| 1. |
- Zhang, Xueli, 1991-
(author)
-
Biomarkers for Diagnosis, Therapy and Prognosis in Colorectal Cancer : a study from databases, machine learning predictions to laboratory confirmations
- 2020
-
Doctoral thesis (other academic/artistic)abstract
- Colorectal cancer (CRC) is one of the leading causes of cancer death worldwide. Early diagnosis and better therapy response have been believed to be associated with better prognosis. CRC biomarkers are considered as precise indicators for the early diagnosis and better therapy response. It is, therefore, of importance to find out, analyze and evaluate the CRC biomarkers to further provide the more precis evidence for predicting novel potential biomarkers and eventually to improve early diagnosis, personalized therapy and prognosis for CRC.In this study, we started with creating and establishing a CRC biomarker database. (CBD: http://sysbio.suda.edu.cn/CBD/index.html) In the CBD database, there were 870 reported CRC biomarkers collected from the published articles in PubMed. In this version of the CBD, CRC biomarker data was carefully collected, sorted, displayed, and analyzed. The major applications of the CBD are to provide 1) the records of CRC biomarkers (DNA, RNA, protein and others) concerning diagnosis, treatment and prognosis; 2) the basic and clinical research information concerning the CRC biomarkers; 3) the primary results for bioinformatics and biostatics analysis of the CRC biomarkers; 4) downloading/uploading the biomedicine information for CRC biomarkers.Based on our CBD and other public databases, we further analyzed the presented CRC biomarkers (DNAs, RNAs, proteins) and predicted novel potential multiple biomarkers (the combination of single biomarkers) with biological networks and pathways analysis for diagnosis, therapy response and prognosis in CRC. We found several hub biomarkers and key pathways for the diagnosis, treatment and prognosis in CRC. Receiver operating characteristic (ROC) test and survival analysis by microarray data revealed that multiple biomarkers could be better biomarkers than the single biomarkers for the diagnosis and prognosis of CRC.There are 62 diagnosis biomarkers for colon cancer in our CBD. In the previous studies, we found these present biomarkers were not enough to improve significantly the diagnosis of colon cancer. In order to find out novel biomarkers for the colon cancer diagnosis, we have performed /machine learning (ML) techniques such as support vector machine (SVM) and regression tree to predict candidate to discover diagnostic biomarkers for colon cancer. Based on the protein-protein interaction (PPI) network topology features of the identified biomarkers, we found 12 protein biomarkers which were considered as the candidate colon cancer diagnosis biomarkers. Among these protein biomarkers Chromogranin-A (CHGA) was the most powerful biomarker, which showed good performance in bioinformatics test and Immunohistochemistry(IHC). We are now expanding this study to CRC.Expression of CHGA protein in colon cancer was further verified with a novel logistic regressionbased meta-analysis, and convinced as a valuable diagnostic biomarker as compared with the typical diagnostic biomarkers, such as TP53, KRAS and MKI67.microRNAs (miRNAs/miRs) have been considered as potential biomarkers. A novel miRNA-mRNA interaction network-based model was used to predict miRNA biomarkers for CRC and found that miRNA-186-5p, miRNA-10b-5p and miRNA-30e-5p might be the novel biomarkers for CRC diagnosis. In conclusion, we have created a useful CBD database for CRC biomarkers and provided detailed information for how to use the CBD in CRC biomarker investigations. Our studies have been focusing on the biomarkers in diagnosis, therapy and prognosis. Based on our CBD and other powerful cancer associated databases, ML has been used to analyze the characteristics of the CRC biomarkers and predict novel potential CRC biomarkers. The predicted potential biomarkers were further confirmed at biomedical laboratory.
