Background: In recent years, hyperspectral microscopy techniques such as infrared or Raman microscopy have been applied successfully for diagnostic purposes. In many of the corresponding studies, it is common practice to measure one and the same sample under different types of microscopes. Any joint analysis of the two image modalities requires to overlay the images, so that identical positions in the sample are located at the same coordinate in both images. This step, commonly referred to as image registration, has typically been performed manually in the lack of established automated computational registration tools. Results: We propose a corresponding registration algorithm that addresses this registration problem, and demonstrate the robustness of our approach in different constellations of microscopes. First, we deal with subregion registration of Fourier Transform Infrared (FTIR) microscopic images in whole-slide histopathological staining images. Second, we register FTIR imaged cores of tissue microarrays in their histopathologically stained counterparts, and finally perform registration of Coherent anti-Stokes Raman spectroscopic (CARS) images within histopathological staining images. Conclusions: Our validation involves a large variety of samples obtained from colon, bladder, and lung tissue on three different types of microscopes, and demonstrates that our proposed method works fully automated and highly robust in different constellations of microscopes involving diverse types of tissue samples.

Background: Inferring gene regulatory network (GRN) has been an important topic in Bioinformatics. Many computational methods infer the GRN from high-throughput expression data. Due to the presence of time delays in the regulatory relationships, High-Order Dynamic Bayesian Network (HO-DBN) is a good model of GRN. However, previous GRN inference methods assume causal sufficiency, i.e. no unobserved common cause. This assumption is convenient but unrealistic, because it is possible that relevant factors have not even been conceived of and therefore un-measured. Therefore an inference method that also handles hidden common cause(s) is highly desirable. Also, previous methods for discovering hidden common causes either do not handle multi-step time delays or restrict that the parents of hidden common causes are not observed genes. Results: We have developed a discrete HO-DBN learning algorithm that can infer also hidden common cause(s) from discrete time series expression data, with some assumptions on the conditional distribution, but is less restrictive than previous methods. We assume that each hidden variable has only observed variables as children and parents, with at least two children and possibly no parents. We also make the simplifying assumption that children of hidden variable(s) are not linked to each other. Moreover, our proposed algorithm can also utilize multiple short time series (not necessarily of the same length), as long time series are difficult to obtain. Conclusions: We have performed extensive experiments using synthetic data on GRNs of size up to 100, with up to 10 hidden nodes. Experiment results show that our proposed algorithm can recover the causal GRNs adequately given the incomplete data. Using the limited real expression data and small subnetworks of the YEASTRACT network, we have also demonstrated the potential of our algorithm on real data, though more time series expression data is needed.

by Lin-Tai Da, Chao E, Baogen Duan, Chuanbiao Zhang, Xin Zhou, Jin Yu

Pyrophosphate ion (PPi) release during transcription elongation is a signature step in each nucleotide addition cycle. The kinetics and energetics of the process as well as how it proceeds with substantial conformational changes of the polymerase complex determine the mechano-chemical coupling mechanism of the transcription elongation. Here we investigated detailed dynamics of the PPi release process in a single-subunit RNA polymerase (RNAP) from bacteriophage T7, implementing all-atom molecular dynamics (MD) simulations. We obtained a jump-from-cavity kinetic model of the PPi release utilizing extensive nanosecond MD simulations. We found that the PPi release in T7 RNAP is initiated by the PPi dissociation from two catalytic aspartic acids, followed by a comparatively slow jump-from-cavity activation process. Combining with a number of microsecond long MD simulations, we also found that the activation process is hindered by charged residue associations as well as by local steric and hydrogen bond interactions. On the other hand, the activation is greatly assisted by a highly flexible lysine residue Lys472 that swings its side chain to pull PPi out. The mechanism can apply in general to single subunit RNA and DNA polymerases with similar molecular structures and conserved key residues. Remarkably, the flexible lysine or arginine residue appears to be a universal module that assists the PPi release even in multi-subunit RNAPs with charge facilitated hopping mechanisms. We also noticed that the PPi release is not tightly coupled to opening motions of an O-helix on the fingers domain of T7 RNAP according to the microsecond MD simulations. Our study thus supports the Brownian ratchet scenario of the mechano-chemical coupling in the transcription elongation of the single-subunit polymerase.

