Cell type identification in spatial transcriptomics data can be improved by leveraging cell-type-informative paired tissue images using a Bayesian probabilistic model.

Spatial transcriptomics technologies have recently emerged as a powerful tool for measuring spatially resolved gene expression directly in tissues sections, revealing cell types and their dysfunction in unprecedented detail. However, spatial transcriptomics technologies are limited in their ability to separate transcriptionally similar cell types and can suffer further difficulties identifying cell types in slide regions where transcript capture is low. Here, we describe a conceptually novel methodology that can computationally integrate spatial transcriptomics data with cell-type-informative paired tissue images, obtained from, for example, the reverse side of the same tissue section, to improve inferences of tissue cell type composition in spatial transcriptomics data. The underlying statistical approach is generalizable to any spatial transcriptomics protocol where informative paired tissue images can be obtained. We demonstrate a use case leveraging cell-type-specific immunofluorescence markers obtained on mouse brain tissue sections and a use case for leveraging the output of AI annotated H&E tissue images, which we used to markedly improve the identification of clinically relevant immune cell infiltration in breast cancer tissue. Thus, combining spatial transcriptomics data with paired tissue images has the potential to improve the identification of cell types and hence to improve the applications of spatial transcriptomics that rely on accurate cell type identification.

The chemotherapeutic CX-5461 primarily targets TOP2B and exhibits selective activity in high-risk neuroblastoma.

Survival in high-risk pediatric neuroblastoma has remained around 50% for the last 20 years, with immunotherapies and targeted therapies having had minimal impact. Here, we identify the small molecule CX-5461 as selectively cytotoxic to high-risk neuroblastoma and synergistic with low picomolar concentrations of topoisomerase I inhibitors in improving survival in vivo in orthotopic patient-derived xenograft neuroblastoma mouse models. CX-5461 recently progressed through phase I clinical trial as a first-in-human inhibitor of RNA-POL I. However, we also use a comprehensive panel of in vitro and in vivo assays to demonstrate that CX-5461 has been mischaracterized and that its primary target at pharmacologically relevant concentrations, is in fact topoisomerase II beta (TOP2B), not RNA-POL I. This is important because existing clinically approved chemotherapeutics have well-documented off-target interactions with TOP2B, which have previously been shown to cause both therapy-induced leukemia and cardiotoxicity-often-fatal adverse events, which can emerge several years after treatment. Thus, while we show that combination therapies involving CX-5461 have promising anti-tumor activity in vivo in neuroblastoma, our identification of TOP2B as the primary target of CX-5461 indicates unexpected safety concerns that should be examined in ongoing phase II clinical trials in adult patients before pursuing clinical studies in children.

Proteomic Analysis of MYB-Regulated Secretome Identifies Functional Pathways and Biomarkers: Potential Pathobiological and Clinical Implications.

Earlier we have shown important roles of MYB in pancreatic tumor pathobiology. To better understand the role of MYB in the tumor microenvironment and identify MYB-associated secreted biomarker proteins, we conducted mass spectrometry analysis of the secretome from MYB-modulated and control pancreatic cancer cell lines. We also performed in silico analyses to determine MYB-associated biofunctions, gene networks, and altered biological pathways. Our data demonstrated significant modulation (p < 0.05) of 337 secreted proteins in MYB-silenced MiaPaCa cells, whereas 282 proteins were differentially present in MYB-overexpressing BxPC3 cells, compared to their respective control cells. Alteration of several phenotypes such as cellular movement, cell death and survival, inflammatory response, protein synthesis, etc. was associated with MYB-induced differentially expressed proteins (DEPs) in secretomes. DEPs from MYB-silenced MiaPaCa PC cells were suggestive of the downregulation of genes primarily associated with glucose metabolism, PI3K/AKT signaling, and oxidative stress response, among others. DEPs from MYB-overexpressing BxPC3 cells suggested the enhanced release of proteins associated with glucose metabolism and cellular motility. We also observed that MYB positively regulated the expression of four proteins with potential biomarker properties, i.e., FLNB, ENO1, ITGB1, and INHBA. Mining of publicly available databases using Oncomine and UALCAN demonstrated that these genes are overexpressed in pancreatic tumors and associated with reduced patient survival. Altogether, these data provide novel avenues for future investigations on diverse biological functions of MYB, specifically in the tumor microenvironment, and could also be exploited for biomarker development.

Bayesian model selection for the Drosophila gap gene network.

