44 research outputs found
WNT-DEPENDENT REGENERATIVE FUNCTION IS INDUCED IN LEUKEMIA-INITIATING AC133BRIGHT CELLS
The Cancer Stem Cell model supported the notion that leukemia was initiated and maintained in vivo by a small fraction of leukemia-initiating cells (LICs). Previous studies have suggested the involvement of Wnt signaling pathway in Acute Myeloid Leukemia (AML) by the ability to sustain the development of LICs. A novel hematopoietic stem and progenitor cell marker, monoclonal antibody AC133, recognizes the CD34bright CD38- subset of human acute myeloid leukemia cells, suggesting that it may be an early marker for the LICs. During the first part of my phD program we previously evaluated the ability of leukemic AC133+ fraction, to perform engraftment following to xenotransplantation in immunodeficient mouse model Rag2-/-\u3b3c-/-. The results showed that the surface marker AC133 is able to enrich for the cell fraction that contains the LICs. In consideration of our previously reported data, derived from the expression profiling analysis performed in normal (n=10) and leukemic (n=33) human long-term reconstituting AC133+ cells, we revealed that the ligand-dependent Wnt signaling is induced in AML through a diffuse expression and release of WNT10B, a hematopoietic stem cells regenerative-associated molecule. In situ detection performed on bone marrow biopsies of AML patients, showed the activation of the Wnt pathway, through the concomitant presence of the ligand WNT10B and of the active dephosphorylated \u3b2-catenin form, suggesting an autocrine / paracrine-type ligand-dependent activation mechanism. In consideration of the link between hematopoietic regeneration and developmental signaling, we transplanted primary AC133+ AML A46 cells into developing zebrafish. This biosensor model revealed the formation of ectopic structures by activation of dorsal organizer markers that act downstream of the Wnt pathway. These results suggested that the misappropriating Wnt associated functions can promote pathological stem cell-like regeneration responsiveness. The analyses performed in situ retained information on the cellular localization, enabling determination of the activity status of individual cells and allowing the tumor environment view. Taking this issue into consideration, during the second part of my phD program, I set up the application of a new in situ method for localized detection and genotyping of individual transcripts directly in cells and tissues. The mRNA in situ detection technique is based on padlock probes ligation and target priming rolling circle amplification allowing the single nucleotide resolution in heterogenous tissues. The mRNA in situ detection performed on bone marrow biopsies derived from AML patients, showed a diffuse localization pattern of WNT10B molecule in the tissue. Conversely, only the AC133bright cell population shows the Wnt signaling activation signature represented by the cytoplasmatic accumulation and nuclear translocation of the active form of \u3b2-catenin. In spite of this, we previously evidenced that the regenerative function of WNT signaling pathway is defined by the up-regulation of WNT10B, WNT10A, WNT2B and WNT6 loci, we identified the WNT10B as a major locus associated with the regenerative function and over-expressed by all AML patients. By the molecular evaluation of the WNT10B transcript, we isolated an aberrant splicing variant (WNT10BIVS1), that identify Non Core-Binding Factor Leukemia (NCBFL) class and whose potential role is discussed. Moreover, we demonstrate that the function of "leukemia stem cell", present in the cell population enriched for the marker AC133bright, is strictly related to regenerative function associated with WNT signaling, defining the key role of WNT10B ligand as a specific molecular marker for leuchemogenesis. This thesis defines the new suitable approaches to characterize the leukemia-initiating cells (LICs) and suggest the role of WNT10B as a new suitable target for AML
Evolutionary genomics : statistical and computational methods
This open access book addresses the challenge of analyzing and understanding the evolutionary dynamics of complex biological systems at the genomic level, and elaborates on some promising strategies that would bring us closer to uncovering of the vital relationships between genotype and phenotype. After a few educational primers, the book continues with sections on sequence homology and alignment, phylogenetic methods to study genome evolution, methodologies for evaluating selective pressures on genomic sequences as well as genomic evolution in light of protein domain architecture and transposable elements, population genomics and other omics, and discussions of current bottlenecks in handling and analyzing genomic data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that lead to the best results. Authoritative and comprehensive, Evolutionary Genomics: Statistical and Computational Methods, Second Edition aims to serve both novices in biology with strong statistics and computational skills, and molecular biologists with a good grasp of standard mathematical concepts, in moving this important field of study forward
Artificial Neural Networks in Agriculture
Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible
Microfluidics and Nanofluidics Handbook
