Differential Gene Expression Studies Research Articles

Transcriptome- and DNA methylation-based cell-type deconvolutions produce similar estimates of differential gene expression and differential methylation

BackgroundChanging cell-type proportions can confound studies of differential gene expression or DNA methylation (DNAm) from peripheral blood mononuclear cells (PBMCs). We examined how cell-type proportions derived from the transcriptome versus the methylome (DNAm) influence estimates of differentially expressed genes (DEGs) and differentially methylated positions (DMPs).MethodsTranscriptome and DNAm data were obtained from PBMC RNA and DNA of Kenyan children (n = 8) before, during, and 6 weeks following uncomplicated malaria. DEGs and DMPs between time points were detected using cell-type adjusted modeling with Cibersortx or IDOL, respectively.ResultsMost major cell types and principal components had moderate to high correlation between the two deconvolution methods (r = 0.60–0.96). Estimates of cell-type proportions and DEGs or DMPs were largely unaffected by the method, with the greatest discrepancy in the estimation of neutrophils.ConclusionVariation in cell-type proportions is captured similarly by both transcriptomic and methylome deconvolution methods for most major cell types.

Integrated de novo transcriptome of Culex pipiens mosquito larvae as a resource for genetic control strategies

We present a de novo transcriptome of the mosquito vector Culex pipiens, assembled by sequences of susceptible and insecticide resistant larvae. The high quality of the assembly was confirmed by TransRate and BUSCO. A mapping percentage until 94.8% was obtained by aligning contigs to Nr, SwissProt, and TrEMBL, with 27,281 sequences that simultaneously mapped on the three databases. A total of 14,966 ORFs were also functionally annotated by using the eggNOG database. Among them, we identified ORF sequences of the main gene families involved in insecticide resistance. Therefore, this resource stands as a valuable reference for further studies of differential gene expression as well as to identify genes of interest for genetic-based control tools.

Chronic arsenic exposure induces malignant transformation of human HaCaT cells through both deterministic and stochastic changes in transcriptome expression

Biological processes are inherently stochastic, i.e., are partially driven by hard to predict random probabilistic processes. Carcinogenesis is driven both by stochastic and deterministic (predictable non-random) changes. However, very few studies systematically examine the contribution of stochastic events leading to cancer development. In differential gene expression studies, the established data analysis paradigms incentivize expression changes that are uniformly different across the experimental versus control groups, introducing preferential inclusion of deterministic changes at the expense of stochastic processes that might also play a crucial role in the process of carcinogenesis. In this study, we applied simple computational techniques to quantify: (i) The impact of chronic arsenic (iAs) exposure as well as passaging time on stochastic gene expression and (ii) Which genes were expressed deterministically and which were expressed stochastically at each of the three stages of cancer development. Using biological coefficient of variation as an empirical measure of stochasticity we demonstrate that chronic iAs exposure consistently suppressed passaging related stochastic gene expression at multiple time points tested, selecting for a homogenous cell population that undergo transformation. Employing multiple balanced removal of outlier data, we show that chronic iAs exposure induced deterministic and stochastic changes in the expression of unique set of genes, that populate largely unique biological pathways. Together, our data unequivocally demonstrate that both deterministic and stochastic changes in transcriptome-wide expression are critical in driving biological processes, pathways and networks towards clonal selection, carcinogenesis, and tumor heterogeneity.

Abstract A161: A differentiation based therapeutic approach for KMT2A rearranged leukemia in infants and children

