State of the art in silico tools for the study of signaling pathways in cancer

In the last several years, researchers have exhibited an intense interest in the evolutionarily conserved signaling pathways that have crucial roles during embryonic development. Interestingly, the malfunctioning of these signaling pathways leads to several human diseases, including cancer. The chemical and biophysical events that occur during cellular signaling, as well as the number of interactions within a signaling pathway, make these systems complex to study. In silico resources are tools used to aid the understanding of cellular signaling pathways. Systems approaches have provided a deeper knowledge of diverse biochemical processes, including individual metabolic pathways, signaling networks and genome-scale metabolic networks. In the future, these tools will be enormously valuable, if they continue to be developed in parallel with growing biological knowledge. In this study, an overview of the bioinformatics resources that are currently available for the analysis of biological networks is provided

Biosemiosis and Causation: Defending Biosemiotics Through Rosen's Theoretical Biology, or, Integrating Biosemiotics and Anticipatory Systems Theory

The fracture in the emerging discipline of biosemiotics when the code biologist Marcello Barbieri claimed that Peircian biosemiotics is not genuine science raises anew the question: What is science? When it comes to radically new approaches in science, there is no simple answer to this question, because if successful, these new approaches change what is understood to be science. This is what Galileo, Darwin and Einstein did to science, and with quantum theory, opposing interpretations are not merely about what theory is right, but what is real science. Peirce's work, as he acknowledged, is really a continuation of efforts of Schelling to challenge the heritage of Newtonian science for the very good reason that the deep assumptions of Newtonian science had made sentient life, human consciousness and free will unintelligible, the condition for there being science. Pointing out the need for such a revolution in science has not succeeded as a defence of Peircian biosemiotics, however. In this paper, I will defend the scientific credentials of Peircian biosemiotics by relating it to the theoretical biology of the bio-mathematician, Robert Rosen. Rosen's relational biology, focusing on anticipatory systems and giving a place to final causes, should also be seen as a rigorous development of the Schellingian project to conceive nature in such a way that the emergence of sentient life, mind and science are intelligible. Rosen has made a very strong case for the characterization of his ideas as a real advance not only in science, but in how science should be understood, and I will argue that it is possible to provide a strong defence of Peircian biosemiotics as science through Rosen's defence of relational biology. In the process, I will show how biosemiotics can and should become a crucial component of anticipatory systems theory

Biogeographical Patterns of Soil Microbial Communities: Ecological, Structural, and Functional Diversity and their Application to Soil Provenance

The current ecological hypothesis states that the soil type (e.g., chemical and physical properties) determines which microbes occupy a particular soil and provides the foundation for soil provenance studies. As human profiles are used to determine a match between evidence from a crime scene and a suspect, a soil microbial profile can be used to determine a match between soil found on the suspect’s shoes or clothing to the soil at a crime scene. However, for a robust tool to be applied in forensic application, an understanding of the uncertainty associated with any comparisons and the parameters that can significantly influence variability in profiles needs to be determined. This study attempted to address some of the most obvious uncertainties of soil provenance applications such as spatial variability, temporal variability, and marker selection (i.e., taxa discrimination). Pattern analysis was used to validate the ecological theories driving the soil microbial biogeography. Elucidating soil microbial communities’ spatial and temporal variability is critical to improve our understanding of the factors regulating their structure and function. Microbial profiling and bioinformatics analyses of the soil community provided a rapid method for soil provenance that can be informative, easier to perform, and more cost effective than approaches using traditional physico-chemical data. This study also showed that stable profiles may allow comparison between evidence and a possible crime scene despite the time lapse (4 years) between sample collections, however, this is dependent on the analysis method, site, vegetation, and level of disturbance. Marker selection was also an important consideration for profiling. Even though Fungi look promising for single taxon soil discrimination, the additional markers can help discriminate between a wide variety of soil types. As in human identification, the more DNA markers queried the greater the discrimination power. Lastly, this study illustrated a novel method to query the iron relating genes and ability to design a novel marker that can easily be used to profile the functional diversity of a soil community to enhance soil classification. Overall this research demonstrated the potential and effectiveness of using microbial DNA from soil, not just for comparison, but also for intelligence gathering to pinpoint the geographic origin of the soil