The Microfluidics and Nanofluidics Handbook: Two-Volume Set comprehensively captures the cross-disciplinary breadth of the fields of micro- and nanofluidics, which encompass the biological sciences, chemistry, physics and engineering applications. To fill the knowledge gap between engineering and the basic sciences, the editors pulled together key individuals, well known in their respective areas, to author chapters that help graduate students, scientists, and practicing engineers understand the overall area of microfluidics and nanofluidics. Topics covered include Finite Volume Method for Numerical Simulation Lattice Boltzmann Method and Its Applications in Microfluidics Microparticle and Nanoparticle Manipulation Methane Solubility Enhancement in Water Confined to Nanoscale Pores Volume Two: Fabrication, Implementation, and Applications focuses on topics related to experimental and numerical methods. It also covers fabrication and applications in a variety of areas, from aerospace to biological systems. Reflecting the inherent nature of microfluidics and nanofluidics, the book includes as much interdisciplinary knowledge as possible. It provides the fundamental science background for newcomers and advanced techniques and concepts for experienced researchers and professionals
Evolutionary Genomics
This open access book addresses the challenge of analyzing and understanding the evolutionary dynamics of complex biological systems at the genomic level, and elaborates on some promising strategies that would bring us closer to uncovering of the vital relationships between genotype and phenotype. After a few educational primers, the book continues with sections on sequence homology and alignment, phylogenetic methods to study genome evolution, methodologies for evaluating selective pressures on genomic sequences as well as genomic evolution in light of protein domain architecture and transposable elements, population genomics and other omics, and discussions of current bottlenecks in handling and analyzing genomic data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that lead to the best results. Authoritative and comprehensive, Evolutionary Genomics: Statistical and Computational Methods, Second Edition aims to serve both novices in biology with strong statistics and computational skills, and molecular biologists with a good grasp of standard mathematical concepts, in moving this important field of study forward
Network-based visualisation and analysis of next-generation sequencing (NGS) data
Next-generation sequencing (NGS) technologies have revolutionised research into
nature and diversity of genomes and transcriptomes. Since the initial description of
these technology platforms over a decade ago, massively parallel RNA sequencing
(RNA-seq) has driven many advances in the characterization and quantification of
transcriptomes. RNA-seq is a powerful gene expression profiling technology
enabling transcript discovery and provides a far more precise measure of the levels of
transcripts and their isoforms than other methods e.g. microarray.
However, the analysis of RNA-seq data remains a significant challenge for many
biologists. The data generated is large and the tools for its assembly, analysis and
visualisation are still under development. Assemblies of reads can be inspected using
tools such as the Integrative Genomics Viewer (IGV) where visualisation of results
involves ‘stacking’ the reads onto a reference genome. Whilst sufficient for many
needs, when the underlying variance of the genome or transcript assemblies is
complex, this visualisation method can be limiting; errors in assembly can be
difficult to spot and visualisation of splicing events may be challenging.
Data visualisation is increasingly recognised as an essential component of genomic
and transcriptomic data analysis, enabling large and complex datasets to be better
understood. An approach that has been gaining traction in biological research is
based on the application of network visualisation and analysis methods. Networks
consist of nodes connected by edges (lines), where nodes usually represent an entity
and edge a relationship between them. These are now widely used for plotting
experimentally or computationally derived relationships between genes and proteins.
The overall aim of this PhD project was to explore the use of network-based
visualisation in the analysis and interpretation of RNA-seq data. In chapter 2, I
describe the development of a data pipeline that has been designed to go from ‘raw’
RNA-seq data to a file format which supports data visualisation as a ‘DNA assembly
graph’. In DNA assembly graphs, nodes represent sequence reads and edges denote a
homology between reads above a defined threshold. Following the mapping of reads
to a reference sequence and defining which reads a map to a given loci, pairwise
sequence alignments are performed between reads using MegaBLAST. This provides
a weighted similarity score that is used to define edges between reads. Visualisation
of the resulting networks is then carried out using BioLayout Express3D that can
render large networks in 3-D, thereby allowing a better appreciation of the often-complex
network structure. This pipeline has formed the basis for my subsequent
work on the exploring and analysing alternative splicing in human RNA-seq data. In
the second half of this chapter, I provide a series of tutorials aimed at different types
of users allowing them to perform such analyses. The first tutorial is aimed at
computational novices who might want to generate networks using a web-browser
and pre-prepared data. Other tutorials are designed for use by more advanced users
who can access the code for the pipeline through GitHub or via an Amazon Machine
Image (AMI).
