An evaluation of DNA-damage response and cell-cycle pathways for breast cancer classification

Accurate subtyping or classification of breast cancer is important for ensuring proper treatment of patients and also for understanding the molecular mechanisms driving this disease. While there have been several gene signatures proposed in the literature to classify breast tumours, these signatures show very low overlaps, different classification performance, and not much relevance to the underlying biology of these tumours. Here we evaluate DNA-damage response (DDR) and cell cycle pathways, which are critical pathways implicated in a considerable proportion of breast tumours, for their usefulness and ability in breast tumour subtyping. We think that subtyping breast tumours based on these two pathways could lead to vital insights into molecular mechanisms driving these tumours. Here, we performed a systematic evaluation of DDR and cell-cycle pathways for subtyping of breast tumours into the five known intrinsic subtypes. Homologous Recombination (HR) pathway showed the best performance in subtyping breast tumours, indicating that HR genes are strongly involved in all breast tumours. Comparisons of pathway based signatures and two standard gene signatures supported the use of known pathways for breast tumour subtyping. Further, the evaluation of these standard gene signatures showed that breast tumour subtyping, prognosis and survival estimation are all closely related. Finally, we constructed an all-inclusive super-signature by combining (union of) all genes and performing a stringent feature selection, and found it to be reasonably accurate and robust in classification as well as prognostic value. Adopting DDR and cell cycle pathways for breast tumour subtyping achieved robust and accurate breast tumour subtyping, and constructing a super-signature which contains feature selected mix of genes from these molecular pathways as well as clinical aspects is valuable in clinical practice.Comment: 28 pages, 7 figures, 6 table

Comparative analysis of organophosphate degrading enzymes from diverse species

Different types of organophosphorous compounds constitute most potent pesticides. These chemicals attack the nervous system of living organisms causing death. Different organisms produce enzymes to degrade these chemicals. These enzymes are present in simple microorganisms from archaea, bacteria to complex eukaryotes like humans. A comparison of representative eight shortlisted enzymes involved in the degradation and inactivation of organophosphates from a wide range of organisms was performed to infer the basis of their common functionality. There is little sequence homology in these enzymes which results in divergent tertiary structures. The only feature that these enzymes seem to share is their amino acid composition. However, structural analysis has shown no significant similarities among this functionally similar group of organophosphate degrading enzymes

Redox signaling by glutathione peroxidase 2 links vascular modulation to metabolic plasticity of breast cancer

In search of redox mechanisms in breast cancer, we uncovered a striking role for glutathione peroxidase 2 (GPx2) in oncogenic signaling and patient survival. GPx2 loss stimulates malignant progression due to reactive oxygen species/hypoxia inducible factor-α (HIF1α)/VEGFA (vascular endothelial growth factor A) signaling, causing poor perfusion and hypoxia, which were reversed by GPx2 reexpression or HIF1α inhibition. Ingenuity Pathway Analysis revealed a link between GPx2 loss, tumor angiogenesis, metabolic modulation, and HIF1α signaling. Single-cell RNA analysis and bioenergetic profiling revealed that GPx2 loss stimulated the Warburg effect in most tumor cell subpopulations, except for one cluster, which was capable of oxidative phosphorylation and glycolysis, as confirmed by coexpression of phosphorylated-AMPK and GLUT1. These findings underscore a unique role for redox signaling by GPx2 dysregulation in breast cancer, underlying tumor heterogeneity, leading to metabolic plasticity and malignant progression

Modeling the attractor landscape of disease progression: a network-based approach

Genome-wide regulatory networks enable cells to function, develop, and survive. Perturbation of these networks can lead to appearance of a disease phenotype. Inspired by Conrad Waddington's epigenetic landscape of cell development, we use a Hopfield network formalism to construct an attractor landscape model of disease progression based on protein- or gene-correlation networks of Parkinson's disease, glioma, and colorectal cancer. Attractors in this landscape correspond to normal and disease states of the cell. We introduce approaches to estimate the size and robustness of these attractors, and take a network-based approach to study their biological features such as the key genes and their functions associated with the attractors. Our results show that the attractor of cancer cells is wider than the attractor of normal cells, suggesting a heterogeneous nature of cancer. Perturbation analysis shows that robustness depends on characteristics of the input data (number of samples per time-point, and the fraction which converge to an attractor). We identify unique gene interactions at each stage, which reflect the temporal rewiring of the gene regulatory network (GRN) with disease progression. Our model of the attractor landscape, constructed from large-scale gene expression profiles of individual patients, captures snapshots of disease progression and identifies gene interactions specific to different stages, opening the way for development of stage-specific therapeutic strategies

In silico comparative genome analysis of malaria parasite Plasmodium falciparum and Plasmodium vivax chromosome 4

Malarial parasite has long been a subject of research for a large community of scientists and has yet to be conquered. One of the main obstacles to effectively control this disease is rapidly evolving genetic structure of Plasmodium parasite itself. In this study, we focused on chromosome 4 of the Plasmodium falciparum and Plasmodium vivax species and carried out comparative studies of genes that are responsible for antigenic variation in respective species. Comparative analysis of genes responsible for antigenic variation (var and vir genes in P. falciparum and P. vivax, respectively) showed significant difference in their respective nucleotide sequence lengths as well as amino acid composition. The possible association of exon's length on pathogenecity of respective Plasmodium species was also investigated, and analysis of gene structure showed that on the whole, exon lengths in P. falciparum are larger compared to P. vivax. Analysis of tandem repeats across the genome has shown that the size of repetitive sequences has a direct effect on chromosomes length, which can also be a potential reason for P. falciparum's greater variability and hence pathogenecity than P. vivax