BACKGROUND: The gap gene system controls the early cascade of the segmentation pathway in Drosophila melanogaster as well as other insects. Owing to its tractability and key role in embryo patterning, this system has been the focus for both computational modelers and experimentalists. The gap gene expression dynamics can be considered strictly as a one-dimensional process and modeled as a system of reaction-diffusion equations. While substantial progress has been made in modeling this phenomenon, there still remains a deficit of approaches to evaluate competing hypotheses. Most of the model development has happened in isolation and there has been little attempt to compare candidate models. RESULTS: The Bayesian framework offers a means of doing formal model evaluation. Here, we demonstrate how this framework can be used to compare different models of gene expression. We focus on the Papatsenko-Levine formalism, which exploits a fractional occupancy based approach to incorporate activation of the gap genes by the maternal genes and cross-regulation by the gap genes themselves. The Bayesian approach provides insight about relationship between system parameters. In the regulatory pathway of segmentation, the parameters for number of binding sites and binding affinity have a negative correlation. The model selection analysis supports a stronger binding affinity for Bicoid compared to other regulatory edges, as shown by a larger posterior mean. The procedure doesn't show support for activation of Kruppel by Bicoid. CONCLUSIONS: We provide an efficient solver for the general representation of the Papatsenko-Levine model. We also demonstrate the utility of Bayes factor for evaluating candidate models for spatial pattering models. In addition, by using the parallel tempering sampler, the convergence of Markov chains can be remarkably improved and robust estimates of Bayes factors obtained.

Author Correction: Suppression of Transposable Elements in Leukemic Stem Cells.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

Genetic Diversity, Population Structure, and Genetic Correlation with Climatic Variation in Chickpea (Cicer arietinum) Landraces from Pakistan.

Chickpea ( L.) production in arid regions, such as those predominant in Pakistan, faces immense challenges of drought and heat stress. Addressing these challenges is made more difficult by the lack of genetic and phenotypic characterization of available cultivated varieties and breeding materials. Genotyping-by-sequencing offers a rapid and cost-effective means to identify genome-wide nucleotide variation in crop germplasm. When combined with extended crop phenotypes deduced from climatic variation at sites of collection, the data can predict which portions of genetic variation might have roles in climate resilience. Here we use 8113 single nucleotide polymorphism markers to determine genetic variation and compare population structure within a previously uncharacterized collection of 77 landraces and 5 elite cultivars, currently grown in situ on farms throughout the chickpea growing regions of Pakistan. The compiled landraces span a striking aridity gradient into the Thal Desert of the Punjab. Despite low levels of variation across the collection and limited genetic structure, we found some differentiation between accessions from arid, semiarid, irrigated, and coastal areas. In a subset of 232 markers, we found evidence of differentiation along gradients of elevation and isothermality. Our results highlight the utility of exploring large germplasm collections for nucleotide variation associated with environmental extremes, and the use of such data to nominate germplasm accessions with the potential to improve crop drought tolerance and other environmental traits.

Translating natural genetic variation to gene expression in a computational model of the Drosophila gap gene regulatory network.

Annotating the genotype-phenotype relationship, and developing a proper quantitative description of the relationship, requires understanding the impact of natural genomic variation on gene expression. We apply a sequence-level model of gap gene expression in the early development of Drosophila to analyze single nucleotide polymorphisms (SNPs) in a panel of natural sequenced D. melanogaster lines. Using a thermodynamic modeling framework, we provide both analytical and computational descriptions of how single-nucleotide variants affect gene expression. The analysis reveals that the sequence variants increase (decrease) gene expression if located within binding sites of repressors (activators). We show that the sign of SNP influence (activation or repression) may change in time and space and elucidate the origin of this change in specific examples. The thermodynamic modeling approach predicts non-local and non-linear effects arising from SNPs, and combinations of SNPs, in individual fly genotypes. Simulation of individual fly genotypes using our model reveals that this non-linearity reduces to almost additive inputs from multiple SNPs. Further, we see signatures of the action of purifying selection in the gap gene regulatory regions. To infer the specific targets of purifying selection, we analyze the patterns of polymorphism in the data at two phenotypic levels: the strengths of binding and expression. We find that combinations of SNPs show evidence of being under selective pressure, while individual SNPs do not. The model predicts that SNPs appear to accumulate in the genotypes of the natural population in a way biased towards small increases in activating action on the expression pattern. Taken together, these results provide a systems-level view of how genetic variation translates to the level of gene regulatory networks via combinatorial SNP effects.

Suppression of Transposable Elements in Leukemic Stem Cells.