Lamin A/C as a prognostic biomarker in colorectal cancer

Lamins A and C (A-type lamins) are type V nuclear intermediate filament proteins which form a complex meshwork underlining the inner nuclear membrane termed the nuclear lamina. A-type lamins have been implicated in NA replication, regulation of gene transcription, apoptosis and regulating the activity of several growth promoters. As such mutations in A-type lamins give rise to diverse range of genetic diseases related to premature ageing and it has been speculated but not proven that expression of A-type lamins may influence tumour progression. To test this hypothesis, a large (n=656) retrospective archive of colorectal cancer specimens from the Netherlands Cohort Study on Diet and Cancer was screened for the expression of A-type lamins. Data clearly show that patients lacking A- type lamin expression in their tumours have a significantly better prognosis compared to clinicopathologically similar patients expressing A-type lamins [HR = 0.59, (95% CI: 0.409 - 0.858). p=0.006].Data also show that expression of lamin A in an in vitro colorectal cancer model leads to genome-wide changes and in particular promotes a significant down- regulation in Bone Morphogenic Protein 4 (BMP4) a member of the TGF-β superfamily which has already been linked to the pathogenesis of some solid tumours and also show that A-type lamin expression acts as a negative regulator of BMP4 mediated growth suppression. A-type lamin expression also results in a concomitant up-regulation in expression of the actin bundling protein T-plastin and down-regulation in expression of the cell adhesion molecule E-cadherin leading to a more motile, less adherent cellular phenotype in lamin A expressing cells. Thus expression of A-type lamins may increase the risk of death in colorectal cancer because its presence gives rise to increased invasiveness due to the negative regulation of BMP mediated growth suppression. This is the first evidence directly linking the expression of A-type lamins to mechanisms promoting tumour progression

Systems Analytics and Integration of Big Omics Data

A “genotype"" is essentially an organism's full hereditary information which is obtained from its parents. A ""phenotype"" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This “Big Data” is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene–environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome

Cardiovascular Risk Genes in Prevention and Treatment Response

GENERAL AIM: To investigate how common single-nucleotide-polymorphisms (SNPs) that associate with cardiovascular disease (CVD) could be used in prevention and treatment of CVD. SUBJECTS: Subjects from the population-based Malmö-Diet-and-Cancer-(MDC)-Study (n=30447) and hypertensives from the Nordic-Diltiazem-(NORDIL)-Study (n=10881). METHODS AND RESULTS: A nine-SNP-lipid-genetic-risk-score was related to fluvastatin treatment-response in 395 MDC subjects with asymptomatic carotid atherosclerosis. In women, a higher score (conferring unfavorable baseline-lipid-levels) correlated with HDL-increase (P=0.001), explaining 11.6-12.9% of the variance in HDL-change. A 13-SNP-myocardial-infarction-(MI)-genetic-risk-score was related to carotid atherosclerosis-markers in 4022 MDC-subjects. The MI-gene-score associated with carotid-bulb-intima-media-thickness (IMT) (beta=0.038 standard deviations of IMT per MI-gene-score-quintile; P-trend=0.005) and plaque (odds-ratio per MI-gene-score-quintile=1.11; 95% confidence interval (CI):1.04-1.18; P=0.001) in multivariable models. It was tested if eight blood-pressure-associated SNPs affected antihypertensive treatment-response in 3863 Swedish hypertensives from NORDIL. No robust associations were identified. Finally, interactions between life-style-factors and the CVD-SNP rs4977574 on chromosome 9p21 were evaluated in 24944 MDC-subjects during 15 years follow-up. There were interactions between rs4977574 and smoking on incident CAD (P=0.035) and CVD-mortality (P=0.012). The risk conferred by rs4977574 in never-smokers (n=9642; Hazard-ratio(HR) per risk-allele(CAD)=1.26; 95%CI:1.13-1.40; HR per risk-allele(CVD-mortality)=1.40; 95%CI:1.20-1.63) was attenuated in smokers (n=7000; HR per risk-allele(CAD)=1.05; 95%CI:0.95-1.16; HR per risk-allele(CVD-mortality)=1.08; 95%CI:0.94-1.23). CONCLUSIONS: CVD-genetics identifies subjects with markers of subclinical atherosclerosis, suggesting that early atherosclerosis-prevention may be targeted to such individuals. Smoking attenuates the relative influence of the thus far strongest identified polygenic CVD-risk-locus, implying potential utility of common CVD-genetics in mainly conventional lower-risk subjects. Lipid-polymorphisms may predict statin-induced HDL-increase in women, but eight blood-pressure-SNPs did not affect antihypertensive treatment-response

Hierarchy and CIS-Regulation in Drosophila Segmentation: Rules for Pattern Formation and Clues to Evolution

In few systems is it possible to analyze the global cis-regulatory structure of developmental transcription networks. One system where this is in principle possible is segmentation in Drosophila melanogaster, although to date such an undertaking has not been attempted. Here using computational algorithms to analyze the transcriptional regulatory regions of genes of the gap and pair rule classes such an analysis is carried out. Computational analysis, transgenic reporter element assays, site directed mutagenesis, genetics, and time courses of in situ hybridizations of central genes in carefully staged embryos are combined to understand how the cis-elements function together to achieve patterning of the anterior posterior axis. The transition from the non-periodic gap patterns to the seven striped periodic patterns of the pair rule genes is analyzed in detail. This step in the genetic hierarchy is of particular interest as it generates the segmental pattern that underlies the Drosophila body plan. The analysis clarifies the primary and secondary pair rule classification system and suggests certain organizational principles in pair rule cis-regulation

