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Cardiac hypertrophy is defined as increased heart mass in response to increased hemodynamic requirements. Long-term cardiac hypertrophy, if not counteracted, will ultimately lead to heart failure. The incidence of heart failure is related to myocardial infarction, which could be salvaged by reperfusion and ultimately invites unfavorable myocardial ischemia-reperfusion injury. The Na+/H+ exchangers (NHEs) are membrane transporters that exchange one intracellular proton for one extracellular Na+. The first discovered NHE isoform, NHE1, is expressed almost ubiquitously in all tissues, especially in the myocardium. During myocardial ischemia-reperfusion, NHE1 catalyzes increased uptake of intracellular Na+, which in turn leads to Ca2+ overload and subsequently myocardial injury. Numerous preclinical research has shown that NHE1 is involved in cardiac hypertrophy and heart failure, but the exact molecular mechanisms remain elusive. The objective of this review is to demonstrate the potential role of NHE1 in cardiac hypertrophy and heart failure and investigate the underlying mechanisms.

Classification of histology sections in large cohorts, in terms of distinct regions of microanatomy (e.g., stromal) and histopathology (e.g., tumor, necrosis), enables the quantification of tumor composition, and the construction of predictive models of genomics and clinical outcome. To tackle the large technical variations and biological heterogeneities, which are intrinsic in large cohorts, emerging systems utilize either prior knowledge from pathologists or unsupervised feature learning for invariant representation of the underlying properties in the data. However, to a large degree, the architecture for tissue histology classification remains unexplored and requires urgent systematical investigation. This paper is the first attempt to provide insights into three fundamental questions in tissue histology classification: I. Is unsupervised feature learning preferable to human engineered features? II. Does cellular saliency help? III. Does the sparse feature encoder contribute to recognition? We show that (a) in I, both Cellular Morphometric Feature and features from unsupervised feature learning lead to superior performance when compared to SIFT and [Color, Texture]; (b) in II, cellular saliency incorporation impairs the performance for systems built upon pixel-/patch-level features; and (c) in III, the effect of the sparse feature encoder is correlated with the robustness of features, and the performance can be consistently improved by the multi-stage extension of systems built upon both Cellular Morphmetric Feature and features from unsupervised feature learning. These insights are validated with two cohorts of Glioblastoma Multiforme (GBM) and Kidney Clear Cell Carcinoma (KIRC).

Classification of histology sections in large cohorts, in terms of distinct regions of microanatomy (e.g., stromal) and histopathology (e.g., tumor, necrosis), enables the quantification of tumor composition, and the construction of predictive models of genomics and clinical outcome. To tackle the large technical variations and biological heterogeneities, which are intrinsic in large cohorts, emerging systems utilize either prior knowledge from pathologists or unsupervised feature learning for invariant representation of the underlying properties in the data. However, to a large degree, the architecture for tissue histology classification remains unexplored and requires urgent systematical investigation. This paper is the first attempt to provide insights into three fundamental questions in tissue histology classification: I. Is unsupervised feature learning preferable to human engineered features? II. Does cellular saliency help? III. Does the sparse feature encoder contribute to recognition? We show that (a) in I, both Cellular Morphometric Feature and features from unsupervised feature learning lead to superior performance when compared to SIFT and [Color, Texture]; (b) in II, cellular saliency incorporation impairs the performance for systems built upon pixel-/patch-level features; and (c) in III, the effect of the sparse feature encoder is correlated with the robustness of features, and the performance can be consistently improved by the multi-stage extension of systems built upon both Cellular Morphmetric Feature and features from unsupervised feature learning. These insights are validated with two cohorts of Glioblastoma Multiforme (GBM) and Kidney Clear Cell Carcinoma (KIRC).