Comparative analysis of organophosphate degrading enzymes from diverse species

Different types of organophosphorous compounds constitute most potent pesticides. These chemicals attack the nervous system of living organisms causing death. Different organisms produce enzymes to degrade these chemicals. These enzymes are present in simple microorganisms from archaea, bacteria to complex eukaryotes like humans. A comparison of representative eight shortlisted enzymes involved in the degradation and inactivation of organophosphates from a wide range of organisms was performed to infer the basis of their common functionality. There is little sequence homology in these enzymes which results in divergent tertiary structures. The only feature that these enzymes seem to share is their amino acid composition. However, structural analysis has shown no significant similarities among this functionally similar group of organophosphate degrading enzymes

Measuring cell-to-cell expression variability in single-cell RNA-sequencing data: a comparative analysis and applications to B cell aging

Abstract Background Single-cell RNA-sequencing (scRNA-seq) technologies enable the capture of gene expression heterogeneity and consequently facilitate the study of cell-to-cell variability at the cell type level. Although different methods have been proposed to quantify cell-to-cell variability, it is unclear what the optimal statistical approach is, especially in light of challenging data structures that are unique to scRNA-seq data like zero inflation. Results We systematically evaluate the performance of 14 different variability metrics that are commonly applied to transcriptomic data for measuring cell-to-cell variability. Leveraging simulations and real datasets, we benchmark the metric performance based on data-specific features, sparsity and sequencing platform, biological properties, and the ability to recapitulate true levels of biological variability based on known gene sets. Next, we use scran, the metric with the strongest all-round performance, to investigate changes in cell-to-cell variability that occur during B cell differentiation and the aging processes. The analysis of primary cell types from hematopoietic stem cells (HSCs) and B lymphopoiesis reveals unique gene signatures with consistent patterns of variable and stable expression profiles during B cell differentiation which highlights the significance of these methods. Identifying differentially variable genes between young and old cells elucidates the regulatory changes that may be overlooked by solely focusing on mean expression changes and we investigate this in the context of regulatory networks. Conclusions We highlight the importance of capturing cell-to-cell gene expression variability in a complex biological process like differentiation and aging and emphasize the value of these findings at the level of individual cell types

Not just a colourful metaphor: modelling the landscape of cellular development using Hopfield networks

The epigenetic landscape was introduced by Conrad Waddington as a metaphor of cellular development. Like a ball rolling down a hillside is channelled through a succession of valleys until it reaches the bottom, cells follow specific trajectories from a pluripotent state to a committed state. Transcription factors (TFs) interacting as a network (the gene regulatory network (GRN)) orchestrate this developmental process within each cell. Here, we quantitatively model the epigenetic landscape using a kind of artificial neural network called the Hopfield network (HN). An HN is composed of nodes (genes/TFs) and weighted undirected edges, resulting in a weight matrix (W) that stores interactions among the nodes over the entire network. We used gene co-expression to compute the edge weights. Through W, we then associate an energy score (E) to each input pattern (pattern of co-expression for a specific developmental stage) such that each pattern has a specific E. We propose that, based on the co-expression values stored in W, HN associates lower E values to stable phenotypic states and higher E to transient states. We validate our model using time course gene-expression data sets representing stages of development across 12 biological processes including differentiation of human embryonic stem cells into specialized cells, differentiation of THP1 monocytes to macrophages during immune response and trans-differentiation of epithelial to mesenchymal cells in cancer. We observe that transient states have higher energy than the stable phenotypic states, yielding an arc-shaped trajectory. This relationship was confirmed by perturbation analysis. HNs offer an attractive framework for quantitative modelling of cell differentiation (as a landscape) from empirical data. Using HNs, we identify genes and TFs that drive cell-fate transitions, and gain insight into the global dynamics of GRNs

Comparison of dynamic responses of cellular metabolites in Escherichia coli to pulse addition of substrates

We conducted an integrated study of cell growth parameters, product formation, and the dynamics of intracellular metabolite concentrations using Escherichia coli with genes knocked out in the glycolytic and oxidative pentose phosphate pathway (PPP) for glucose catabolism. We investigated the same characteristics in the wild-type strain, using acetate or pyruvate as the sole carbon source. Dramatic effects on growth parameters and extracellular and intracellular metabolite concentrations were observed after blocking either glycolytic breakdown of glucose by inactivation of phosphoglucose isomerase (disruption of pgi gene) or pentose phosphate breakdown of glucose by inactivation of glucose-6-phosphate dehydrogenase (disruption of zwf gene). Reducing power (NADPH) was mainly produced through PPP when the pgi gene was knocked out, while NADPH was produced through the tricarboxylic acid (TCA) cycle by isocitrate dehydrogenase or NADP-linked malic enzyme when the zwf gene was knocked out. As expected, when the pgi gene was knocked out, intracellular concentrations of PPP metabolites were high and glycolytic and concentrations of TCA cycle pathway metabolites were low. In the zwf gene knockout, concentrations of PPP metabolites were low and concentrations of intracellular glycolytic and TCA cycle metabolites were high