Heme oxygenase-1 in the forefront of a multi-molecular network that governs cell–cell contacts and filopodia-induced zippering in prostate cancer

Prostate cancer (PCa) cells display abnormal expression of cytoskeletal proteins resulting in an augmented capacity to resist chemotherapy and colonize distant organs. We have previously shown that heme oxygenase 1 (HO-1) is implicated in cell morphology regulation in PCa. Here, through a multi 'omics' approach we define the HO-1 interactome in PCa, identifying HO-1 molecular partners associated with the integrity of the cellular cytoskeleton. The bioinformatics screening for these cytoskeletal-related partners reveal that they are highly misregulated in prostate adenocarcinoma compared with normal prostate tissue. Under HO-1 induction, PCa cells present reduced frequency in migration events, trajectory and cell velocity and, a significant higher proportion of filopodia-like protrusions favoring zippering among neighboring cells. Moreover forced expression of HO-1 was also capable of altering cell protrusions in transwell co-culture systems of PCa cells with MC3T3 cells (pre-osteoblastic cell line). Accordingly, these effects were reversed under siHO. Transcriptomics profiling evidenced significant modulation of key markers related to cell adhesion and cell–cell communication under HO-1 induction. The integration from our omics-based research provides a four molecular pathway foundation (ANXA2/HMGA1/POU3F1; NFRSF13/GSN; TMOD3/RAI14/VWF; and PLAT/PLAU) behind HO-1 regulation of tumor cytoskeletal cell compartments. The complementary proteomics and transcriptomics approaches presented here promise to move us closer to unravel the molecular framework underpinning HO-1 involvement in the modulation of cytoskeleton pathways, pushing toward a less aggressive phenotype in PCa

On Ranked Approximate Matching Of Large Attributed Graphs

Many emerging database applications entail sophisticated graph based query manipulation, predominantly evident in large-scale scientific applications. To access the information embedded in graphs, efficient graph matching tools and algorithms have become of prime importance. Although the prohibitively expensive time complexity associated with exact sub-graph isomorphism techniques has limited its efficacy in the application domain, approximate yet efficient graph matching techniques have received much attention due to their pragmatic applicability. Since public domain databases are noisy and incomplete in nature, inexact graph matching techniques have proven to be more promising in terms of inferring knowledge from numerous structural data repositories. Contemporary algorithms for approximate graph matching incur substantial cost to generate candidates, and then test and rank them for possible match. Leading algorithms balance processing time and overall resource consumption cost by leveraging sophisticated data structures and graph properties to improve overall performance. In this dissertation, we propose novel techniques for approximate graph matching based on two different techniques called TraM or Top-k Graph Matching and Approximate Network Matching or AtoM respectively. While TraM off-loads a significant amount of its processing on to the database making the approach viable for large graphs, AtoM provides improved turn around time by means of graph summarization prior to matching. The summarization process is aided by domain sensitive similarity matrices, which in turn helps improve the matching performance. The vector space embedding of the graphs and efficient filtration of the search space enables computation of approximate graph similarity at a throw-away cost. We combine domain similarity and topological similarity to obtain overall graph similarity and compare them with neighborhood biased segments of the data-graph for proper matches. We show that our approach can naturally support the emerging trend in graph pattern queries and discuss its suitability for large networks as it can be seamlessly transformed to adhere to map-reduce framework. We have conducted thorough experiments on several synthetic and real data sets, and have demonstrated the effectiveness and efficiency of the proposed method

The identification of long non-coding RNA ZFAS1 through an exploratory RNA-sequencing analysis and its association with epithelial-to-mesenchymal transition in colon cancer adenocarcinoma.

