00993.].

Whole metagenome sequencing of cecum microbiomes in Ethiopian indigenous chickens from two different altitudes reveals antibiotic resistance genes.

We analyzed the whole genomes of cecum microbiomes of Ethiopian indigenous chickens from two distinct geographical zones: Afar (AF) district (Dulecha, 730 m above sea level) and Amhara (AM) district (Menz Gera Midir, 3300 m). Through metagenomic analysis we found that microbial populations were mainly dominated by Bacteroidetes and Firmicutes. We identified 2210 common genes in the two groups. LEfSe showed that the distribution of Coprobacter, Geobacter, Cronobacter, Alloprevotella, and Dysgonomonas were more abundant in AF than AM. Analyses using KEGG, eggNOG, and CAZy databases indicated that the pathways of metabolism, genetic information processing, environmental information processing, and cellular process were significantly enriched. Functional abundance was found to be associated with the nutrient absorption and microbial localization of indigenous chickens. We also investigated antibiotic resistant genes and found antibiotics like LSM, cephalosporin, and tetracycline were significantly more abundant in AF than AM.

Transcriptome of Chicken Liver Tissues Reveals the Candidate Genes and Pathways Responsible for Adaptation into Two Different Climatic Conditions.

RNA sequencing was used to profile the liver transcriptome of a Korean commercial chicken (Hanhyup) at two different environments (Korea and Kyrgyzstan) to investigate their role during acclimatization into different climatic conditions. Ten samples from each location were analyzed to identify candidate genes that respond to environmental changes such as altitude, humidity, temperature, etc. Sequencing reads were preprocessed, aligned with the reference genome, assembled and expressions were estimated through bioinformatics approaches. At a false discovery rate (FDR) <0.05 and fold change (FC) ≥2, we found 315 genes were DE. Out of 315 DE genes, 174 and 141 were up- and down-regulated respectively in the Kyrgyz environment. Gene ontology (GO) enrichment analysis showed that the differentially expressed genes (DEGs) were associated with energy metabolism such as pyruvate and lactate metabolic processes, and glycerol catabolic process. Similarly, KEGG pathway analysis indicated pyruvate metabolism, glycolysis/gluconeogenesis, biosynthesis, citrate cycles were differentially enriched in the Kyrgyz environment. DEGs like TSKU, VTG1, SGK, CDK2, etc. in such pathways are highly involved in the adaptation of organisms into diverse climatic conditions. Our investigation may serve as a resource for the chicken industry, especially in exporting Hanhyup chicken from Korea to other countries.

Cardiac and Skeletal Muscle Transcriptome Response to Heat Stress in Kenyan Chicken Ecotypes Adapted to Low and High Altitudes Reveal Differences in Thermal Tolerance and Stress Response.

Heat stress (HS) negatively affects chicken performance. Agricultural expansion will happen in regions that experience high ambient temperatures, where fast-growing commercial chickens are vulnerable. Indigenous chickens of such regions, due to generations of exposure to environmental challenges, might have higher thermal tolerance. In this study, two indigenous chicken ecotypes, from the hot and humid Mombasa (lowland) and the colder Naivasha (highland) regions, were used to investigate the effects of acute (5 h, 35°C) and chronic (3 days of 35°C for 8 h/day) HS on the cardiac and skeletal muscle, through RNA sequencing. The rectal temperature gain and the number of differentially expressed genes (DEGs) [False Discovery Rate (FDR) < 0.05] were two times higher in the acute stage than in the chronic stage in both ecotypes, suggesting that cyclic exposure to HS can lead to adaptation. A tissue- and stage-specific difference in response to HS was observed, with peroxisome proliferator-activated-receptor (PPAR) signaling and mitogen-activate protein kinase (MAPK) signaling pathways, enriched in heart and skeletal muscle, respectively, and the p53 pathway enriched only in the acute stage in both tissues. The acute and chronic stage DEGs were integrated by a region-specific gene coexpression network (GCN), and genes with the highest number of connections (hub genes) were identified. The hub genes in the lowland network were CCNB2, Crb2, CHST9, SESN1, and NR4A3, while COMMD4, TTC32, H1F0, ACYP1, and RPS28 were the hub genes in the highland network. Pathway analysis of genes in the GCN showed that p53 and PPAR signaling pathways were enriched in both low and highland networks, while MAPK signaling and protein processing in endoplasmic reticulum were enriched only in the gene network of highland chickens. This shows that to dissipate the accumulated heat, to reduce heat induced apoptosis, and to promote DNA damage repair, the ecotypes activated or suppressed different genes, indicating the differences in thermal tolerance and HS response mechanisms between the ecotypes. This study provides information on the HS response of chickens, adapted to two different agro climatic environments, extending our understanding of the mechanisms of HS response and the effect of adaptation in counteracting HS.