Computational analysis of alternative splicing in human and mice

Im ersten Teil wurden Transkript-Spleißstellen untersucht, mit dem Ziel, alternative und Referenzspleißstellen zu unterscheiden. Die Ergebnisse belegen, dass sich beide Klassen von Spleißstellen durch einen Spleißstellen-Score und vermehrtes Auftreten von Spleißfaktor-Bindemotiven in Umgebung der Spleißstellen abgrenzen lassen. Zusätzlich konnte eine positive Korrelation zwischen der Häufigkeit der Nutzung bestimmter Spleißstellen und dem Spleißstellen-Score in beiden Vergleichsklassen nachgewiesen werden. Diese Abhängigkeit impliziert, dass die Genauigkeit der Annotation alternativer Spleißvarianten mit der Anzahl beobachteter Transkripte steigt. Im zweiten Teil wurde das Spleißsignalmotiv GYNNGY untersucht, welches mehr als 40% aller überlappenden Donor-Spleißsignale ausmacht. Mittels in silico Analysen und experimenteller Validierung wurde die Plausibilität dieses subtilen Spleißmusters bestätigt. Der Vergleich mit anderen humanen Spleißvarianten sowie mit Tandem Donoren in Maus-Transkripten zeigte zudem ausgeprägte Unterschiede bezüglich des Spleißstellen-Scores, der Konservierung, sowie dem Vorkommen von Spleißfaktoren-Bindemotiven. Die Verschiebung des Leserasters durch alternatives Spleißen an GYNNGY-Donoren lässt auf eine komplexe Rolle im RNA-Reifungsprozess schließen. Im dritten Teil wurden Reaktionen des spleißosomalen Makrokomples aus publizierten, experimentellen Daten zusammengestellt und mit Hilfe der Petri-Netz-Theorie in einem qualitativen Modell dargestellt. Unter Annahme eines Steady-State Systems wurden minimale, semipositive T-Invarianten berechnet und zur Validierung des Modells herangezogen. Auf Grundlage der vollständigen Abdeckung des Reaktionsnetzwerks mit T-Invarianten konnten weitere Strukturmerkmale, wie Maximal-Gemeinsame Transitions.Mengen und T-Cluster berechnet werden, welche wichtige Stadien des Spleißosomaufbaus widerspiegeln

The Dark side of Obesity: Multi-omics analysis of the dysmetabolic morbidities spectrum

Obesity is one of the most prevalent clinical conditions worldwide and is associated with a wide spectrum of dysmetabolic comorbidities. Complex cardio-metabolic disease cohorts, such as obesity cohorts are characterised by population heterogeneity, multiple underlying diseases status and different comorbidities’ treatment regiments. The systematic collection of multiple types of clinical and biological data from such cohorts and the data-analysis in an integrative manner is a challenging task due to the variables’ dimensionality and the lack of standardised know-how of post-processing.The main resource of this thesis has been the BARIA cohort, a detailed collection over time of multiple omics and demographic data from participants in bariatric surgery. BARIA datasets included plasma metabolites, RNA from hepatic, jejunal, mesenteric and subcutaneous adipose tissues and gut microbial metagenome, besides biometric data. The work presented in this thesis included the development of a systems biology integrative framework based on BARIA that (i) utilised unsupervised machine learning algorithms, self-organizing maps in particular, and multi-omics integrative frameworks, the DIABLO library, in order to stratify the BARIA heterogeneous obesity cohort and predict the bariatric surgery’s outcome. The thesis covered how BARIA can be the onset for (ii) studying molecular mechanisms related to type 2 diabetes (T2D) and G-protein coupled receptors (GPCRs) and for identifying a minimal set of biomarkers for obesity’s comorbidities such as (iii) non-alcoholic fatty liver disease (NAFL) and (iv) gallstones formation after bariatric surgery.The results indicated that the metabotypes comprising a bariatric surgery cohort exhibited a concrete metabolic status and different responses over time after the bariatric surgery. It has been demonstrated how obesity and T2D associated metabolites, such as 3-hydroxydecanoate, can increase inflammatory responses via GPCRs molecular activation and signalling. Last but not least, minimal sets of both evasive and non-evasive multi-omic discriminatory biomarkers for obesity’s dysmetabolic morbidities (NAFLD and gallstones after bariatric surgery) were obtained. Taking into consideration all the findings, this thesis presented how data-driven approaches can be used for studying in-depth heterogeneous cohorts, hereby facilitating early diagnosis and enabling potential preventive actions