Proteases as modulators of tumor-stromal interaction: primary tumors to bone metastases

As cells undergo oncogenic transformation and as malignant cells arrive at metastatic sites, a complex interplay occurs with the surrounding stroma. This dialogue between the tumor and stroma ultimately dictates the success of the tumor cells in the given microenvironment. As a result, understanding the molecular mechanisms at work is important for developing new therapeutic modalities. Proteases are major players in the interaction between tumor and stroma. This review will focus on the role of proteases in modulating tumor-stromal interactions of both primary breast and prostate tumors as well as at bone metastatic sites in a way that favors tumor growth.     

Exploiting gene-environment interaction to detect genetic associations

Complex disease by definition results from the interplay of genetic and environmental factors. However, it is currently unclear how gene-environment interaction can best be used to locate complex disease susceptibility loci, particularly in the context of studies where between 1,000 and 1,000,000 markers are scanned for association with disease. We present a joint test of marginal association and gene-environment interaction for case-control data. We compare the power and sample size requirements of this joint test to other analyses: the marginal test of genetic association, the standard test for gene-environment interaction based on logistic regression, and the case-only test for interaction that exploits gene-environment independence. Although for many penetrance models the joint test of genetic marginal effect and interaction is not the most powerful, it is nearly optimal across all penetrance models we considered. In particular, it generally has better power than the marginal test when the genetic effect is restricted to exposed subjects and much better power than the tests of gene-environment interaction when the genetic effect is not restricted to a particular exposure level. This makes the joint test an attractive tool for large-scale association scans where the true gene-environment interaction model is unknown.     

Peptides as modulators of enzymes and regulatory proteins

There is currently great interest in the development of methods to modulate the function of diverse classes of target proteins with chemicals (agonists or antagonists). These would be valuable reagents for biomedical research and some might serve as potential drug leads. Traditionally, most chemicals that modulate protein function have been enzyme inhibitors isolated in functional screens specific for the enzyme of interest. However, recent efforts from many laboratories have suggested that relatively simple binding assays may provide a more convenient and general route to chemical modulators. We review here this work with a particular emphasis on peptide modulators.     

Mixture interaction analysis: a polynomial model for multiple-receptor systems which incorporates the Beidler equation

A multiple-receptor model for analysis of mixture interactionsis proposed. This model is polynomial, removing the possibilityof predicted responses exceeding the maximum response of a system.The Beidler equation for the kinetics of response to singlecomponents was incorporated into the polynomial model, providinga model for multiple-receptor systems using the same parametersas for the Beidler single-receptor mixture model. The predictionsof the polynomial and the conventional multiple-receptor modelsare similar only at lower response levels. A mixed model isalso proposed which provides for responses to mixtures in whichsome components compete for the same receptor and other componentseach bind to independent receptors.     

Integration of miRNA weighted gene co-expression network and miRNA-mRNA co-expression analyses reveals potential regulatory functions of miRNAs in calf rumen development

This study aimed to explore the roles of microRNAs (miRNAs) in calf rumen development during early life. Rumen tissues were collected from 16 calves (8 at pre-weaning and 8 at post-weaning) for miRNA-sequencing, differential expression (DE), miRNA weighted gene co-expression network (WGCNA) and miRNA-mRNA co-expression analyses. 295 miRNAs were identified. Bta-miR-143, miR-26a, miR-145 and miR-27b were the most abundantly expressed. 122 miRNAs were significantly DE between the pre- and post-weaning periods and the most up- and down-regulated miRNAs were bta-miR-29b and bta-miR-493, respectively. Enrichment analyses of the target genes of DE miRNAs revealed important roles for miRNA in rumen developmental processes, immune system development, protein digestion and processes related to the extracellular matrix. WGCNA indicated that bta-miR-145 and bta-miR-199a-3p are important hub miRNAs in the regulation of these processes. Therefore, bta-miR-143, miR-29b, miR-145, miR-493, miR-26a and miR-199 family members might be key regulators of calf rumen development during early life.     