Abstract Introduction: Translocations in the KMT2A gene (KMT2A-r) in leukemia is an aggressive oncodriver. Currently, few therapeutic options are available for these patients and novel therapeutic strategies are urgently needed to improve outcomes. Characteristically, leukemic cells undergo a differentiation block at an early stage of development and agents that restore terminal differentiation represent an effective therapeutic strategy. Based on the initial reports that the inhibition of the Menin-KMT2A fusion is a potent inducer of leukemic cell differentiation, we sought to study a novel treatment approach to enhance the current treatment outcomes in KMT2A-r leukemia. Methods: Pediatric leukemia cells with and without KMT2A-r were studied. Differentiation was induced by the Menin inhibitor MI-463 for 12 days, followed by assessment of differentiation on day 6 and day 12 by Giemsa-Wright staining and visualization using the Infinity analyzer software. In the next set of experiments, the differentiated and undifferentiated cells were treated with common anti-leukemic chemotherapeutic agents and the induction of apoptosis and target modulation analyses were carried out by Annexin/PI staining and immunoblotting. The differentiated cells were also treated with a panel of 220 FDA approved agents, targeting diverse oncogenic pathways to generate a sensitivity signature specific to the differentiated cells. Gene expression array studies were used to identify differentially expressed genes in the two cell populations. Results: The KMT2A-r leukemia cells showed increased dose dependent sensitivity to MI-463. The highest drug sensitivity was exhibited by the infant leukemia cell line MV4-11. At 1µM, no significant loss of cell viability was noted but the rate of proliferation was significantly decreased compared to DMSO control, providing optimal conditions for differentiation. Leukemic cells cultured with MI-463 showed characteristic morphological changes of differentiation including vacuolated cytoplasm, kidney shaped nuclei and decreased nuclei to cytoplasmic ratio. Importantly, we found that the differentiated cells showed a similar chemosensitivity pattern but the sensitivity to AraC was greatly reduced. For example, in MV4-11 cells, IC50 was 170 nM compared to 8 nM in undifferentiated cells. Directed drug screen and differential gene expression studies identified specific growth and survival pathways that can be positively targeted in differentiated cells. Discussion: Data presented demonstrate the activity of MI-463 to induce cell death as well as to initiate differentiation in KMT2A-r cells. We further characterize the sensitivity profile of current chemotherapeutic agents for multi-agent treatment regimens and identify novel agents to optimize future treatment protocols. Differential gene expression analyses were used to understand the potential mechanisms of therapeutic vulnerabilities in these cells. Our results provide the first preclinical evidence to select agents and combinations to improve the outcomes in KMT2A-r leukemia in children. Citation Format: Ritul Sharma, Aru Narendran. A differentiation based therapeutic approach for KMT2A rearranged leukemia in infants and children [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr A161.

The genus Arachis: an excellent resource for studies on differential gene expression for stress tolerance.

Peanut Arachis hypogaea is a segmental allotetraploid in the section Arachis of the genus Arachis along with the Section Rhizomataceae. Section Arachis has several diploid species along with Arachis hypogaea and A. monticola. The section Rhizomataceae comprises polyploid species. Several species in the genus are highly tolerant to biotic and abiotic stresses and provide excellent sets of genotypes for studies on differential gene expression. Though there were several studies in this direction, more studies are needed to identify more and more gene combinations. Next generation RNA-seq based differential gene expression study is a powerful tool to identify the genes and regulatory pathways involved in stress tolerance. Transcriptomic and proteomic study of peanut plants under biotic stresses reveals a number of differentially expressed genes such as R genes (NBS-LRR, LRR-RLK, protein kinases, MAP kinases), pathogenesis related proteins (PR1, PR2, PR5, PR10) and defense related genes (defensin, F-box, glutathione S-transferase) that are the most consistently expressed genes throughout the studies reported so far. In most of the studies on biotic stress induction, the differentially expressed genes involved in the process with enriched pathways showed plant-pathogen interactions, phenylpropanoid biosynthesis, defense and signal transduction. Differential gene expression studies in response to abiotic stresses, reported the most commonly expressed genes are transcription factors (MYB, WRKY, NAC, bZIP, bHLH, AP2/ERF), LEA proteins, chitinase, aquaporins, F-box, cytochrome p450 and ROS scavenging enzymes. These differentially expressed genes are in enriched pathways of transcription regulation, starch and sucrose metabolism, signal transduction and biosynthesis of unsaturated fatty acids. These identified differentially expressed genes provide a better understanding of the resistance/tolerance mechanism, and the genes for manipulating biotic and abiotic stress tolerance in peanut and other crop plants. There are a number of differentially expressed genes during biotic and abiotic stresses were successfully characterized in peanut or model plants (tobacco or Arabidopsis) by genetic manipulation to develop stress tolerance plants, which have been detailed out in this review and more concerted studies are needed to identify more and more gene/gene combinations.