In chapter 3, the utility of network-based visualisations of RNA-seq data is explored
using data processed through the pipeline described in Chapter 2. The aim of the
work described in this chapter was to better understand the basic principles and
challenges associated with network visualisation of RNA-seq data, in particular how
it could be used to visualise transcript structure and splice-variation. These analyses
were performed on data generated from four samples of human fibroblasts taken at
different time points during their entry into cell division. One of the first challenges
encountered was the fact that the existing network layout algorithm (Fruchterman-
Reingold) implemented within BioLayout Express3D did not result in an optimal
layout of the unusual graph structures produced by these analyses. Following the
implementation of the more advanced layout algorithm FMMM within the tool,
network structure could be far better appreciated. Using this layout method, the
majority of genes sequenced to an adequate depth assemble into networks with a
linear ‘corkscrew’ appearance and when representing single isoform transcripts add
little to existing views of these data. However, in a small number of cases (~5%), the
networks generated from transcripts expressed in human fibroblasts possess more
complex structures, with ‘loops’, ‘knots’ and multiple ends being observed. In a
majority of cases examined, these loops were associated with alternative splicing
events, a fact confirmed by RT-PCR analyses. Other DNA assembly networks
representing the mRNAs for genes such as MKI67 showed knot-like structures,
which was found to be due to the presence of repetitive sequence within an exon of
the gene. In another case, CENPO the unusual structure observed was due to reads
derived from an overlapping gene of ADCY3 gene present on the opposite strand
with reads being wrongly mapped to CENPO. Finally, I explored the use of a
network reduction strategy as an approach to visualising highly expressed genes such
as GAPDH and TUBA1C. Having successfully demonstrated the utility of networks
in analysing transcript isoforms in data derived from a single cell type I set out to
explore its utility in analysing transcript variation in tissue data where multiple
isoforms expressed by different cells within the tissue might be present in a given
sample.
In chapter 4, I explore the analysis of transcript variation in an RNA-seq dataset
derived from human tissue. The first half of this chapter describes the quality control
of these data again using a network-based approach but this time based the
correlation in expression between genes and samples. Of the 95 samples derived
from 27 human tissues, 77 passed the quality control. A network was constructed
using a correlation threshold of r ≥ 0.9, which comprised 6,109 nodes (genes) and
1,091,477 edges (correlations) and clustered. Subsequently, the profile and gene
content of each cluster was examined and enrichment of GO terms analysed. In the
second half of this chapter, the aim was to detect and analyse alternative splicing
events between different tissues using the rMATS tool. By using a false-discovery
rate (FDR) cut-off of < 0.01, I found that in comparisons of brain vs. heart, brain vs.
liver and heart vs. liver, the program reported 4,992, 4,804 and 3,990 splicing events,
respectively. Of these events, only 78 splicing events (52 genes) with more than 50%
of exon inclusion level and expression level more than FPKM 30. To further explore
the sometimes-complex structure of transcripts diversity derived from tissue, RNAseq
assembly networks for KLC1, SORBS2, GUK1, and TPM1 were explored. Each
of these networks showed different types of alternative splicing events and it was
sometimes difficult to determine the isoforms expressed between tissues using other
approaches. For instance, there is an issue in visualising the read assembly of long
genes such as KLC1 and SORBS2, using a Sashimi plots or even Vials, just because
of the number of exons and the size of their genomic loci. In another case of GUK1,
tissue-specific isoform expression was observed when a network of three tissues was
combined. Arguably the most complex analysis is the network of TPM1 where the
uniquification step was employed for this highly expressed gene.
In chapter 5, I perform a usability testing for NGS Graph Generator web application
and visualising RNA-seq assemblies as a network using BioLayout Express3D. This
test was important to ensure that the application is well received and utilised by the
user. Almost all participants of this usability test agree that this application would
encourage biologists to visualise and understand the alternative splicing together
with existing tools. The participants agreed that Sashimi plots rather difficult to view
and visualise and perhaps would lose something interesting features. However, there
were also reviews of this application that need improvements such as the capability
to analyse big network in a short time, side-by-side analysis of network with Sashimi
plot and Ensembl. Additional information of the network would be necessary to
improve the understanding of the alternative splicing.
In conclusion, this work demonstrates the utility of network visualisation of RNAseq
data, where the unusual structure of these networks can be used to identify issues
in assembly, repetitive sequences within transcripts and splice variation. As such,
this approach has the potential to significantly improve our understanding of
transcript complexity. Overall, this thesis demonstrates that network-based
visualisation provides a new and complementary approach to characterise alternative
splicing from RNA-seq data and has the potential to be useful for the analysis and
interpretation of other kinds of sequencing data
Mathematical Modelling of Spatially Coherent Transcription
Genetics and epigenetics are widely expected to revolutionise our understanding of health and
disease. However any attempt to extract relevant information from noisy data requires a
combination of modelling and statistical techniques. Given the number of genes and the complexity
involved in the genome, sophisticated methods will be needed to properly capture the information
that is contained.
Many mechanisms and variables can affect and control the expression of a gene. In this
thesis, it is specifically spatially coherent variations in transcription which are investigated. Several
different areas were examined, producing a broad set of results. Important findings include the
demonstration of spatial coherence as the result of epigenetic effects, the creation and validation of
a technique to detect spatial coherence, and the extension of spatial modelling to epigenetic data.
Other important results include the detection of spatial coherence variation due to confounding
variables (PMI and neuronal concentration) and the development of new spatial modelling
techniques. The results indicate that spatial modelling provides a useful approach to investigating
unusual and unknown aspects of epigenetic and transcriptional regulation
Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain
The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio