Asymmetric Primitive-Model Electrolytes: Debye-Huckel Theory, Criticality and Energy Bounds

Debye-Huckel (DH) theory is extended to treat two-component size- and charge-asymmetric primitive models, focussing primarily on the 1:1 additive hard-sphere electrolyte with, say, negative ion diameters, a--, larger than the positive ion diameters, a++. The treatment highlights the crucial importance of the charge-unbalanced ``border zones'' around each ion into which other ions of only one species may penetrate. Extensions of the DH approach which describe the border zones in a physically reasonable way are exact at high $T$ and low density, $\rho$, and, furthermore, are also in substantial agreement with recent simulation predictions for \emph{trends} in the critical parameters, $T_c$ and $\rho_c$, with increasing size asymmetry. Conversely, the simplest linear asymmetric DH description, which fails to account for physically expected behavior in the border zones at low $T$, can violate a new lower bound on the energy (which applies generally to models asymmetric in both charge and size). Other recent theories, including those based on the mean spherical approximation, have predicted trends in the critical parameters quite opposite to those established by the simulations.Comment: to appear in Physical Review

Error-pooling-based statistical methods for identifying novel temporal replication profiles of human chromosomes observed by DNA tiling arrays

Statistical analysis on tiling array data is extremely challenging due to the astronomically large number of sequence probes, high noise levels of individual probes and limited number of replicates in these data. To overcome these difficulties, we first developed statistical error estimation and weighted ANOVA modeling approaches to high-density tiling array data, especially the former based on an advanced error-pooling method to accurately obtain heterogeneous technical error of small-sample tiling array data. Based on these approaches, we analyzed the high-density tiling array data of the temporal replication patterns during cell-cycle S phase of synchronized HeLa cells on human chromosomes 21 and 22. We found many novel temporal replication patterns, identifying about 26% of over 1 million tiling array sequence probes with significant differential replication during the four 2-h time periods of S phase. Among these differentially replicated probes, 126 941 sequence probes were matched to 417 known genes. The majority of these genes were found to be replicated within one or two consecutive time periods, while the others were replicated at two non-consecutive time periods. Also, coding regions found to be more differentially replicated in particular time periods than noncoding regions in the gene-poor chromosome 21 (25% differentially replicated among genic probes versus 18.6% among intergenic probes), while such a phenomenon was less prominent in gene-rich chromosome 22. A rigorous statistical testing for local proximity of differentially replicated genic and intergenic probes was performed to identify significant stretches of differentially replicated sequence regions. From this analysis, we found that adjacent genes were frequently replicated at different time periods, potentially implying the existence of quite dense replication origins. Evaluating the conditional probability significance of identified gene ontology terms on chromosomes 21 and 22, we detected some over-represented molecular functions and biological processes among these differentially replicated genes, such as the ones relevant to hydrolase, transferase and receptor-binding activities. Some of these results were confirmed showing >70% consistency with cDNA microarray data that were independently generated in parallel with the tiling arrays. Thus, our improved analysis approaches specifically designed for high-density tiling array data enabled us to reliably and sensitively identify many novel temporal replication patterns on human chromosomes

Loss of TET2 increases B-1 cell number and IgM production while limiting CDR3 diversity

Recent studies have demonstrated a role for Ten-Eleven Translocation-2 (TET2), an epigenetic modulator, in regulating germinal center formation and plasma cell differentiation in B-2 cells, yet the role of TET2 in regulating B-1 cells is largely unknown. Here, B-1 cell subset numbers, IgM production, and gene expression were analyzed in mice with global knockout of TET2 compared to wildtype (WT) controls. Results revealed that TET2-KO mice had elevated numbers of B-1a and B-1b cells in their primary niche, the peritoneal cavity, as well as in the bone marrow (B-1a) and spleen (B-1b). Consistent with this finding, circulating IgM, but not IgG, was elevated in TET2-KO mice compared to WT. Analysis of bulk RNASeq of sort purified peritoneal B-1a and B-1b cells revealed reduced expression of heavy and light chain immunoglobulin genes, predominantly in B-1a cells from TET2-KO mice compared to WT controls. As expected, the expression of IgM transcripts was the most abundant isotype in B-1 cells. Yet, only in B-1a cells there was a significant increase in the proportion of IgM transcripts in TET2-KO mice compared to WT. Analysis of the CDR3 of the BCR revealed an increased abundance of replicated CDR3 sequences in B-1 cells from TET2-KO mice, which was more clearly pronounced in B-1a compared to B-1b cells. V-D-J usage and circos plot analysis of V-J combinations showed enhanced usage of VH11 and VH12 pairings. Taken together, our study is the first to demonstrate that global loss of TET2 increases B-1 cell number and IgM production and reduces CDR3 diversity, which could impact many biological processes and disease states that are regulated by IgM