Exploring the therapeutic potential of green tea phytocompounds for pancreatic cancer: An mRNA differential gene expression and pathway analysis study

Introduction and Aim: Pancreatic adenocarcinoma, which develops from the exocrine pancreas, is the most prevalent and aggressive type of pancreatic tumour having increased global prevalence, and has more than doubled over the 30 years. The aim of this study is to explore therapeutic potential of Camellia sinensis for pancreatic cancer using mRNA datasets and molecular docking technology. Materials and Methods: The purpose of the current study is to use computational methods to assess the effectiveness of several Camellia sinensis phytochemicals against Pancreatic cancer. The IMMPAT databank is utilized to retrieve the possible ligands. Molecular docking was methodically carried out using the virtual screening tool PyRx and the BIOVIA Discovery Studio Visualizer to forecast the binding affinity among the phytochemicals and the targeted proteins. Using ADMET filters, the ligands' pharmacological assessment was completed. Results: The fifty-two phytochemicals identified from Camellia sinensis were initially screened based on their affinity towards the targeted proteins. The ligand binding affinity score suggested that the phytochemical Vitamin E had the greater affinity towards the two targeted proteins and can be a promising therapeutic potential for the study of pancreatic cancer. Conclusion: The outcomes of this investigation suggested that the phytocompound Vitamin E, upon molecular docking exhibited the highest binding affinity which can be used as a drug candidate for pancreatic cancer.

The relationship between case-control differential gene expression from brain tissue and genetic associations in schizophrenia.

Large numbers of genetic loci have been identified that are known to contain common risk alleles for schizophrenia, but linking associated alleles to specific risk genes remains challenging. Given that most alleles that influence liability to schizophrenia are thought to do so by altered gene expression, intuitively, case-control differential gene expression studies should highlight genes with a higher probability of being associated with schizophrenia and could help identify the most likely causal genes within associated loci. Here, we test this hypothesis by comparing transcriptome analysis of the dorsolateral prefrontal cortex from 563 schizophrenia cases and 802 controls with genome-wide association study (GWAS) data from the third wave study of the Psychiatric Genomics Consortium. Genes differentially expressed in schizophrenia were not enriched for common allelic association statistics compared with other brain-expressed genes, nor were they enriched for genes within associated loci previously reported to be prioritized by genetic fine-mapping. Genes prioritized by Summary-based Mendelian Randomization were underexpressed in cases compared to other genes in the same GWAS loci. However, the overall strength and direction of expression change predicted by SMR were not related to that observed in the differential expression data. Overall, this study does not support the hypothesis that genes identified as differentially expressed from RNA sequencing of bulk brain tissue are enriched for those that show evidence for genetic associations. Such data have limited utility for prioritizing genes in currently associated loci in schizophrenia.

Allele-specific expression analysis for complex genetic phenotypes applied to a unique dilated cardiomyopathy cohort

Allele-specific expression (ASE) analysis detects the relative abundance of alleles at heterozygous loci as a proxy for cis-regulatory variation, which affects the personal transcriptome and proteome. This study describes the development and application of an ASE analysis pipeline on a unique cohort of 87 well phenotyped and RNA sequenced patients from the Maastricht Cardiomyopathy Registry with dilated cardiomyopathy (DCM), a complex genetic disorder with a remaining gap in explained heritability. Regulatory processes for which ASE is a proxy might explain this gap. We found an overrepresentation of known DCM-associated genes among the significant results across the cohort. In addition, we were able to find genes of interest that have not been associated with DCM through conventional methods such as genome-wide association or differential gene expression studies. The pipeline offers RNA sequencing data processing, individual and population level ASE analyses as well as group comparisons and several intuitive visualizations such as Manhattan plots and protein–protein interaction networks. With this pipeline, we found evidence supporting the case that cis-regulatory variation contributes to the phenotypic heterogeneity of DCM. Additionally, our results highlight that ASE analysis offers an additional layer to conventional genomic and transcriptomic analyses for candidate gene identification and biological insight.

Topological biclustering ARTMAP for identifying within bicluster relationships

Biclustering is a powerful tool for exploratory data analysis in domains such as social networking, data reduction, and differential gene expression studies. Topological learning identifies connected regions that are difficult to find using other traditional clustering methods and produces a graphical representation. Therefore, to improve the quality of biclustering and module extraction, this work combines the adaptive resonance theory (ART)-based methods of biclustering ARTMAP (BARTMAP) and topological ART (TopoART), to produce TopoBARTMAP. The latter inherits the ability to detect topological associations while performing data reduction. The capabilities of TopoBARTMAP were benchmarked using 35 real world cancer datasets and contrasted with other (bi)clustering methods, where it showed a statistically significant improvement over the other assessed methods on ordered and shuffled data experiments. In experiments with 12 synthetic datasets, the method was observed to perform better at identifying constant, scale, shift, and shift scale type biclusters. The produced graphical representation was refined to represent gene bicluster associations and was assessed on the NCBI GSE89116 dataset containing expression levels of 39,326 probes sampled over 38 observations.