by Alex Davies, Matthieu Louis, Barbara Webb

Detailed observations of larval Drosophila chemotaxis have characterised the relationship between the odour gradient and the runs, head casts and turns made by the animal. We use a computational model to test whether hypothesised sensorimotor control mechanisms are sufficient to account for larval behaviour. The model combines three mechanisms based on simple transformations of the recent history of odour intensity at the head location. The first is an increased probability of terminating runs in response to gradually decreasing concentration, the second an increased probability of terminating head casts in response to rapidly increasing concentration, and the third a biasing of run directions up concentration gradients through modulation of small head casts. We show that this model can be tuned to produce behavioural statistics comparable to those reported for the larva, and that this tuning results in similar chemotaxis performance to the larva. We demonstrate that each mechanism can enable odour approach but the combination of mechanisms is most effective, and investigate how these low-level control mechanisms relate to behavioural measures such as the preference indices used to investigate larval learning behaviour in group assays.

by Anna Rubinski, Noam E. Ziv

Glutamatergic synapse size remodeling is governed not only by specific activity forms but also by apparently stochastic processes with well-defined statistics. These spontaneous remodeling processes can give rise to skewed and stable synaptic size distributions, underlie scaling of these distributions and drive changes in glutamatergic synapse size “configurations”. Where inhibitory synapses are concerned, however, little is known on spontaneous remodeling dynamics, their statistics, their activity dependence or their long-term consequences. Here we followed individual inhibitory synapses for days, and analyzed their size remodeling dynamics within the statistical framework previously developed for glutamatergic synapses. Similar to glutamatergic synapses, size distributions of inhibitory synapses were skewed and stable; at the same time, however, sizes of individual synapses changed considerably, leading to gradual changes in synaptic size configurations. The suppression of network activity only transiently affected spontaneous remodeling dynamics, did not affect synaptic size configuration change rates and was not followed by the scaling of inhibitory synapse size distributions. Comparisons with glutamatergic synapses within the same dendrites revealed a degree of coupling between nearby inhibitory and excitatory synapse remodeling, but also revealed that inhibitory synapse size configurations changed at considerably slower rates than those of their glutamatergic neighbors. These findings point to quantitative differences in spontaneous remodeling dynamics of inhibitory and excitatory synapses but also reveal deep qualitative similarities in the processes that control their sizes and govern their remodeling dynamics.

by Maciej Jan Ejsmond, Jacek Radwan

Major Histocompatibility Complex (MHC) genes code for proteins involved in the incitation of the adaptive immune response in vertebrates, which is achieved through binding oligopeptides (antigens) of pathogenic origin. Across vertebrate species, substitutions of amino acids at sites responsible for the specificity of antigen binding (ABS) are positively selected. This is attributed to pathogen-driven balancing selection, which is also thought to maintain the high polymorphism of MHC genes, and to cause the sharing of allelic lineages between species. However, the nature of this selection remains controversial. We used individual-based computer simulations to investigate the roles of two phenomena capable of maintaining MHC polymorphism: heterozygote advantage and host-pathogen arms race (Red Queen process). Our simulations revealed that levels of MHC polymorphism were high and driven mostly by the Red Queen process at a high pathogen mutation rate, but were low and driven mostly by heterozygote advantage when the pathogen mutation rate was low. We found that novel mutations at ABSs are strongly favored by the Red Queen process, but not by heterozygote advantage, regardless of the pathogen mutation rate. However, while the strong advantage of novel alleles increased the allele turnover rate, under a high pathogen mutation rate, allelic lineages persisted for a comparable length of time under Red Queen and under heterozygote advantage. Thus, when pathogens evolve quickly, the Red Queen is capable of explaining both positive selection and long coalescence times, but the tension between the novel allele advantage and persistence of alleles deserves further investigation.