Explosive growth in the study of microbial epigenetics has revealed a diversity of chemical structures and biological functions of DNA modifications in restriction-modification (R-M) and basic genetic processes. Here, we describe the discovery of shared consensus sequences for two seemingly unrelated DNA modification systems, [superscript 6m]A methylation and phosphorothioation (PT), in which sulfur replaces a nonbridging oxygen in the DNA backbone. Mass spectrometric analysis of DNA from Escherichia coli B7A and Salmonella enterica serovar Cerro 87, strains possessing PT-based R-M genes, revealed d(G[subscript PS] [superscript 6m]A) dinucleotides in the G[subscript PS] [superscript 6m]AAC consensus representing ∼5% of the 1,100 to 1,300 PT-modified d(G[subscript PS] A) motifs per genome, with [superscript 6m]A arising from a yet-to-be-identified methyltransferase. To further explore PT and 6m A in another consensus sequence, G[subscript PS] [superscript 6m]ATC, we engineered a strain of E. coli HST04 to express Dnd genes from Hahella chejuensis KCTC2396 (PT in G[subscript PS] ATC) and Dam methyltransferase from E. coli DH10B ( [superscript 6m] A in G [superscript 6m] ATC). Based on this model, in vitro studies revealed reduced Dam activity in G PS ATC-containing oligonucleotides whereas single-molecule real-time sequencing of HST04 DNA revealed [superscript 6m] A in all 2,058 G[subscript PS] ATC sites (5% of 37,698 total GATC sites). This model system also revealed temperature-sensitive restriction by DndFGH in KCTC2396 and B7A, which was exploited to discover that [superscript 6m] A can substitute for PT to confer resistance to restriction by the DndFGH system. These results point to complex but unappreciated interactions between DNA modification systems and raise the possibility of coevolution of interacting systems to facilitate the function of each.

Quantitative analysis of whole slide images (WSIs) in a large cohort may provide predictive models of clinical outcome. However, the performance of the existing techniques is hindered as a result of large technical variations (e.g., fixation, staining) and biological heterogeneities (e.g., cell type, cell state) that are always present in a large cohort. Although unsupervised feature learning provides a promising way in learning pertinent features without human intervention, its capability can be greatly limited due to the lack of well-curated examples. In this paper, we explored the transferability of knowledge acquired from a well-curated Glioblastoma Multiforme (GBM) dataset through its application to the representation and characterization of tissue histology from the Cancer Genome Atlas (TCGA) Breast Invasive Carcinoma (BRCA) cohort. Our experimental results reveals two major phenotypic subtypes with statistically significantly different survival curves. Further differential expression analysis of these two subtypes indicates enrichment of genes regulated by NF-kB in response to TNF and genes up-regulated in response to IFNG.

Quantitative analysis of whole slide images (WSIs) in a large cohort may provide predictive models of clinical outcome. However, the performance of the existing techniques is hindered as a result of large technical variations (e.g., fixation, staining) and biological heterogeneities (e.g., cell type, cell state) that are always present in a large cohort. Although unsupervised feature learning provides a promising way in learning pertinent features without human intervention, its capability can be greatly limited due to the lack of well-curated examples. In this paper, we explored the transferability of knowledge acquired from a well-curated Glioblastoma Multiforme (GBM) dataset through its application to the representation and characterization of tissue histology from the Cancer Genome Atlas (TCGA) Breast Invasive Carcinoma (BRCA) cohort. Our experimental results reveals two major phenotypic subtypes with statistically significantly different survival curves. Further differential expression analysis of these two subtypes indicates enrichment of genes regulated by NF-kB in response to TNF and genes up-regulated in response to IFNG.

Integrative analysis based on quantitative representation of whole slide images (WSIs) in a large histology cohort may provide predictive models of clinical outcome. On one hand, the efficiency and effectiveness of such representation is hindered as a result of large technical variations (e.g., fixation, staining) and biological heterogeneities (e.g., cell type, cell state) that are always present in a large cohort. On the other hand, perceptual interpretation/validation of important multivariate phenotypic signatures are often difficult due to the loss of visual information during feature transformation in hyperspace. To address these issues, we propose a novel approach for integrative analysis based on cellular morphometric context, which is a robust representation of WSI, with the emphasis on tumor architecture and tumor heterogeneity, built upon cellular level morphometric features within the spatial pyramid matching (SPM) framework. The proposed approach is applied to The Cancer Genome Atlas (TCGA) lower grade glioma (LGG) cohort, where experimental results (i) reveal several clinically relevant cellular morphometric types, which enables both perceptual interpretation/validation and further investigation through gene set enrichment analysis; and (ii) indicate the significantly increased survival rates in one of the cellular morphometric context subtypes derived from the cellular morphometric context.