Effect of an Eco-Friendly Cuminaldehyde Guanylhydrazone Disinfectant on Shiga Toxin Production and Global Transcription of Escherichia coli.

Antimicrobials have been important medicines used to treat various infections. However, some antibiotics increase the expression of Shiga toxin (Stx). Also, the pervasive use of persistent antibiotics has led to ecotoxicity and antibiotic resistance. In this study, a newly developed broad-spectrum and reversible antibiotic (guanylhydrazone disinfectant) was evaluated for its antibiotic activity and effects on Stx production and global transcription of bacteria. No Stx induction was observed in 25 Shiga toxin-producing E. coli (STEC) isolates treated with a sublethal concentration of the guanylhydrazone. A differential gene expression study comparing two guanylhydrazone-treated to non-treated E. coli strains indicated that the expression of a group of stress-responsive genes were enhanced. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that guanylhydrazone treatment significantly downregulated the pathways of ribosome and flagellar assembly in both pathogenic and non-pathogenic strains and differentially regulated some pathways essential for bacteria to maintain cell shape and gain survival advantage in two strains. In addition, upregulation of antibiotic resistant genes related to the multidrug efflux system and virulence genes coding for colibactin, colicin, and adhesin was observed in strains treated with the disinfectant. The knowledge obtained in this study contributes to our understanding of the mode of this disinfectant action and facilitates our effort to better use disinfectants for STEC treatments.

Prune homolog 2 with BCH domain (PRUNE2) gene expression is associated with feed efficiency-related traits in Nelore steers.

Animal feeding is a critical factor in increasing producer profitability. Improving feed efficiency can help reduce feeding costs and reduce the environmental impact of beef production. Candidate genes previously identified for this trait in differential gene expression studies (e.g., case-control studies) have not examined continuous gene-phenotype variation, which is a limitation. The aim of this study was to investigate the association between the expression of five candidate genes in the liver, measured by quantitative real-time PCR and feed-related traits. We adopted a linear mixed model to associate liver gene expression from 52 Nelore steers with the following production traits: average daily gain (ADG), body weight (BW), dry matter intake (DMI), feed conversion ratio (FCR), feed efficiency (FE), Kleiber index (KI), metabolic body weight (MBW), residual feed intake (RFI), and relative growth ratio (RGR). The total expression of the prune homolog 2 (PRUNE2) gene was significantly associated with DMI, FCR, FE, and RFI (P < 0.05). Furthermore, we have identified a new transcript of PRUNE2 (TCONS_00027692, GenBank MZ041267) that was inversely correlated with FCR and FE (P < 0.05), in contrast to the originally identified PRUNE2 transcript. The cytochrome P450 subfamily 2B (CYP2B6), early growth response protein 1 (EGR1), collagen type I alpha 1 chain (COL1A1), and connective tissue growth factor (CTGF) genes were not associated with any feed efficiency-related traits (P > 0.05). The findings reported herein suggest that PRUNE2 expression levels affects feed efficiency-related traits variation in Nelore steers.

A transcriptome software comparison for the analyses of treatments expected to give subtle gene expression responses

