Influence of ECM Composition and Intracellular Calcium on Endothelial Biomechanics and Prediction of Cellular Stresses Using Machine Learning

Endothelial cells, which form the inner layer of the vasculature, constantly interact with their external microenvironment called the extracellular matrix (ECM) by exerting contractile cell-substrate stresses called tractions and cell-cell stresses called intercellular stresses. This cellular mechanosensing can become aberrant and act as a precursor for many vascular pathological and physiological processes such as cancer metastasis, atherosclerosis, cell differentiation, migration, and morphogenesis. Also, intracellular calcium signalling plays an important role in endothelial cell motility and in maintaining vascular tone. Alteration in ECM composition has been linked to several pathologies, in fact, a transition to a fibronectin-rich matrix from a type I collagen-rich and elastin-rich matrix in coronary artery disease, for example. However, the influence of ECM compositions and intracellular calcium levels on cell mechanics is not clearly understood. The first study will shed light on ECM composition and its influence on endothelial mechanical properties including traction, intercellular stresses, cell velocity, and various morphological parameters. The second study will enhance our knowledge on the role calcium signaling plays on cellular tractions. The final chapters will focus on the development and utilization of Machine Learning (ML) models for the predictions of tractions and intercellular stresses with morphological and pharmacological predictors, which to our knowledge is the first work in the field. The results yielded from this work will further our understanding of cellular mechanics at the mesoscale by: i) Identifying the role of specific ECM molecules in mechanical signaling, ii) Understanding the influence of transient calcium signaling on tractions, and iii) Providing a machine learning framework that can be used for the prediction of tractions and intercellular stresses as a dose dependent response to a drug that is known to influence cell mechanics. These findings will be beneficial to drug development studies and targeted drug therapy for treating various vascular-related pathologies

Intersection Dimension and Maximum Degree

We show that the intersection dimension of graphs with respect to several hereditary graph classes can be bounded as a function of the maximum degree. As an interesting special case, we show that the circular dimension of a graph with maximum degree ∆ is at most O(∆ log ∆ log log ∆ ). We also obtain bounds in terms of treewidth

Biological Diversity Act, 2002: Shadow of permit-raj over research

It is too late in history of the world to think that there is time to produce ordered classifications of all plants, animals, fungi and micro-organisms, and then to employ these classifications to seek new kinds of generalities while these organisms are still extant. –Peter Rave

SNP-based pathway enrichment analysis for genome-wide association studies

<p>Abstract</p> <p>Background</p> <p>Recently we have witnessed a surge of interest in using genome-wide association studies (GWAS) to discover the genetic basis of complex diseases. Many genetic variations, mostly in the form of single nucleotide polymorphisms (SNPs), have been identified in a wide spectrum of diseases, including diabetes, cancer, and psychiatric diseases. A common theme arising from these studies is that the genetic variations discovered by GWAS can only explain a small fraction of the genetic risks associated with the complex diseases. New strategies and statistical approaches are needed to address this lack of explanation. One such approach is the pathway analysis, which considers the genetic variations underlying a biological pathway, rather than separately as in the traditional GWAS studies. A critical challenge in the pathway analysis is how to combine evidences of association over multiple SNPs within a gene and multiple genes within a pathway. Most current methods choose the most significant SNP from each gene as a representative, ignoring the joint action of multiple SNPs within a gene. This approach leads to preferential identification of genes with a greater number of SNPs.</p> <p>Results</p> <p>We describe a SNP-based pathway enrichment method for GWAS studies. The method consists of the following two main steps: 1) for a given pathway, using an adaptive truncated product statistic to identify all representative (potentially more than one) SNPs of each gene, calculating the average number of representative SNPs for the genes, then re-selecting the representative SNPs of genes in the pathway based on this number; and 2) ranking all selected SNPs by the significance of their statistical association with a trait of interest, and testing if the set of SNPs from a particular pathway is significantly enriched with high ranks using a weighted Kolmogorov-Smirnov test. We applied our method to two large genetically distinct GWAS data sets of schizophrenia, one from European-American (EA) and the other from African-American (AA). In the EA data set, we found 22 pathways with nominal P-value less than or equal to 0.001 and corresponding false discovery rate (FDR) less than 5%. In the AA data set, we found 11 pathways by controlling the same nominal P-value and FDR threshold. Interestingly, 8 of these pathways overlap with those found in the EA sample. We have implemented our method in a JAVA software package, called <it>SNP Set Enrichment Analysis </it>(SSEA), which contains a user-friendly interface and is freely available at <url>http://cbcl.ics.uci.edu/SSEA.</url></p> <p>Conclusions</p> <p>The SNP-based pathway enrichment method described here offers a new alternative approach for analysing GWAS data. By applying it to schizophrenia GWAS studies, we show that our method is able to identify statistically significant pathways, and importantly, pathways that can be replicated in large genetically distinct samples.</p

Effect of Haemoglobin and Iron Status of the Antenatal Mothers on their Newborns at Birth: A Cross-sectional Study