Comparison of Bacterial Populations in the Ceca of Swine at Two Different Stages and their Functional Annotations.

The microbial composition in the cecum of pig influences host health, immunity, nutrient digestion, and feeding requirements significantly. Advancements in metagenome sequencing technologies such as 16S rRNAs have made it possible to explore cecum microbial population. In this study, we performed a comparative analysis of cecum microbiota of crossbred Korean native pigs at two different growth stages (stage L = 10 weeks, and stage LD = 26 weeks) using 16S rRNA sequencing technology. Our results revealed remarkable differences in microbial composition, α and ß diversity, and differential abundance between the two stages. Phylum composition analysis with respect to SILVA132 database showed Firmicutes to be present at 51.87% and 48.76% in stages L and LD, respectively. Similarly, Bacteroidetes were present at 37.28% and 45.98% in L and LD, respectively. The genera Prevotella, Anaerovibrio, Succinivibrio, Megasphaera were differentially enriched in stage L, whereas Clostridium, Terrisporobacter, Rikenellaceae were enriched in stage LD. Functional annotation of microbiome by level-three KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis revealed that glycine, serine, threonine, valine, leucine, isoleucine arginine, proline, and tryptophan metabolism were differentially enriched in stage L, whereas alanine, aspartate, glutamate, cysteine, methionine, phenylalanine, tyrosine, and tryptophan biosynthesis metabolism were differentially enriched in stage LD. Through machine-learning approaches such as LEfSe (linear discriminant analysis effect size), random forest, and Pearson's correlation, we found pathways such as amino acid metabolism, transport systems, and genetic regulation of metabolism are commonly enriched in both stages. Our findings suggest that the bacterial compositions in cecum content of pigs are heavily involved in their nutrient digestion process. This study may help to meet the demand of human food and can play significant roles in medicinal application.

Transcriptome analysis to identify long non coding RNA (lncRNA) and characterize their functional role in back fat tissue of pig.

Long non coding RNAs (lncRNA) have been previously found to be involved in important cellular activities like epigenetics, implantation, cell growth etc. in pigs. However, comprehensive analysis of lncRNA in back fat tissues at different developmental stages in pigs is still lacking. In this study we conducted transcriptome analysis in the back fat tissue of a F1 crossbred Korean Native Pig (KNP) × Yorkshire Pig to identify lncRNA. We investigated their role in 16 pigs at two different growth stages; stage 1 (10 weeks, n = 8) and stage 2 (26 weeks, n = 8). After quality assessment of sequencing reads, we got a total of 1,641,165 assembled transcripts out of eight paired end read from each stage. Among them, 6808 lncRNA transcripts were identified by filtering on the basis of multiple parameters like read length ≥ 200 nucleotides, exon numbers ≥2, FPKM ≥0.5, coding potential score < 0 etc. PFAM and RFAM were used to filter out all possible protein coding genes and housekeeping RNAs respectively. A total of 103 lncRNAs and 1057 mRNAs were found to be differentially expressed (DE) between the two stages (|log2FC| > 2, q < 0.05). We also identified 306 genes located around 100 kb upstream and 234 genes downstream around these DE lncRNA transcripts. The expression of top eleven DE lncRNAs (COL4A6, LY7S, MYH2, OXCT1, SMPDL3A, TMEM182, TTC36, RFOOOO4, RFOOO15, RFOOO45, CADM2) had been validating by qRT-PCR. Pathway and GO terms analysis showed that, positive regulation of biosynthetic process, Wnt signaling pathway, cellular protein modification process, and positive regulation of nitrogen compound were differentially enriched. Our results suggested that, KEGG pathways such as protein digestion and absorption, Arrhythmogenic right ventricular cardiomyopathy (ARVC) to be significantly enriched in both DE lncRNAs as well as DE mRNAs and involved in back fat tissues development. It also suggests that, identified lncRNAs are involved in regulation of important adipose tissues development pathways.

Identification of Novel Abiotic Stress Proteins in Triticum aestivum Through Functional Annotation of Hypothetical Proteins.

Cereal grain bread wheat (T. aestivum) is an important source of food and belongs to Poaceae family. Hypothetical proteins (HPs), i.e., proteins with unknown functions, share a substantial portion of wheat proteomes and play important roles in growth and physiology of plant system. Several functional annotations studies utilizing the protein sequences for characterization of role of individual protein in physiology of plant systems were being reported in recent past. In this study, an integrated pipeline of software/servers has been used for the identification and functional annotation of 124 unique HPs of T. aestivum considering available data in NCBI till date. All HPs were broadly annotated, out of which functions of 77 HPs were successfully assigned with high confidence level. Precisely functional annotation of remaining 47 HPs is also characterized with low confidence. Several latest versions of protein family databases, pathways information, genomics context methods and in silico tools were utilized to identify and assign function for individual HPs. Annotation result of several HPs mainly belongs to cellular protein, metabolic enzymes, binding proteins, transmembrane proteins, transcription factors and photosystem regulator proteins. Subsequently, functional analysis has revealed the role of few HPs in abiotic stress, which were further verified by phylogenetic analysis. The functionally associated proteins with each of above-mentioned abiotic stress-related proteins were identified through protein-protein interaction network analysis. The outcome of this study may be helpful for formulating general set pipeline/protocols for a better understanding of the role of HPs in physiological development of various plant systems.