Colorectal adenocarcinoma is the fourth most common cancer diagnosed worldwide and is a significant cause of morbidity and mortality. This dissertation performed an exploratory RNA-sequencing analysis comparing gene expression between colon adenocarcinoma tissue and paired normal colon epithelium. After identification of a number of lncRNAs that were increased in expression in colon adenocarcinoma compared to normal colon epithelium, we aimed to validate the expression and investigate their function in vitro. Specifically, we focused on the lncRNA ZFAS1 and its association with epithelial-to-mesenchymal transition. These studies found the following: 1. Seven candidate lncRNAs were identified from the exploratory RNA-sequencing analysis to be significantly increased in expression in colon adenocarcinoma, three of which ZFAS1, GAS5, and PVT1 were found to be significantly increased in colon adenocarcinoma compared to paired normal colon epithelium as examined by laser capture microdissection. 2. Both ZFAS1 and GAS5 are significantly increased in cytoplasm of cell lines compared to the nucleus, whereas PVT1 was more represented in the nucleus. As such there was significant knockdown of both ZFAS1 and GAS5 following transfection with siRNA. 3. Knockdown of ZFAS1 leads to decreased proliferation and migration in colon adenocarcinoma cell lines. In contrast, knockdown of GAS5 did not lead to a change in proliferation. We focused our subsequent investigation on ZFAS1. 4. ZFAS1 has a reciprocal relationship with miR-200b and miR-200c expression in vitro but not with three of the other experimentally verified miRNAs that bind ZFAS1. We also validated the functional effect of miR-200b and miR-200c mimics on decreasing cell migration. 5. ZFAS1 knockdown is associated with the functional changes on cellular phenotype through decreasing ZEB1 expression through miR-200 signaling, causing a subsequent increase in the expression of the epithelial marker, E-cadherin, and a decrease in the expression of the mesenchymal marker, vimentin. These findings demonstrate an association between ZFAS1 and miR-200/ZEB1/E-cadherin, vimentin signaling in EMT signaling in colon adenocarcinoma. In contrast to typical EMT signaling, ZFAS1 knockdown also leads to decreased cell proliferation suggesting its potential value as a therapeutic agent

Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies

Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates

Chemical Information Bulletin

Created as a supplement for "the regular journals of the American Chemical Society," this publication contains annotated bibliographies of chemical documentation literature as well as information about meetings, conferences, awards, scholarships, and other news from the American Chemical Society (ACS) Division of Chemical Information (CINF)

Novel Gene Discovery in Primary Ciliary Dyskinesia

Primary Ciliary Dyskinesia (PCD) is one of the ‘ciliopathies’, genetic disorders affecting either cilia structure or function. PCD is a rare recessive disease caused by defective motile cilia. Affected individuals manifest with neonatal respiratory distress, chronic wet cough, upper respiratory tract problems, progressive lung disease resulting in bronchiectasis, laterality problems including heart defects and adult infertility. Early diagnosis and management are essential for better respiratory disease prognosis. PCD is a highly genetically heterogeneous disorder with causal mutations identified in 36 genes that account for the disease in about 70% of PCD cases, suggesting that additional genes remain to be discovered. Targeted next generation sequencing was used for genetic screening of a cohort of patients with confirmed or suggestive PCD diagnosis. The use of multi-gene panel sequencing yielded a high diagnostic output (> 70%) with mutations identified in known PCD genes. Over half of these mutations were novel alleles, expanding the mutation spectrum in PCD genes. The inclusion of patients from various ethnic backgrounds revealed a striking impact of ethnicity on the composition of disease alleles uncovering a significant genetic stratification of PCD in different populations. Pathogenic mutations were also identified in several new candidate genes not previously linked to PCD. Molecular and cell biology techniques were coupled with model organism studies to characterize the involvement of the new candidate genes in cilia motility and PCD. Paramecium was proven to be a good model for functional characterization of PCD potential candidate genes. The previously uncharacterized C11orf70 was identified to play a highly conserved role in dynein assembly and intraflagellar transport (IFT)-related cilia cargo trafficking. Mutations identified in DNAH9 resulted in a distinct motile cilia defect with mild respiratory symptoms, unusual in PCD. Mutations identified in two intraflagellar transport genes, IFT74 and WDR19, linked together primary and motile ciliopathy phenotypes observed in the affected individuals

Molecular simulation of protein-ligand complexes

Computational methods provide important contributions to modern drug discovery projects. In this thesis, we discuss the insights into protein-ligand interactions afforded by methods such as molecular docking, molecular dynamics (MD) and alchemical free energy calculations, which expedite the process of lead compound design and optimisation. These methods are applied to two case studies of biomolecular systems of therapeutic interest. The targets of the studies are the integrin αvβ6 and the bromodomain-containing protein 4 (BRD4). As the accuracy of molecular mechanics based methods relies on the quality of the force field in which the potential energy is calculated from, we focus on developing force field parameters for a series of small molecule inhibitors of αvβ6. Parameters are then applied to MD and relative free energy perturbation (FEP) simulations. MD simulations highlight the importance of hydrogen bonds, metal chelate interactions and cation