Annotation depth confounds direct comparison of gene expression across species

Background Comparisons of the molecular framework among organisms can be done on both structural and functional levels. One of the most common top-down approaches for functional comparisons is RNA sequencing. This estimation of organismal transcriptional responses is of interest for understanding evolution of molecular activity, which is used for answering a diversity of questions ranging from basic biology to pre-clinical species selection and translation. However, direct comparison between species is often hindered by evolutionary divergence in structure of molecular framework, as well as large difference in the depth of our understanding of the genetic background between humans and other species. Here, we focus on the latter. We attempt to understand how differences in transcriptome annotation affect direct gene abundance comparisons between species. Results We examine and suggest some straightforward approaches for direct comparison given the current available tools and using a sample dataset from human, cynomolgus monkey, dog, rat and mouse with a common quantitation and normalization approach. In addition, we examine how variation in genome annotation depth and quality across species may affect these direct comparisons. Conclusions Our findings suggest that further efforts for better genome annotation or computational normalization tools may be of strong interest.

DeltaNeTS+: elucidating the mechanism of drugs and diseases using gene expression and transcriptional regulatory networks

Background Knowledge on the molecular targets of diseases and drugs is crucial for elucidating disease pathogenesis and mechanism of action of drugs, and for driving drug discovery and treatment formulation. In this regard, high-throughput gene transcriptional profiling has become a leading technology, generating whole-genome data on the transcriptional alterations caused by diseases or drug compounds. However, identifying direct gene targets, especially in the background of indirect (downstream) effects, based on differential gene expressions is difficult due to the complexity of gene regulatory network governing the gene transcriptional processes. Results In this work, we developed a network analysis method, called DeltaNeTS+, for inferring direct gene targets of drugs and diseases from gene transcriptional profiles. DeltaNeTS+ uses a gene regulatory network model to identify direct perturbations to the transcription of genes using gene expression data. Importantly, DeltaNeTS+ is able to combine both steady-state and time-course expression profiles, as well as leverage information on the gene network structure. We demonstrated the power of DeltaNeTS+ in predicting gene targets using gene expression data in complex organisms, including Caenorhabditis elegans and human cell lines (T-cell and Calu-3). More specifically, in an application to time-course gene expression profiles of influenza A H1N1 (swine flu) and H5N1 (avian flu) infection, DeltaNeTS+ shed light on the key differences of dynamic cellular perturbations caused by the two influenza strains. Conclusion DeltaNeTS+ is a powerful network analysis tool for inferring gene targets from gene expression profiles. As demonstrated in the case studies, by incorporating available information on gene network structure, DeltaNeTS+ produces accurate predictions of direct gene targets from a small sample size (~ 10 s). Integrating static and dynamic expression data with transcriptional network structure extracted from genomic information, as enabled by DeltaNeTS+, is crucial toward personalized medicine, where treatments can be tailored to individual patients. DeltaNeTS+ can be freely downloaded from http://www.github.com/cabsel/deltanetsplus.

Influence of Recombinant Codon-Optimized Plasmid DNA Encoding VEGF and FGF2 on Co-Induction of Angiogenesis

Several methods for the stimulation of skin wound repair have been proposed over the last few decades. The most promising among them are gene and stem cell therapy. Our present experiments combined several approaches via the application of human umbilical cord blood mononuclear cells (hUCB-MC) that were transfected with pBud-VEGF165-FGF2 plasmid (gene-cell therapy) and direct gene therapy using pBud-VEGF165-FGF2 plasmid to enhance healing of full thickness skin wounds in rats. The dual expression cassette plasmid pBud-VEGF165-FGF2 encodes both VEGF and FGF2 therapeutic genes, expressing pro-angiogenic growth factors. Our results showed that, with two weeks post-transplantation, some transplanted cells still retained expression of the stem cell and hematopoietic markers C-kit and CD34. Other transplanted cells were found among keratinocytes, hair follicle cells, endothelial cells, and in the derma. PCNA expression studies revealed that transplantation of transfected cells terminated proliferative processes in regenerating wounds earlier than transplantation of untransfected cells. In the direct gene therapy group, four days post-operatively, the processes of flap revascularization, while using Easy LDI Microcirculation Camera, was higher than in control wounded skin. We concluded that hUCB-MC can be used for the treatment of skin wounds and transfection these cells with VEGF and FGF2 genes enhances their regenerative abilities. We also concluded that the application of pBud-VEGF165-FGF2 plasmids is efficient for the direct gene therapy of skin wounds by stimulation of wound revascularization.