BackgroundIn this comparative study we evaluate the performance of four software tools: DNAstar-D (DESeq2), DNAstar-E (edgeR), CLC Genomics and Partek Flow for identification of differentially expressed genes (DEGs) using a transcriptome of E. coli. The RNA-seq data are from the effect of below-background radiation 5.5 nGy total dose (0.2nGy/hr) on E. coli grown shielded from natural radiation 655 m below ground in a pre-World War II steel vault. The gene expression response to three supplemented sources of radiation designed to mimic natural background, 1952 – 5720 nGy in total dose (71–208 nGy/hr), are compared to this “radiation-deprived” treatment. In addition, RNA-seq data of Caenorhabditis elegans nematode from similar radiation treatments was analyzed by three of the software packages.ResultsIn E. coli, the four software programs identified one of the supplementary sources of radiation (KCl) to evoke about 5 times more transcribed genes than the minus-radiation treatment (69–114 differentially expressed genes, DEGs), and so the rest of the analyses used this KCl vs “Minus” comparison. After imposing a 30-read minimum cutoff, one of the DNAStar options shared two of the three steps (mapping, normalization, and statistic) with Partek Flow (they both used median of ratios to normalize and the DESeq2 statistical package), and these two programs identified the highest number of DEGs in common with each other (53). In contrast, when the programs used different approaches in each of the three steps, between 31 and 40 DEGs were found in common. Regarding the extent of expression differences, three of the four programs gave high fold-change results (15–178 fold), but one (DNAstar’s DESeq2) resulted in more conservative fold-changes (1.5–3.5). In a parallel study comparing three qPCR commercial validation software programs, these programs also gave variable results as to which genes were significantly regulated. Similarly, the C. elegans analysis showed exaggerated fold-changes in CLC and DNAstar’s edgeR while DNAstar-D was more conservative.ConclusionsRegarding the extent of expression (fold-change), and considering the subtlety of the very low level radiation treatments, in E. coli three of the four programs gave what we consider exaggerated fold-change results (15 – 178 fold), but one (DNAstar’s DESeq2) gave more realistic fold-changes (1.5–3.5). When RT-qPCR validation comparisons to transcriptome results were carried out, they supported the more conservative DNAstar-D’s expression results. When another model organism’s (nematode) response to these radiation differences was similarly analyzed, DNAstar-D also resulted in the most conservative expression patterns. Therefore, we would propose DESeq2 (“DNAstar-D”) as an appropriate software tool for differential gene expression studies for treatments expected to give subtle transcriptome responses.

Abstract A039: Transcriptomic analysis of canine osteosarcoma from a precision medicine perspective reveals limitations of differential gene expression studies

Abstract Despite significant advances in cancer diagnosis and treatment, osteosarcoma (OSA), an aggressive primary bone tumor, has eluded attempts at improving patient survival for many decades. Spontaneously occurring OSA in the dog shares remarkably similar clinical, histological, and molecular characteristics and therefore serves as an excellent model for the disease. The difficulty in managing OSA lies in its extreme genetic complexity, drug-resistance, and heterogeneity, making it improbable that a single-target treatment would be beneficial for the majority of affected individuals. Precision medicine seeks to fill this gap by addressing the intra- and inter-tumoral heterogeneity to improve patient outcome and survival. Characterization of differentially expressed genes (DEGs) provides insight into the phenotype and can be useful for informing appropriate therapies as well as development of novel treatments. To identify relevant DEGs, RNA profiles of primary tumor should be contrasted with that of normal tissue derived from the same cell-of-origin as the tumor prior to chemotherapy, as convoluted drug-resistant pathways make interpretation difficult. Traditional DEG analysis combines patient data to derive statistically inferred genes that are dysregulated in the group; however, the results from this approach are not necessarily consistent across individual patients, thus contradicting the basis of precision medicine. In this preliminary study, we use transcriptomic sequencing of RNA isolated from primary canine OSA tumor and patient-matched normal bone from seven dogs prior to chemotherapy to identify DEGs in the group. We then evaluate the universality of these changes in transcript levels among patients by deriving individual-level fold-change values using strict filtering parameters. The results from this study can be useful for reframing our perspective of transcriptomic analysis from a precision medicine perspective by identifying variations in DEGs among individuals. Citation Format: Rebecca L. Nance, Sara Cooper, Dmytro Starenki, Xu Wang, Maninder Sandey, Jey Koehler, Payal Agarwal, Brad Matz, Stephanie Lindley, Annette Smith, Ashley Smith, Noelle Bergman, Bruce F. Smith. Transcriptomic analysis of canine osteosarcoma from a precision medicine perspective reveals limitations of differential gene expression studies [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr A039.

Establishment of a pure culture of immature Sertoli cells by PDGFRA staining and cell sorting.