Introduction: Iron deficiency (ID) anaemia in pregnant mothers can affect the iron reserves of their newborns and lead to anaemia later. The haematological indices and iron status of pregnant women and its correlation with their neonates is still unclear. Aim: To assess the correlation between maternal and cord blood Hb and iron status. Materials and Methods: The present cross-sectional study included 134 antenatal mothers, at term gestation without any significant antenatal complications. Complete haemogram, serum iron, ferritin, and iron binding capacity were assessed for these mothers before delivery and also from the cord blood samples of their newborns at birth. Statistical difference and correlation were observed using Chi-square test and Pearson’s correlation coefficient. Results: Maternal anaemia Hb <11 gm/dL) was observed in 62 (46.3%). The mean Hb and ferritin of the mothers were 11.06±1.02 gm/dL and 113.3±7.1 μg/L, respectively. The mean Hb and ferritin levels of the cord blood samples were 12.24±0.17 gm/dL and 214.3±20.1 μg/L, respectively. In univariate analysis, maternal Hb showed a significant correlation with cord blood Hb with Odds Ratio (OR) 0.508 and 95% Confidence Interval (CI): 0.428-0.603. The Pearson’s correlation showed a moderate correlation between mother and cord blood Packed Cell Volume (PCV) (r=0.344, p<0.001) and weak correlation between other maternal and cord blood iron indices and serum ferritin (r=0.191, p=0.027 and r=0.203, p=0.019). Conclusion: There is a significant correlation between maternal and cord blood Hb in term neonates. The study indicates that the haematological indices of pregnant women determine the neonatal Hb in term babies

Multiple Transporters Associated with Malaria Parasite Responses to Chloroquine and Quinine

Mutations and/or overexpression of various transporters are known to confer drug resistance in a variety of organisms. In the malaria parasite Plasmodium falciparum, a homologue of P-glycoprotein, PfMDR1, has been implicated in responses to chloroquine (CO), quinine (ON) and other drugs, and a putative transporter, PfCRT, was recently demonstrated to be the key molecule in CO resistance. However, other unknown molecules are probably involved, as different parasite clones carrying the same pfcrt and pfmdr1 alleles show a wide range of quantitative responses to CO and ON. Such molecules may contribute to increasing incidences of ON treatment failure, the molecular basis of which is not understood. To identify additional genes involved in parasite CO and ON responses, we assayed the in vitro susceptibilities of 97 culture-adapted cloned isolates to CO and ON and searched for single nucleotide polymorphisms (SNPs) in DNA encoding 49 putative transporters (total 113 kb) and in 39 housekeeping genes that acted as negative controls. SNPs in 11 of the putative transporter genes, including pfcrt and pfmdr1, showed significant associations with decreased sensitivity to CQ and/or ON in P. faliparum. Significant linkage disequilibria within and between these genes were also detected, suggesting interactions among the transporter genes. This study provides specific leads for better understanding of complex drug resistances in malaria parasite

LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures

For the Library of Integrated Network-based Cellular Signatures (LINCS) project many gene expression signatures using the L1000 technology have been produced. The L1000 technology is a cost-effective method to profile gene expression in large scale. LINCS Canvas Browser (LCB) is an interactive HTML5 web-based software application that facilitates querying, browsing and interrogating many of the currently available LINCS L1000 data. LCB implements two compacted layered canvases, one to visualize clustered L1000 expression data, and the other to display enrichment analysis results using 30 different gene set libraries. Clicking on an experimental condition highlights gene-sets enriched for the differentially expressed genes from the selected experiment. A search interface allows users to input gene lists and query them against over 100 000 conditions to find the top matching experiments. The tool integrates many resources for an unprecedented potential for new discoveries in systems biology and systems pharmacology. The LCB application is available at http://www.maayanlab.net/LINCS/LCB. Customized versions will be made part of the http://lincscloud.org and http://lincs.hms.harvard.edu websites

Chronic cisplatin treatment promotes enhanced damage repair and tumor progression in a mouse model of lung cancer

Chemotherapy resistance is a major obstacle in cancer treatment, yet the mechanisms of response to specific therapies have been largely unexplored in vivo. Employing genetic, genomic, and imaging approaches, we examined the dynamics of response to a mainstay chemotherapeutic, cisplatin, in multiple mouse models of human non-small-cell lung cancer (NSCLC). We show that lung tumors initially respond to cisplatin by sensing DNA damage, undergoing cell cycle arrest, and inducing apoptosis—leading to a significant reduction in tumor burden. Importantly, we demonstrate that this response does not depend on the tumor suppressor p53 or its transcriptional target, p21. Prolonged cisplatin treatment promotes the emergence of resistant tumors with enhanced repair capacity that are cross-resistant to platinum analogs, exhibit advanced histopathology, and possess an increased frequency of genomic alterations. Cisplatin-resistant tumors express elevated levels of multiple DNA damage repair and cell cycle arrest-related genes, including p53-inducible protein with a death domain (Pidd). We demonstrate a novel role for PIDD as a regulator of chemotherapy response in human lung tumor cells.National Institutes of Health (U.S.) (grant 5-UO1-CA84306)National Cancer Institute (U.S.) (CA034992

Evaluation of RNAi and CRISPR technologies by large-scale gene expression profiling in the Connectivity Map

The application of RNA interference (RNAi) to mammalian cells has provided the means to perform phenotypic screens to determine the functions of genes. Although RNAi has revolutionized loss-of-function genetic experiments, it has been difficult to systematically assess the prevalence and consequences of off-target effects. The Connectivity Map (CMAP) represents an unprecedented resource to study the gene expression consequences of expressing short hairpin RNAs (shRNAs). Analysis of signatures for over 13,000 shRNAs applied in 9 cell lines revealed that microRNA (miRNA)-like off-target effects of RNAi are far stronger and more pervasive than generally appreciated. We show that mitigating off-target effects is feasible in these datasets via computational methodologies to produce a consensus gene signature (CGS). In addition, we compared RNAi technology to clustered regularly interspaced short palindromic repeat (CRISPR)-based knockout by analysis of 373 single guide RNAs (sgRNAs) in 6 cells lines and show that the on-target efficacies are comparable, but CRISPR technology is far less susceptible to systematic off-target effects. These results will help guide the proper use and analysis of loss-of-function reagents for the determination of gene function