by Minzhe Guo, Hui Wang, S. Steven Potter, Jeffrey A. Whitsett, Yan Xu

A major challenge in developmental biology is to understand the genetic and cellular processes/programs driving organ formation and differentiation of the diverse cell types that comprise the embryo. While recent studies using single cell transcriptome analysis illustrate the power to measure and understand cellular heterogeneity in complex biological systems, processing large amounts of RNA-seq data from heterogeneous cell populations creates the need for readily accessible tools for the analysis of single-cell RNA-seq (scRNA-seq) profiles. The present study presents a generally applicable analytic pipeline (SINCERA: a computational pipeline for SINgle CEll RNA-seq profiling Analysis) for processing scRNA-seq data from a whole organ or sorted cells. The pipeline supports the analysis for: 1) the distinction and identification of major cell types; 2) the identification of cell type specific gene signatures; and 3) the determination of driving forces of given cell types. We applied this pipeline to the RNA-seq analysis of single cells isolated from embryonic mouse lung at E16.5. Through the pipeline analysis, we distinguished major cell types of fetal mouse lung, including epithelial, endothelial, smooth muscle, pericyte, and fibroblast-like cell types, and identified cell type specific gene signatures, bioprocesses, and key regulators. SINCERA is implemented in R, licensed under the GNU General Public License v3, and freely available from CCHMC PBGE website, https://research.cchmc.org/pbge/sincera.html.

Journal of the American Chemical SocietyDOI: 10.1021/jacs.5b10648

Journal of the American Chemical SocietyDOI: 10.1021/jacs.5b10329

Journal of the American Chemical SocietyDOI: 10.1021/jacs.5b08720

Journal of the American Chemical SocietyDOI: 10.1021/jacs.5b07825

Journal of the American Chemical SocietyDOI: 10.1021/jacs.5b10549

Journal of the American Chemical SocietyDOI: 10.1021/jacs.5b09021

The prevailing view that the evolution of cells in a tumor is driven by Darwinian selection has never been rigorously tested. Because selection greatly affects the level of intratumor genetic diversity, it is important to assess whether intratumor evolution follows the Darwinian or the non-Darwinian mode of evolution. To provide...

Signaling through the immune checkpoint programmed cell death protein-1 (PD-1) enables tumor progression by dampening antitumor immune responses. Therapeutic blockade of the signaling axis between PD-1 and its ligand programmed cell death ligand-1 (PD-L1) with monoclonal antibodies has shown remarkable clinical success in the treatment of cancer. However, antibodies have...

The receptor-like tyrosine phosphatase CD45 regulates antigen receptor signaling by dephosphorylating the C-terminal inhibitory tyrosine of the src family kinases. However, despite its abundance, the function of the large, alternatively spliced extracellular domain of CD45 has remained elusive. We used normally spliced CD45 transgenes either incorporating a phosphatase-inactivating point mutation...

Given the highly heterogeneous character of brain malignancies and the associated implication for its proper diagnosis and treatment, finding biomarkers that better characterize this disease from a molecular standpoint is imperative. In this study, we evaluated CD146 as a potential molecular target for diagnosis and targeted therapy of glioblastoma multiforme...

Copy number variations (CNVs) have been implicated in human diseases. However, it remains unclear how they affect immune dysfunction and autoimmune diseases, including rheumatoid arthritis (RA). Here, we identified a novel leukocyte-specific protein 1 (LSP1) deletion variant for RA susceptibility located in 11p15.5. We replicated that the copy number of...

Evidence from numerous cancers suggests that increased aggressiveness is accompanied by up-regulation of signaling pathways and acquisition of properties common to stem cells. It is unclear if different subtypes of late-stage cancer vary in stemness properties and whether or not these subtypes are transcriptionally similar to normal tissue stem cells....

Fragile X syndrome is a common cause of intellectual disability and autism spectrum disorder. The gene underlying the disorder, fragile X mental retardation 1 (FMR1), is silenced in most cases by a CGG-repeat expansion mutation in the 5′ untranslated region (UTR). Recently, we identified a variant located in the 3′UTR...