Sertoli cells are key somatic cells in the testis that form seminiferous tubules and support spermatogenesis. The isolation of pure Sertoli cells is important for their study. However, it is a difficult effort because of the close association of Sertoli cells with peritubular myoid cells surrounding seminiferous tubules. Here, we propose a novel approach to the establishment of a pure Sertoli cell culture from immature mouse testes. It is based on the staining of testicular cells for platelet-derived growth factor receptor alpha (PDGFRA), followed by fluorescence-activated cell sorting and culturing of a PDGFRA-negative cell population. Cells positive for a Sertoli cell marker WT1 accounted for more than 96% of cells in cultures from 6 to 12 days postpartum (dpp) mice. The numbers of peritubular myoid cells identified by ACTA2 staining did not exceed 4%. Cells in the cultures were also positive for Sertoli cell proteins SOX9 and DMRT1. Amh and Hsd17b3 expression decreased and Ar and Gata1 expression increased in 12 dpp cultures compared to 6 dpp cultures, which suggests that cultured Sertoli cells at least partially retained their differentiation status. This method can be employed in various applications including the analysis of differential gene expression and functional studies.

Transcriptomic Analysis of Canine Osteosarcoma from a Precision Medicine Perspective Reveals Limitations of Differential Gene Expression Studies.

Despite significant advances in cancer diagnosis and treatment, osteosarcoma (OSA), an aggressive primary bone tumor, has eluded attempts at improving patient survival for many decades. The difficulty in managing OSA lies in its extreme genetic complexity, drug resistance, and heterogeneity, making it improbable that a single-target treatment would be beneficial for the majority of affected individuals. Precision medicine seeks to fill this gap by addressing the intra- and inter-tumoral heterogeneity to improve patient outcome and survival. The characterization of differentially expressed genes (DEGs) unique to the tumor provides insight into the phenotype and can be useful for informing appropriate therapies as well as the development of novel treatments. Traditional DEG analysis combines patient data to derive statistically inferred genes that are dysregulated in the group; however, the results from this approach are not necessarily consistent across individual patients, thus contradicting the basis of precision medicine. Spontaneously occurring OSA in the dog shares remarkably similar clinical, histological, and molecular characteristics to the human disease and therefore serves as an excellent model. In this study, we use transcriptomic sequencing of RNA isolated from primary OSA tumor and patient-matched normal bone from seven dogs prior to chemotherapy to identify DEGs in the group. We then evaluate the universality of these changes in transcript levels across patients to identify DEGs at the individual level. These results can be useful for reframing our perspective of transcriptomic analysis from a precision medicine perspective by identifying variations in DEGs among individuals.

Micro-RNA-Regulated SQUAMOSA-PROMOTER BINDING PROTEIN-LIKE (SPL) Gene Expression and Cytokinin Accumulation Distinguish Early-Developing Male and Female Inflorescences in Oil Palm (Elaeis guineensis).

Sexual differentiation of inflorescences and flowers is important for reproduction and affects crop plant productivity. We report here on a molecular study of the process of sexual differentiation in the immature inflorescence of oil palm (Elaeis guineensis). This species is monoecious and exhibits gender diphasy, producing male and female inflorescences separately on the same plant in alternation. Three main approaches were used: small RNA-seq to characterise and study the expression of miRNA genes; RNA-seq to monitor mRNA accumulation patterns; hormone quantification to assess the role of cytokinins and auxins in inflorescence differentiation. Our study allowed the characterisation of 30 previously unreported palm MIRNA genes. In differential gene and miRNA expression studies, we identified a number of key developmental genes and miRNA-mRNA target modules previously described in relation to their developmental regulatory role in the cereal panicle, notably the miR156/529/535-SQUAMOSA PROMOTER-BINDING PROTEIN-LIKE (SPL) gene regulatory module. Gene enrichment analysis highlighted the importance of hormone-related genes, and this observation was corroborated by the detection of much higher levels of cytokinins in the female inflorescence. Our data illustrate the importance of branching regulation within the developmental window studied, during which the female inflorescence, unlike its male counterpart, produces flower clusters on new successive axes by sympodial growth.

TransPi-a comprehensive TRanscriptome ANalysiS PIpeline for de novo transcriptome assembly.

