Bio-JOIE: Joint Representation Learning of Biological Knowledge Bases

The widespread of Coronavirus has led to a worldwide pandemic with a high mortality rate. Currently, the knowledge accumulated from different studies about this virus is very limited. Leveraging a wide-range of biological knowledge, such as gene ontology and protein-protein interaction (PPI) networks from other closely related species presents a vital approach to infer the molecular impact of a new species. In this paper, we propose the transferred multi-relational embedding model Bio-JOIE to capture the knowledge of gene ontology and PPI networks, which demonstrates superb capability in modeling the SARS-CoV-2-human protein interactions. Bio-JOIE jointly trains two model components. The knowledge model encodes the relational facts from the protein and GO domains into separated embedding spaces, using a hierarchy-aware encoding technique employed for the GO terms. On top of that, the transfer model learns a non-linear transformation to transfer the knowledge of PPIs and gene ontology annotations across their embedding spaces. By leveraging only structured knowledge, Bio-JOIE significantly outperforms existing state-of-the-art methods in PPI type prediction on multiple species. Furthermore, we also demonstrate the potential of leveraging the learned representations on clustering proteins with enzymatic function into enzyme commission families. Finally, we show that Bio-JOIE can accurately identify PPIs between the SARS-CoV-2 proteins and human proteins, providing valuable insights for advancing research on this new disease.Comment: ACM BCB 2020, Best Student Pape

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

The Internal Architecture of Leukocyte Lipid Body Organelles Captured by Three-Dimensional Electron Microscopy Tomography

Lipid bodies (LBs), also known as lipid droplets, are complex organelles of all eukaryotic cells linked to a variety of biological functions as well as to the development of human diseases. In cells from the immune system, such as eosinophils, neutrophils and macrophages, LBs are rapidly formed in the cytoplasm in response to inflammatory and infectious diseases and are sites of synthesis of eicosanoid lipid mediators. However, little is known about the structural organization of these organelles. It is unclear whether leukocyte LBs contain a hydrophobic core of neutral lipids as found in lipid droplets from adipocytes and how diverse proteins, including enzymes involved in eicosanoid formation, incorporate into LBs. Here, leukocyte LB ultrastructure was studied in detail by conventional transmission electron microscopy (TEM), immunogold EM and electron tomography. By careful analysis of the two-dimensional ultrastructure of LBs from human blood eosinophils under different conditions, we identified membranous structures within LBs in both resting and activated cells. Cyclooxygenase, a membrane inserted protein that catalyzes the first step in prostaglandin synthesis, was localized throughout the internum of LBs. We used fully automated dual-axis electron tomography to study the three-dimensional architecture of LBs in high resolution. By tracking 4 nm-thick serial digital sections we found that leukocyte LBs enclose an intricate system of membranes within their “cores”. After computational reconstruction, we showed that these membranes are organized as a network of tubules which resemble the endoplasmic reticulum (ER). Our findings explain how membrane-bound proteins interact and are spatially arranged within LB “cores” and support a model for LB formation by incorporating cytoplasmic membranes of the ER, instead of the conventional view that LBs emerge from the ER leaflets. This is important to understand the functional capabilities of leukocyte LBs in health and during diverse diseases in which these organelles are functionally involved

Single Cell Proteomics in Biomedicine: High-dimensional Data Acquisition, Visualization and Analysis

New insights on cellular heterogeneity in the last decade provoke the development of a variety of single cell omics tools at a lightning pace. The resultant high-dimensional single cell data generated by these tools require new theoretical approaches and analytical algorithms for effective visualization and interpretation. In this review, we briefly survey the state-of-the-art single cell proteomic tools with a particular focus on data acquisition and quantification, followed by an elaboration of a number of statistical and computational approaches developed to date for dissecting the high-dimensional single cell data. The underlying assumptions, unique features, and limitations of the analytical methods with the designated biological questions they seek to answer will be discussed. Particular attention will be given to those information theoretical approaches that are anchored in a set of first principles of physics and can yield detailed (and often surprising) predictions

Tissue Localization and Extracellular Matrix Degradation by PI, PII and PIII Snake Venom Metalloproteinases: Clues on the Mechanisms of Venom-Induced Hemorrhage