Prediction of regulatory pathways using mRNA expression and protein interaction data: application to identification of galactose regulatory pathway

We propose a novel technique that constructs gene regulatory networks from DNA microarray data and gene-protein databases and then applies Mason rule to systematically search for the most dominant regulators of the network. The algorithm then recommends the identified dominant regulator genes as the best candidates for future knock-out experiments. Actively choosing the genes for knock-out experiments allows optimal perturbation of the pathway and therefore produces the most informative DNA microarray data for pathway identification purposes. This approach is more practically advantageous in analysis of large pathways where the time and cost of DNA microarray data experiments can be reduced using the proposed optimal experiment design. The proposed method was successfully tested on the galactose regulatory network.     

License to heal: bidirectional interaction of antigen-specific regulatory T cells and tolerogenic APC

Naturally occurring CD4(+)CD25(+) regulatory T (T(R)) cells, a component of the innate immune response, which play a key role in the maintenance of self-tolerance, have become the focus of numerous studies over the last decade. These cells inhibit the immune response in an Ag-nonspecific manner, interacting with other T cells. Much less is known about adaptive T(R) cells, which develop in response to chronic antigenic stimulation, and act directly on professional and nonprofessional APC, rendering them tolerogenic and able to elicit the differentiation of CD8(+) and CD4(+) T cells with suppressive activity. In this review, we will discuss data pertaining to the bidirectional interaction between Ag-specific T(R) with APC and their clinical relevance.     

On the sleep promoting effects of BR-16A: interaction with GABAergic modulators

Pentobarbitone-induced hypnosis test was used as an animal model to explore the role of BR-16A, a polyherbal formulation in sleep. Pentobarbitone produces quick sleep latency (onset) and prolongation of total sleep time (duration). Sleep latency and total sleep time were used as a parameters for the evaluation. BR-16A potentiated the effect of triazolam (0.1 mg/kg, ip) and alprazolam (0.25 mg/kg, ip). Melatonin (5.0 mg/kg, ip) and zolpidem (0.5 mg/kg, ip) did not produce any significant effect on sleep parameters. However, alprazolam (0.25mg/kg, ip) potentiated the effect of BR-16A (100 mg/ kg, po) in higher dose only. Sleep promoting effect of BR-16A in combination with GABAergic drugs (triazolam and alprazolam,) suggested that these drugs have common mechanism in sleep promoting effect of pentobarbitone and could be used along with other GABAergic hypnotics for the treatment of insomnia. This may reduce the dose of the latter drug(s). BR-16A can be used for the treatment of sleep and sleep-related disorders.     

Bayesian model to detect phenotype-specific genes for copy number data

ABSTRACT: BACKGROUND: An important question in genetic studies is to determine those genetic variants, in particular CNVs, that arespecific to different groups of individuals. This could help in elucidating differences in disease predispositionand response to pharmaceutical treatments. We propose a Bayesian model designed to analyze thousands of copynumber variants (CNVs) where only few of them are expected to be associated with a specific phenotype. RESULTS: The model is illustrated by analyzing three major human groups belonging to HapMap data. We also show howthe model can be used to determine specific CNVs related to response to treatment in patients diagnosed withovarian cancer. The model is also extended to address the problem of how to adjust for confounding covariates(e.g., population stratification). Through a simulation study, we show that the proposed model outperforms otherapproaches that are typically used to analyze this data when analyzing common copy-number polymorphisms(CNPs) or complex CNVs. We have developed an R package, called bayesGen, that implements the model andestimating algorithms. CONCLUSIONS: Our proposed model is useful to discover specific genetic variants when different subgroups of individuals areanalyzed. The model can address studies with or without control group. By integrating all data in a unique modelwe can obtain a list of genes that are associated with a given phenotype as well as a different list of genes that areshared among the different subtypes of cases.     

Genetic network inference: from co-expression clustering to reverse engineering

MOTIVATION: Advances in molecular biological, analytical and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. In particular, using high-throughput gene expression assays, we are able to measure the output of the gene regulatory network. We aim here to review datamining and modeling approaches for conceptualizing and unraveling the functional relationships implicit in these datasets. Clustering of co-expression profiles allows us to infer shared regulatory inputs and functional pathways. We discuss various aspects of clustering, ranging from distance measures to clustering algorithms and multiple-cluster memberships. More advanced analysis aims to infer causal connections between genes directly, i.e. who is regulating whom and how. We discuss several approaches to the problem of reverse engineering of genetic networks, from discrete Boolean networks, to continuous linear and non-linear models. We conclude that the combination of predictive modeling with systematic experimental verification will be required to gain a deeper insight into living organisms, therapeutic targeting and bioengineering.     