Definition of a Small Core Transcriptional Circuit Regulated by AML1-ETO

Transcription factors regulate gene networks controlling normal hematopoiesis and are frequently deregulated in acute myeloid leukemia (AML). Critical to our understanding of the mechanism of cellular transformation by oncogenic transcription factors is the ability to define their direct gene targets. While this seems to be a straight forward task, gene network cascades can change within minutes to hours, making it difficult to distinguish direct from secondary or compensatory transcriptional changes by traditional methodologies. We describe an approach utilizing CRISPR-based genome editing to insert a degron tag into the endogenous AML1-ETO locus of Kasumi-1 cells, as well as overexpression of a degradable AML1-ETO protein in CD34+ human cord blood cells, which is a an AML1-ETO-dependent pre-leukemia model. Upon addition of a small molecule proteolysis targeting chimera (PROTAC), the AML1-ETO protein was rapidly degraded in both systems. Furthermore, by combining rapid degradation with nascent transcript analysis (PRO-seq), RNA-seq and Cut&Run, we have defined the core AML1-ETO regulatory network, which consists of fewer than 100 direct gene targets. The ability of AML1-ETO to regulate this relatively small gene pool is critical for maintaining cells in a self-renewing state, and AML1-ETO degradation set off a cascade of transcriptional events resulting in myeloid differentiation.

Optimized Genetic Testing for Polledness in Multiple Breeds of Cattle

Many breeds of modern cattle are naturally horned, and for sound husbandry management reasons the calves frequently undergo procedures to physically remove the horns by disbudding or dehorning. These procedures are however a welfare concern. Selective breeding for polledness – absence of horns – has been effective in some cattle breeds but not in others (Bos indicus genotypes) due in part to the complex genetics of horn phenotype. To address this problem different approaches to genetic testing which provide accurate early-in-life prediction of horn phenotype have been evaluated, initially using microsatellites (MSAT) and more recently single nucleotide polymorphism (SNP). A direct gene test is not effective given the genetic heterogeneity and large-sized sequence variants associated with polledness in different breeds. The current study investigated 39,943 animals of multiple breeds to assess the accuracy of available poll testing assays. While the standard SNP-based test was an improvement on the earlier MSAT haplotyping method, 1999 (9.69%) out of 20,636 animals tested with this SNP-based assay did not predict a genotype, most commonly associated with the Indicus-influenced breeds. The current study has developed an optimized poll gene test that resolved the vast majority of these 1999 unresolved animals, while the predicted genotypes of those previously resolved remained unchanged. Hence the optimized poll test successfully predicted a genotype in 99.96% of samples assessed. We demonstrated that a robust set of 5 SNPs can effectively determine PC and PF alleles and eliminate the ambiguous and undetermined results of poll gene testing previously identified as an issue in cattle.

DeltaNeTS+: Elucidating the mechanism of drugs and diseases using gene expression and transcriptional regulatory networks

BackgroundKnowledge on the molecular targets of diseases and drugs is crucial for elucidating disease pathogenesis and mechanism of action of drugs, and for driving drug discovery and treatment formulation. In this regard, high-throughput gene transcriptional profiling has become a leading technology, generating whole-genome data on the transcriptional alterations caused by diseases or drug compounds. However, identifying direct gene targets, especially in the background of indirect (downstream) effects, based on differential gene expressions is difficult due to the complexity of gene regulatory network governing the gene transcriptional processes.ResultsIn this work, we developed a network analysis method, called DeltaNeTS+, for inferring direct gene targets of drugs and diseases from gene transcriptional profiles. DeltaNeTS+ relies on a gene regulatory network model to identify direct perturbations to the transcription of genes. Importantly, DeltaNeTS+ is able to combine both steady-state and time-course gene expression profiles, as well as to leverage information on the gene network structure that is increasingly becoming available for a multitude of organisms, including human. We demonstrated the power of DeltaNeTS+ in predicting gene targets using gene expression data in complex organisms, including Caenorhabditis elegans and human cell lines (T-cell and Calu-3). More specifically, in an application to time-course gene expression profiles of influenza A H1N1 (swine flu) and H5N1 (avian flu) infection, DeltaNeTS+ shed light on the key differences of dynamic cellular perturbations caused by the two influenza strains.ConclusionDeltaNeTS+ is an enabling tool to infer gene transcriptional perturbations caused by diseases and drugs from gene transcriptional profiles. By incorporating available information on gene network structure, DeltaNeTS+ produces accurate predictions of direct gene targets from a small sample size (~10s). DeltaNeTS+ can freely downloaded from http://www.github.com/cabsel/deltanetsplus.

Induced RPB1 depletion reveals a direct gene-specific control of RNA Polymerase III function by RNA Polymerase II

SummaryIncreasing evidence suggests that tRNA levels are dynamically and specifically regulated in response to internal and external cues to modulate the cellular translational program. However, the molecular players and the mechanisms regulating the gene-specific expression of tRNAs are still unknown. Using an inducible auxin-degron system to rapidly deplete RPB1 (the largest subunit of RNA Pol II) in living cells, we identified Pol II as a direct gene-specific regulator of tRNA transcription. Our data suggest that Pol II transcription robustly interferes with Pol III function at specific tRNA genes. This activity was further found to be essential for MAF1-mediated repression of a large set of tRNA genes during serum starvation, indicating that repression of tRNA genes by Pol II is dynamically regulated. Hence, Pol II plays a direct and central role in the gene-specific regulation of tRNA expression.