|
|
| 2. |
- Pilstål, Robert, 1985-
(author)
-
On protein structure, function and modularity from an evolutionary perspective
- 2018
-
Doctoral thesis (other academic/artistic)abstract
- We are compounded entities, given life by a complex molecular machinery. When studying these molecules we have to make sense of a diverse set of dynamical nanostructures with wast and intricate patterns of interactions. Protein polymers is one of the major groups of building blocks of such nanostructures which fold up into more or less distinct three dimensional structures. Due to their shape, dynamics and chemical properties proteins are able to perform a plethora of specific functions essential to all known cellular lifeforms.The connection between protein sequence, translated into protein structure and in the continuation into protein function is well accepted but poorly understood. Malfunction in the process of protein folding is known to be implicated in natural aging, cancer and degenerative diseases such as Alzheimer's.Protein folds are described hierarchically by structural ontologies such as SCOP, CATH and Pfam all which has yet to succeed in deciphering the natural language of protein function. These paradigmatic views centered on protein structure fail to describe more mutable entities, such as intrinsically disordered proteins (IDPs) which lack a clear defined structure.As of 2012, about two thirds of cancer patients was predicted to survive past 5 years of diagnosis. Despite this, about a third do not survive and numerous of successfully treated patients suffer from secondary conditions due to chemotherapy, surgery and the like. In order to handle cancer more efficiently we have to better understand the underlying molecular mechanisms.Elusive to standard methods of investigation, IDPs have a central role in pathology; dysfunction in IDPs are key factors in cellular system failures such as cancer, as many IDPs are hub regulators for major cell functions. These IDPs carry short conserved functional boxes, that are not described by known ontologies, which suggests the existence of a smaller entity. In an investigation of a pair of such boxes of c-MYC, a plausible structural model of its interacting with Pin1 emerged, but such a model still leaves the observer with a puzzle of understanding the actual function of that interaction.If the protein is represented as a graph and modeled as the interaction patterns instead of as a structural entity, another picture emerges. As a graph, there is a parable from that of the boxes of IDPs, to that of sectors of allosterically connected residues and the theory of foldons and folding units. Such a description is also useful in deciphering the implications of specific mutations.In order to render a functional description feasible for both structured and disordered proteins, there is a need of a model separate from form and structure. Realized as protein primes, patterns of interaction, which has a specific function that can be defined as prime interactions and context. With function defined as interactions, it might be possible that the discussion of proteins and their mechanisms is thereby simplified to the point rendering protein structural determination merely supplementary to understanding protein function.
|
|
| 3. |
- Tsirigos, Konstantinos, 1982-
(author)
-
Bioinformatics Methods for Topology Prediction of Membrane Proteins
- 2017
-
Doctoral thesis (other academic/artistic)abstract
- Membrane proteins are key elements of the cell since they are associated with a variety of very important biological functions crucial to its survival. They are implicated in cellular recognition and adhesion, act as molecular receptors, transport substrates through membranes and exhibit specific enzymatic activity.This thesis is focused on integral membrane proteins, most of which contain transmembrane segments that form an alpha helix and are composed of mainly hydrophobic residues, spanning the lipid bilayer. A more specialized and less well-studied case, is the case of integral membrane proteins found in the outer membrane of Gram-negative bacteria and (presumably) in the outer envelope of mitochondria and chloroplasts, proteins whose transmembrane segments are formed by amphipathic beta strands that create a closed barrel (beta-barrels). The importance of transmembrane proteins, as well as the inherent difficulties in crystallizing and obtaining three-dimensional structures of these, dictates the need for developing computational algorithms and tools that will allow for a reliable and fast prediction of their structural and functional features. In order to elucidate their function, we must acquire knowledge about their structure and topology with relation to the membrane. Therefore, a large number of computational methods have been developed in order to predict the transmembrane segments and the overall topology of transmembrane proteins. In this thesis, I initially describe a large-scale benchmark of many topology prediction tools in order to devise a strategy that will allow for better detection of alpha-helical membrane proteins in a proteome. Then, I give a description of construction of improved machine-learning algorithms and computer software for accurate topology prediction of transmembrane proteins and discrimination of such proteins from non-transmembrane proteins. Finally, I introduce a fast way to obtain a position-specific scoring matrix, which is essential for modern topology prediction methods.
|
|
| 4. |
- Rivas-Carrillo, Salvador Daniel
(author)
-
The revolutionary partnership of computation and biology
- 2023
-
Doctoral thesis (other academic/artistic)abstract
- The organization of living beings is complex. Science uses modeling in order to gain a deeper understanding, and to be able to manipulate the processes of living organisms. To this purpose, I used and developed computational tools to investigate and model different relevant biological phenomena. In paper I, I utilized whole-genome data from wild and domesticated European rabbit (Oryctolagus cuniculus sp.) populations to identify segregating insertions of endogenous retroviruses and compare their variation along the host phylogeny and domestication history. The results from this study highlight the importance of genomic modeling beyond reference organisms and reference individuals, and provide deep insights regarding strategies for variant analyses in host population comparative genomics. In paper IV, I studied the process of exaptation of foreign genetic elements at broad-scale by observing the presence and characteristics of retroviral env gene, syncytin, across vertebrates. I searched a library of more than 150 chromosome-length assemblies covering 17 taxonomical orders for syncytin homologs, where I identified and syntenically aligned over 300 loci insertions, including not previously known insertions. Additionally, three-dimensional structures of the recovered sequences were predicted using AlphaFold2. Phylogenomics analyses suggest a complex dynamic of multiple retroviral insertions at different time points with sequence conservation specific to clades that share a similar histo-physiological placental type.In paper II, I expanded the scope to encompass translational medicine by developing an unsupervised machine learning methodology for detecting anomalies in biomedical signals, MindReader, which I applied primarily to electroencephalogram. In paper III, I developed a hidden Markov model implementation that includes a hypothesis generator for stream time-domain signals, which is used as a dependency for paper II. The work in this thesis substantiates that a combination of biological knowledge, cutting-edge technology, and robust algorithmic design constitute the primordial factors for scientific advancement.
|
|