Network-based model weighting to detect multiple loci influencing complex diseases

For genome-wide association studies, it has been increasingly recognized that the popular locus-by-locus search for DNA variants associated with disease susceptibility may not be effective, especially when there are interactions between or among multiple loci, for which a multi-loci search strategy may be more productive. However, even if computationally feasible, a genome-wide search over all possible multiple loci requires exploring a huge model space and making costly adjustment for multiple testing, leading to reduced statistical power. On the other hand, there are accumulating data suggesting that protein products of many disease-causing genes tend to interact with each other, or cluster in the same biological pathway. To incorporate this prior knowledge and existing data on gene networks, we propose a gene network-based method to improve statistical power over that of the exhaustive search by giving higher weights to models involving genes nearby in a network. We use simulated data under realistic scenarios, including a large-scale human protein–protein interaction network and 23 known ataxia-causing genes, to demonstrate potential gain by our proposed method when disease-genes are clustered in a network.     

YOLO-Fish: A robust fish detection model to detect fish in realistic underwater environment

Over the last few years, several research works have been performed to monitor fish in the underwater environment aimed for marine research, understanding ocean geography, and primarily for sustainable fisheries. Automating fish identification is very helpful, considering the time and cost of the manual process. However, it can be challenging to differentiate fish from the seabed and fish types from each other due to environmental challenges like low illumination, complex background, high variation in luminosity, free movement of fish, and high diversity of fish species. In this paper, we propose YOLO-Fish, a deep learning based fish detection model. We have proposed two models, YOLO-Fish-1 and YOLO-Fish-2. YOLO-Fish-1 enhances YOLOv3 by fixing the issue of upsampling step sizes of to reduce the misdetection of tiny fish. YOLO-Fish-2 further improves the model by adding Spatial Pyramid Pooling to the first model to add the capability to detect fish appearance in those dynamic environments. To test the models, we introduce two datasets: DeepFish and OzFish. The DeepFish dataset contains around 15k bounding box annotations across 4505 images, where images belong to 20 different fish habitats. The OzFish is another dataset comprised of about 43k bounding box annotations of wide varieties of fish across around 1800 images. YOLO-Fish1 and YOLO-Fish2 achieved average precision of 76.56% and 75.70%, respectively for fish detection in unconstrained real-world marine environments, which is significantly better than YOLOv3. Both of these models are lightweight compared to recent versions of YOLO like YOLOv4, yet the performances are very similar.     

Characterization of a scintillation proximity assay to detect modulators of transforming growth factor alpha (TGF alpha) binding.

A scintillation proximity assay (SPA) for transforming growth factor alpha (TGF alpha) using SPA beads coated with A431 membranes has been studied. Binding of TGF alpha to the beads was characteristic of a receptor interaction. A class of high-affinity receptors for [125I]-TGF alpha (Kd = 0.10-0.26 nM) was detected by competition studies between [125I]TGF alpha and cold TGF alpha and by analysis of association and dissociation rate constants. An antibody to the epidermal growth factor receptor (clone 528) inhibited binding of [125I]TGF alpha (IC50 = 0.20 micrograms/ml), but an anti-TGF alpha antibody (clone 134A-2B3) (less than 25 micrograms/ml) did not block binding. Suramin inhibited [125I]-TGF alpha binding (IC50 = 0.20 mM). The ether lipids 1-O-hexadecyl-2-O-methyl-sn-glycero-3-phosphocholine, 1-O-octadecyl-2-O-methyl-sn-glycero-3-phosphocholine, and rac-lyso-platelet activating factor inhibited TGF alpha binding (IC50 values of 49, 69, and 57 microM, respectively). SPA is a convenient method for identifying agents that may act by interfering with TGF alpha binding.