Genome-wide analysis of miRNAs and Tasi-RNAs in Zea mays in response to phosphate deficiency.

Globally important cereal crop maize provides important nutritions and starch in dietary foods. Low phosphate (LPi) availability in the soil frequently limits the maize quality and yield across the world. Small non-coding RNAs (Snc-RNAs) play crucial roles in growth and adaptation of plants to the environment. Snc-RNAs like microRNAs (miRs) and trans-acting small interfering RNAs (Tasi-Rs) play important functions in posttranscriptional regulation of gene expression, which controls plant development, reproduction, and biotic/abiotic stress responses. In order to identify the miR and Tasi-R alterations in leaf and root of maize in response to sufficient phosphate and LPi at 3LS and 4LS, the snc-RNA population libraries for 0th, 1st, 2nd, 4th, and 8th day were constructed. These libraries were used for genome-wide alignment and RNA-fold analysis for possible prediction of potential miRs and Tasi-Rs. This study reported 174 known and conserved differentially expressed miRs of 27 miR families of maize plant. In addition, leaf and root specific potential novel miRs representing 155 new families were also discovered. Differentially expressed conserved as well as novel miR functions in root and leaf during early stage of Pi starvation were extensively discussed. Leaf and root specific miRs as well as common miRs with their target genes, participating in different biological, cellular, and metabolic processes were explored. Further, four miR390-directed Tasi-Rs which belong to TAS3 gene family along with other orthologs of Tasi-Rs were also identified. Finally, the study provides an insight into the composite regulatory mechanism of miRs in maize in response to Pi deficiency.

Molecular docking and dynamics simulation study of flavonoids as BET bromodomain inhibitors.

Bromodomains (BRDs) are the epigenetic proteins responsible for transcriptional regulation through its interaction with methylated or acetylated histone residues. The lysine residues of Bromodomain-1 (BD1) of Brd4 undergo ε-N-Acetylation posttranslational modifications to control transcription of genes. Due to its role in diverse cellular functions, Brd4 of bromodomain family, was considered as a prominent target for many diseases such as cancer, obesity, kidney disease, lung fibrosis, inflammatory diseases, etc. In this study, an attempt has been made to screen compounds from flavonoids and extended flavonoids libraries targeting acetylated lysine (KAc) binding site of BD1 of Brd4 using docking and molecular dynamics simulations. Two different docking programs AutoDock and Glide were used to compare their suitability for the receptor. Interestingly, in both the docking programs, the screened flavonoids have occupied the same binding pocket confirming the selection of active site. Further the MMGBSA binding free energy calculations and ADME analysis were carried out on screened compounds to establish their anti-cancerous properties. We have identified a flavonoid which shows docking and Glide e-model score comparatively much higher than those of already reported known inhibitors against Brd4. The protein-ligand complex with top-ranked flavonoid was used for dynamics simulation study for 50 ns in order to validate its stability inside the active site of Brd4 receptor. The results provide valuable information for structure-based drug design of Brd4 inhibitors.

Effect of STAT3 inhibitor in chronic myeloid leukemia associated signaling pathway: a mathematical modeling, simulation and systems biology study.

Chronic myeloid leukemia (CML) is a hematopoietic stem-cell disorder which proliferates due to abnormal growth of basophil cells. Several proangiogenic molecules have been reported to be associated in CML progression, including the hepatocyte growth factor (HGF). However, detail mechanism about the cellular distribution and function of HGF in CML is yet to be revealed. The proliferation of hematopoietic cells are regulated by some of the growth factors like interleukin 3 (IL-3), IL-6, erythropoietin, thrombopoietin, etc. In this study IL-6 pathways have been taken into consideration which induces JAK/STAT and MAPK pathways to decipher the CML progression stages. An attempt has been made to model these pathways with the help of ordinary differential equations (ODEs) and estimating unknown parameters through fminsearch optimization algorithm. Some of the specific component like STAT3, of the pathway has been analyzed in detail and their role in CML progression has been elucidated. The roles of STAT3 inhibitors into the treatment of CML have been thoroughly studied and optimum concentration of the inhibitors have been predicted.