The use of RNA sequencing (RNA-Seq) data and the generation of de novo transcriptome assemblies have been pivotal for studies in ecology and evolution. This is especially true for nonmodel organisms, where no genome information is available. In such organisms, studies of differential gene expression, DNA enrichment bait design and phylogenetics can all be accomplished with de novo transcriptome assemblies. Multiple tools are available for transcriptome assembly, but no single tool can provide the best assembly for all data sets. Therefore, a multi-assembler approach, followed by a reduction step, is often sought to generate an improved representation of the assembly. To reduce errors in these complex analyses while at the same time attaining reproducibility and scalability, automated workflows have been essential in the analysis of RNA-Seq data. However, most of these tools are designed for species where genome data are used as reference for the assembly process, limiting their use in nonmodel organisms. We present TransPi, a comprehensive pipeline for de novo transcriptome assembly, with minimum user input but without losing the ability of a thorough analysis. A combination of different model organisms, k-mer sets, read lengths and read quantities was used for assessing the tool. Furthermore, a total of 49 nonmodel organisms, spanning different phyla, were also analysed. Compared to approaches using single assemblers only, TransPi produces higher BUSCO completeness percentages, and a concurrent significant reduction in duplication rates. TransPi is easy to configure and can be deployed seamlessly using Conda, Docker and Singularity.

Genome‑wide identification of CAMTA gene family members in rice (Oryza sativa L.) and in silico study on their versatility in respect to gene expression and promoter structure

The calmodulin-binding transcription activator (CAMTA) is a family of transcriptional factors containing a cluster of calmodulin-binding proteins that can activate gene regulation in response to stresses. The presence of this family of genes has been reported earlier, though, the comprehensive analyses of rice CAMTA (OsCAMTA) genes, their promoter regions, and the proteins were not deliberated till date. The present report revealed the existence of seven CAMTA genes along with their alternate transcripts in five chromosomes of rice (Oryza sativa) genome. Phylogenetic trees classified seven CAMTA genes into three clades indicating the evolutionary conservation in gene structure and their association with other plant species. The in silico study was carried out considering 2 kilobases (kb) promoter regions of seven OsCAMTA genes regarding the distribution of transcription factor binding sites (TFbs) of major and plant-specific transcription factors whereas OsCAMTA7a was identified with highest number of TFbs, while OsCAMTA4 had the lowest. Comparative modelling, i.e., homology modelling, and molecular docking of the CAMTA proteins contributed the thoughtful comprehension of protein 3D structures and protein-protein interaction with probable partners. Gene ontology annotation identified the involvement of the proteins in biological processes, molecular functions, and localization in cellular components. Differential gene expression study gave an insight on functional multiplicity to showcase OsCAMTA3b as most upregulated stress-responsive gene. Summarization of the present findings can be interpreted that OsCAMTA gene duplication, variation in TFbs available in the promoters, and interactions of OsCAMTA proteins with their binding partners might be linked to tolerance against multiple biotic and abiotic cues.

A Study of Differential Gene Expression and Core Canonical Pathways Involved in Rhenium Ligand Treated Epithelial Mesenchymal Transition (EMT) Induced A549 Lung Cancer Cell Lines by INGENUITY Software System.

Rhenium compounds have shown anti-cancer properties against many different types of cancer cell lines; however, the cellular signaling mechanisms involved in the cytotoxic properties of rhenium-based compounds were never deciphered or reported. In this manuscript, we report the results of an investigation done by RNA sequencing of rhenium treated A549 lung cancer cell lines along with an untreated vehicular control, analyzed by the Ingenuity Pathway Analysis (IPA) software system to decipher the core canonical pathways involved in rhenium induced cancer cell death. A549 EMT lung cancer cell lines were treated with rhenium ligand (Tricarbonylperrhenato(bathocuproine)rhenium(I), PR7) for seven days along with vehicular control. RNA was isolated from the treated and control cells and sequenced by a commercial company (PrimBio Corporation). The RNA sequencing data was analyzed by the INGNUITY software system and the core canonical pathways involved with differential gene expression were identified. Our report is showing that there are several cellular pathways involved in inducing cell death by rhenium-based compound PR7.

Review on Applications of R programming in Biological Data Analysis

The key programming language used in the field of data science is R programming, because of the freeavailability and ability to handle huge data. Packages developed in the R are used widely by scientists toobtain solutions for various real-world problems in several disciplines like healthcare, agriculture, andinformation technology. Next-generation sequencing data requires various bioinformatics analyses likequality checking, differential gene expression studies, and annotation which is facilitated by R packages andsoftware. Bioconductorprojectremains a hub of R packages that are open access and move the researchers tothe comfort zone for various kinds of analysis. In this review, we briefly discussed various packages usefulfor NGS data analysis and also explained how to use the packages for basic level analysis that benefitsresearchers having less exposure to R programming.