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- Diamanti, Klev, 1987-
(författare)
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Integrating multi-omics for type 2 diabetes : Data science and big data towards personalized medicine
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Type 2 diabetes (T2D) is a complex metabolic disease characterized by multi-tissue insulin resistance and failure of the pancreatic β-cells to secrete sufficient amounts of insulin. Cells recruit transcription factors (TF) to specific genomic loci to regulate gene expression that consequently affects the protein and metabolite abundancies. Here we investigated the interplay of transcriptional and translational regulation, and its impact on metabolome and phenome for several insulin-resistant tissues from T2D donors. We implemented computational tools and multi-omics integrative approaches that can facilitate the selection of candidate combinatorial markers for T2D.We developed a data-driven approach to identify putative regulatory regions and TF-interaction complexes. The cell-specific sets of regulatory regions were enriched for disease-related single nucleotide polymorphisms (SNPs), highlighting the importance of such loci towards the genomic stability and the regulation of gene expression. We employed a similar principle in a second study where we integrated single nucleus ribonucleic acid sequencing (snRNA-seq) with bulk targeted chromosome-conformation-capture (HiCap) and mass spectrometry (MS) proteomics from liver. We identified a putatively polymorphic site that may contribute to variation in the pharmacogenetics of fluoropyrimidines toxicity for the DPYD gene. Additionally, we found a complex regulatory network between a group of 16 enhancers and the SLC2A2 gene that has been linked to increased risk for hepatocellular carcinoma (HCC). Moreover, three enhancers harbored motif-breaking mutations located in regulatory regions of a cohort of 314 HCC cases, and were candidate contributors to malignancy.In a cohort of 43 multi-organ donors we explored the alternating pattern of metabolites among visceral adipose tissue (VAT), pancreatic islets, skeletal muscle, liver and blood serum samples. A large fraction of lysophosphatidylcholines (LPC) decreased in muscle and serum of T2D donors, while a large number of carnitines increased in liver and blood of T2D donors, confirming that changes in metabolites occur in primary tissues, while their alterations in serum consist a secondary event. Next, we associated metabolite abundancies from 42 subjects to glucose uptake, fat content and volume of various organs measured by positron emission tomography/magnetic resonance imaging (PET/MRI). The fat content of the liver was positively associated with the amino acid tyrosine, and negatively associated with LPC(P-16:0). The insulin sensitivity of VAT and subcutaneous adipose tissue was positively associated with several LPCs, while the opposite applied to branch-chained amino acids. Finally, we presented the network visualization of a rule-based machine learning model that predicted non-diabetes and T2D in an “unseen” dataset with 78% accuracy.
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- Rivas-Carrillo, Salvador Daniel, et al.
(författare)
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Broad-scale in silico assessment retroviral exaptated gene : syncytin
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Syncytin is a fossil protein exapted from retroviruses that fulfills a pivotal role during trophoblast implantation and placental metabolite exchange. However, little is yet known about the distribution of syncytin across vertebrates. Here, we searched a library of more than 150 high-quality assemblies across 17 taxonomical orders for syncytin homologs. We identified and syntenically aligned over 300 loci insertions, including not previously known insertions. Additionally, we predicted the tridimensional structures of the recover sequences using AlphaFold2. Sequence conservation and 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. This research has widened our knowledge about the physiology of placentation through a better understanding of the evolutionary role of syncytin.
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- Rivas-Carrillo, Salvador Daniel, et al.
(författare)
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Chapulin : a leap forward on mobile element and structural variant identification
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Transposable elements represent a substantial proportion of eukaryotic genomes, where they can disrupt or enhance gene expression on the host. However, identification at population scale where often short sequencing signals are available is challenging. Current approaches rely on parsing sequence alignment files looking for anomalies on read length, read orientation and read depth, but they are often slow and complicated to install. Here, we present the Chapulin, a portable cross-platform, open-sourced Rust application for structural variant identification and characterization, including transposable elements. By using concurrent computing and native execution, Chapulin identifies a large fraction of mobile element insertions while outperforming existing transposable element tools. Chapulin was designed to be versatile and robust, in order to accommodate the demands of current data, such as population-scale studies or clinical samples
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- Rivas-Carrillo, Salvador Daniel, et al.
(författare)
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MindReader : unsupervised electroencephalographic reader
- 2023
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Background: Electroencephalogram (EEG) interpretation plays a critical role in the clinical assessment of neurological conditions, including epilepsy. Manual analysis requires highly specialized and heavily trained personnel. Moreover, the rate of capturing abnormal events makes interpretation time-consuming, resource-hungry, and, overall, an expensive process.Automatic detection offers the potential to improve the quality of patient care by shortening the time to diagnosis, managing big data, and optimizing the allocation of human resources.Findings: We present MindReader, an unsupervised method for EEG signals. First, MindReader processes the signal through an autoencoder in order to detect EEG abnormalities. Next, patterns are hypothesized by a Hidden Markov Model. Our algorithm automatically generates labels for non-pathological phases, thus reducing the search space for trained personnel.Conclusions: MindReader is effective in detecting EEG abnormalities in focal and generalized epilepsy.
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- Rivas-Carrillo, Salvador Daniel
(författare)
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The revolutionary partnership of computation and biology
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)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.
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