Genomic transposable elements (TEs) comprise nearly half of the human genome. The expression of TEs is considered potentially hazardous, as it can lead to insertional mutagenesis and genomic instability. However, recent studies have revealed that TEs are involved in immune-mediated cell clearance. Hypomethylating agents can increase the expression of TEs in cancer cells, inducing 'viral mimicry', causing interferon signalling and cancer cell killing. To investigate the role of TEs in the pathogenesis of acute myeloid leukaemia (AML), we studied TE expression in several cell fractions of AML while tracking its development (pre-leukemic haematopoietic stem cells, leukemic stem cells [LSCs], and leukemic blasts). LSCs, which are resistant to chemotherapy and serve as reservoirs for relapse, showed significant suppression of TEs and interferon pathways. Similarly, high-risk cases of myelodysplastic syndrome (MDS) showed far greater suppression of TEs than low-risk cases. We propose TE suppression as a mechanism for immune escape in AML and MDS. Repression of TEs co-occurred with the upregulation of several genes known to modulate TE expression, such as RNA helicases and autophagy genes. Thus, we have identified potential pathways that can be targeted to activate cancer immunogenicity via TEs in AML and MDS.

Deep sequencing and in silico analyses identify MYB-regulated gene networks and signaling pathways in pancreatic cancer.

We have recently demonstrated that the transcription factor MYB can modulate several cancer-associated phenotypes in pancreatic cancer. In order to understand the molecular basis of these MYB-associated changes, we conducted deep-sequencing of transcriptome of MYB-overexpressing and -silenced pancreatic cancer cells, followed by in silico pathway analysis. We identified significant modulation of 774 genes upon MYB-silencing (p < 0.05) that were assigned to 25 gene networks by in silico analysis. Further analyses placed genes in our RNA sequencing-generated dataset to several canonical signalling pathways, such as cell-cycle control, DNA-damage and -repair responses, p53 and HIF1α. Importantly, we observed downregulation of the pancreatic adenocarcinoma signaling pathway in MYB-silenced pancreatic cancer cells exhibiting suppression of EGFR and NF-κB. Decreased expression of EGFR and RELA was validated by both qPCR and immunoblotting and they were both shown to be under direct transcriptional control of MYB. These observations were further confirmed in a converse approach wherein MYB was overexpressed ectopically in a MYB-null pancreatic cancer cell line. Our findings thus suggest that MYB potentially regulates growth and genomic stability of pancreatic cancer cells via targeting complex gene networks and signaling pathways. Further in-depth functional studies are warranted to fully understand MYB signaling in pancreatic cancer.

Population genomic analysis uncovers African and European admixture in Drosophila melanogaster populations from the south-eastern United States and Caribbean Islands.

Drosophila melanogaster is postulated to have colonized North America in the past several 100 years in two waves. Flies from Europe colonized the east coast United States while flies from Africa inhabited the Caribbean, which if true, make the south-east US and Caribbean Islands a secondary contact zone for African and European D. melanogaster. This scenario has been proposed based on phenotypes and limited genetic data. In our study, we have sequenced individual whole genomes of flies from populations in the south-east US and Caribbean Islands and examined these populations in conjunction with population sequences from the west coast US, Africa, and Europe. We find that west coast US populations are closely related to the European population, likely reflecting a rapid westward expansion upon first settlements into North America. We also find genomic evidence of African and European admixture in south-east US and Caribbean populations, with a clinal pattern of decreasing proportions of African ancestry with higher latitude. Our genomic analysis of D. melanogaster populations from the south-east US and Caribbean Islands provides more evidence for the Caribbean Islands as the source of previously reported novel African alleles found in other east coast US populations. We also find the border between the south-east US and the Caribbean island to be the admixture hot zone where distinctly African-like Caribbean flies become genomically more similar to European-like south-east US flies. Our findings have important implications for previous studies examining the generation of east coast US clines via selection.

Bubble statistics and positioning in superhelically stressed DNA.

We present a general framework to study the thermodynamic denaturation of double-stranded DNA under superhelical stress. We report calculations of position- and size-dependent opening probabilities for bubbles along the sequence. Our results are obtained from transfer-matrix solutions of the Zimm-Bragg model for unconstrained DNA and of a self-consistent linearization of the Benham model for superhelical DNA. The numerical efficiency of our method allows for the analysis of entire genomes and of random sequences of corresponding length (10(6)-10(9) base pairs). We show that, at physiological conditions, opening in superhelical DNA is strongly cooperative with average bubble sizes of 10(2)-10(3) base pairs (bp), and orders of magnitude higher than in unconstrained DNA. In heterogeneous sequences, the mean degree of base-pair opening is self-averaging, while bubble localization and statistics are dominated by sequence disorder. Compared to random sequences with identical GC-content, genomic DNA has a significantly increased probability to open large bubbles under superhelical stress. These bubbles are frequently located directly upstream of transcription start sites.