Quantum computing at the frontiers of biological sciences

The search for meaningful structure in biological data has relied on cutting-edge advances in computational technology and data science methods. However, challenges arise as we push the limits of scale and complexity in biological problems. Innovation in massively parallel, classical computing hardware and algorithms continues to address many of these challenges, but there is a need to simultaneously consider new paradigms to circumvent current barriers to processing speed. Accordingly, we articulate a view towards quantum computation and quantum information science, where algorithms have demonstrated potential polynomial and exponential computational speedups in certain applications, such as machine learning. The maturation of the field of quantum computing, in hardware and algorithm development, also coincides with the growth of several collaborative efforts to address questions across length and time scales, and scientific disciplines. We use this coincidence to explore the potential for quantum computing to aid in one such endeavor: the merging of insights from genetics, genomics, neuroimaging and behavioral phenotyping. By examining joint opportunities for computational innovation across fields, we highlight the need for a common language between biological data analysis and quantum computing. Ultimately, we consider current and future prospects for the employment of quantum computing algorithms in the biological sciences

Application of machine learning methods to histone methylation ChIP-Seq data reveals H4R3me2 globally represses gene expression

<p>Abstract</p> <p>Background</p> <p>In the last decade, biochemical studies have revealed that epigenetic modifications including histone modifications, histone variants and DNA methylation form a complex network that regulate the state of chromatin and processes that depend on it including transcription and DNA replication. Currently, a large number of these epigenetic modifications are being mapped in a variety of cell lines at different stages of development using high throughput sequencing by members of the ENCODE consortium, the NIH Roadmap Epigenomics Program and the Human Epigenome Project. An extremely promising and underexplored area of research is the application of machine learning methods, which are designed to construct predictive network models, to these large-scale epigenomic data sets.</p> <p>Results</p> <p>Using a ChIP-Seq data set of 20 histone lysine and arginine methylations and histone variant H2A.Z in human CD4⁺T-cells, we built predictive models of gene expression as a function of histone modification/variant levels using Multilinear (ML) Regression and Multivariate Adaptive Regression Splines (MARS). Along with extensive crosstalk among the 20 histone methylations, we found H4R3me2 was the most and second most globally repressive histone methylation among the 20 studied in the ML and MARS models, respectively. In support of our finding, a number of experimental studies show that PRMT5-catalyzed symmetric dimethylation of H4R3 is associated with repression of gene expression. This includes a recent study, which demonstrated that H4R3me2 is required for DNMT3A-mediated DNA methylation--a known global repressor of gene expression.</p> <p>Conclusion</p> <p>In stark contrast to univariate analysis of the relationship between H4R3me2 and gene expression levels, our study showed that the regulatory role of some modifications like H4R3me2 is masked by confounding variables, but can be elucidated by multivariate/systems-level approaches.</p

Stem Cell Pluripotency Genes Klf4 and Oct4 Regulate Complex SMC Phenotypic Changes Critical in Late-Stage Atherosclerotic Lesion Pathogenesis

Background: Rupture and erosion of advanced atherosclerotic lesions with a resultant myocardial infarction or stroke are the leading worldwide cause of death. However, we have a limited understanding of the identity, origin, and function of many cells that make up late-stage atherosclerotic lesions, as well as the mechanisms by which they control plaque stability. Methods: We conducted a comprehensive single-cell RNA sequencing of advanced human carotid endarterectomy samples and compared these with single-cell RNA sequencing from murine microdissected advanced atherosclerotic lesions with smooth muscle cell (SMC) and endothelial lineage tracing to survey all plaque cell types and rigorously determine their origin. We further used chromatin immunoprecipitation sequencing (ChIP-seq), bulk RNA sequencing, and an innovative dual lineage tracing mouse to understand the mechanism by which SMC phenotypic transitions affect lesion pathogenesis. Results: We provide evidence that SMC-specific Klf4- versus Oct4-knockout showed virtually opposite genomic signatures, and their putative target genes play an important role regulating SMC phenotypic changes. Single-cell RNA sequencing revealed remarkable similarity of transcriptomic clusters between mouse and human lesions and extensive plasticity of SMC- and endothelial cell-derived cells including 7 distinct clusters, most negative for traditional markers. In particular, SMC contributed to a Myh11 -, Lgals3 +population with a chondrocyte-like gene signature that was markedly reduced with SMC-Klf4 knockout. We observed that SMCs that activate Lgals3 compose up to two thirds of all SMC in lesions. However, initial activation of Lgals3 in these cells does not represent conversion to a terminally differentiated state, but rather represents transition of these cells to a unique stem cell marker gene-positive, extracellular matrix-remodeling, "pioneer" cell phenotype that is the first to invest within lesions and subsequently gives rise to at least 3 other SMC phenotypes within advanced lesions, including Klf4-dependent osteogenic phenotypes likely to contribute to plaque calcification and plaque destabilization. Conclusions: Taken together, these results provide evidence that SMC-derived cells within advanced mouse and human atherosclerotic lesions exhibit far greater phenotypic plasticity than generally believed, with Klf4 regulating transition to multiple phenotypes including Lgals3 +osteogenic cells likely to be detrimental for late-stage atherosclerosis plaque pathogenesis