20 páginas, 4 figuras, 3 tablas y 7 tablas en material suplementario.Snake venom hemorrhagic metalloproteinases (SVMPs) of the PI, PII and PIII classes were compared in terms of tissue localization and their ability to hydrolyze basement membrane components in vivo, as well as by a proteomics analysis of exudates collected in tissue injected with these enzymes. Immunohistochemical analyses of co-localization of these SVMPs with type IV collagen revealed that PII and PIII enzymes co-localized with type IV collagen in capillaries, arterioles and post-capillary venules to a higher extent than PI SVMP, which showed a more widespread distribution in the tissue. The patterns of hydrolysis by these three SVMPs of laminin, type VI collagen and nidogen in vivo greatly differ, whereas the three enzymes showed a similar pattern of degradation of type IV collagen, supporting the concept that hydrolysis of this component is critical for the destabilization of microvessel structure leading to hemorrhage. Proteomic analysis of wound exudate revealed similarities and differences between the action of the three SVMPs. Higher extent of proteolysis was observed for the PI enzyme regarding several extracellular matrix components and fibrinogen, whereas exudates from mice injected with PII and PIII SVMPs had higher amounts of some intracellular proteins. Our results provide novel clues for understanding the mechanisms by which SVMPs induce damage to the microvasculature and generate hemorrhage.This work was performed in partial fulfillment of the requirements for the PhD degree for Cristina Herrera at Universidad de Costa Rica.Peer reviewe

MSI-based mapping strategies in tumour-heterogeneity

Since the early 2000s, considerable innovations in MS technology and associated gene sequencing systems have enabled the "-omics" revolution. The data collected from multiple omics research can be combined to gain a better understanding of cancer's biological activity. Breast and ovarian cancer are among the most common cancers worldwide in women. Despite significant advances in diagnosis, treatment, and subtype identification, breast cancer remains the world's second leading cause of cancer-related deaths in women, with ovarian cancer ranking fifth. Tumour heterogeneity is a significant hurdle in cancer patient prognosis, response to therapy, and metastasis. As such, heterogeneity is one of the most significant and clinically relevant areas of cancer research nowadays. Metabolic reprogramming is a hallmark of malignancy that has been widely acknowledged in recent literature. Metabolic heterogeneity in tumours poses a challenge in developing therapies that exploit metabolic vulnerabilities. Consequently, it is crucial to approach tumour heterogeneity with an unlabeled yet spatially specific read-out of metabolic and genetic information. The advantage of DESI-MSI technology originates from its untargeted nature, which allows for the investigation of thousands of component distributions, at a micrometre scale, in a single experiment. Most notably, using a DESI-MSI clustering approach could potentially offer novel insights into metabolism, providing a method to characterise metabolically distinct sub-regions and subsequently delineate the underlying genetic drivers through genomic analyses. Hence, in this study, we aim to map the inter-and intra-tumour metabolic heterogeneity in breast and ovarian cancer by integrating multimodal MSI-based mapping strategies, comprising DESI and MALDI, with IMC (Imaging Mass Cytometry) analysis of the tumour section, using CyTOF, and high- throughput genetic characterisation of metabolically-distinct regions by transcriptomics. The multimodal analysis workflow was initially performed using sequential breast cancer Patient-Derived Xenografts (PDX) models and was expanded on primary tumour sections. Moreover, a newly developed DESI-MSI friendly, hydroxypropyl-methylcellulose and polyvinylpyrrolidone (HPMC/PVP) hydrogel-based embedding was successfully established to allow simultaneous preparation and analysis of numerous fresh frozen core-size biopsies in the same Tissue Microarray (TMA) block for the investigation of tumour heterogeneity. Additionally, a single section strategy was combined with DESI-MSI coupled to Laser Capture Microdissection (LCM) application to integrate gene expression analysis and Liquid Chromatography-Mass Spectrometry (LC-MS) on the same tissue segment. The developed single section methodology was then tested with multi-region collected ovarian tumours. DESI-MSI-guided spatial transcriptomics was performed for co-registration of different omics datasets on the same regions of interest (ROIs). This co-registration of various omics could unravel possible interactions between distinct metabolic profiles and specific genetic drivers that can lead to intra-tumour heterogeneity. Linking all these findings from MSI-based or guided various strategies allows for a transition from a qualitative approach to a conceptual understanding of the architecture of multiple molecular networks responsible for cellular metabolism in tumour heterogeneity.Open Acces