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Alberto Santos & Yesid Cuesta-Astroz

De las omicas a las multiomicas

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2

Cascada de información

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Omics over time

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The “core” omics disciplines and how they are used to help understand complex biological systems.

Credit: Technology Networks

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Hasin et al.,2017

Multi-omics data types in disease research

Multiple omics data types and approaches to disease research. Layers depict different types of omics data (Box 1). Omics data are collected on the entire pool of molecules, represented as circles. Except for the genome, all data layers reflect both genetic regulation and environment, which may affect each individual molecule to a different extent. The thin red arrows represent potential interactions or correlations detected between molecules in different layers—for example, the red transcript can be correlated to multiple proteins. Within layer interactions, although prevalent, are not depicted. Thicker arrows indicate different potential starting points or conceptual frameworks for consolidating multiple omics data to understand disease. The genome first approach implies that one starts from associated locus, while the phenotype first approach implies any other layer as the starting point. The environment first approach (not shown) examines environmental perturbations

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Omics methods providing insights into a distinct aspect of the biological state of a sample

Hayes et al.,2024

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Joint analysis of two or more omics methods provides more comprehensive insight into key factors

It that can be used for classification or prediction and or serve as potential biomarkers or drug target

Each distinct omics method provides information about a different but incomplete aspect of the internal state of the cell.

Hayes et al.,2024

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Omics and multiomics (aplications)

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The life cycle of multi-omics data meets the parasite's life cycles�A multi-omics context of parasite’s life cycle

Kooij et al., 2006

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Convirtiendo Datos En Conocimiento

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Muchos datos disponibles en las bases de datos, información

sobre secuencias, estructura y función de proteínas, mutaciones

genéticas, estos datos están disponibles no solo para

humanos sino también para otras especies.

Se propone la integración de estos datos, los cuales contienen

gran cantidad de patrones que pueden ser ensamblados por

medio de la integración de información proveniente de distintas

fuentes.

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Genómica del cáncer: Convirtiendo datos en conocimiento

Vazquez et al., 2012

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Cellular networks

A graphical representation of a complex system

Network	Interaction
Protein - Protein	Physical interactions
Metabolical	Metabolic and transport reactions
Regulatory networks	Protein / DNA interactions
PTM networks	Phosphorylation kinase/sustrate

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Non - biological networks�

https://associationsnow.com/wp-content/uploads/2018/04/GettyImages-547533236-800x480.jpg

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What types of data are valuable in answering the biological questions?

What are the goals of the experiments?

What model system is being used and what components (biomolecules) are being measured?

What computational steps are necessary for the interpretation and analysis of the data?

After data collection, statistical analysis and validation of primary data. The next step is the construction of the model.

Relevant questions before integrating omics data

Eckhardt et al., 2020

These models can be represented as a network.

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Network models can be dynamic or static depending on a variety of variables, and can include

weights, directionality, groupings to convey additional information.

Network-based modelling

Eckhardt et al., 2020

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Computational methods for infering host-parasite interactions

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Computational methods for infering host-parasite interactions

Data sources: experimental and computational

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Human-parasite interactomes

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Parasites pictures: diark.org

P. vivax

P. knowlesi

tissues.jensenlab.org

Brain

Intestine

Skin

Blood

Lymph

Eye

Heart

Muscle

Lung

Liver

Mouth

Nose

Spleen

Bone marrow

L. braziliensis

L. mexicana

Cutaneous leishmaniasis

Visceral leishmaniasis

L. donovani

L. infantum

Trypanosomatida

Apicomplexa

P. falciparum

C. parvum

C. hominis

S. mansoni

T. spiralis

Helminths

G. lamblia

Diplomonadida

T. gondii

Host-parasite interactions: 15 parasites vs 14 tissues

We propose an integrative approach that gives context to the interactions according to the parasite’s life cycle and subcellular localization of the proteins. To understand molecular similarities and differences in host response to diverse sets of parasites. These interactions are also essential to understand parasite infection and local adaptation within the host and vector.

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Proteome filtering: adding parasite-specific biological context

We used an orthology-based approach to transfer high-confidence intraspecies interactions obtained from the STRING database to the corresponding interspecies homolog protein pairs in the host–parasite system.

The resulting PPI networks were compared across parasites to identify common mechanisms that may define a global pathogenic hallmark.

Figure 1. Method workflow. (A) The method input consists of the host and parasite proteomes, parasite attributes (uni- or multicellular), and tropism (tissues). Our approach filters the host proteome according to the specified list of tissues using TISSUES database and limits it to only cellular membrane and extracellular proteins extracted from the COMPARTMENTS database. The parasite proteome is filtered to include membrane and secreted proteins (unicellular parasites) or only secreted proteins (multicellular parasites). The next step in the pipeline uses the eggNOG database to identify orthologous proteins for both the filtered proteomes of both the host and the parasite. The workflow continues with the extraction of intra-species protein–protein interactions from STRING database for all the orthologous proteins. These intra-species interactions are then transferred to the host–parasite system as long as the interacting proteins have an orthologous host and parasite proteins. (B) Workflow for soluble secretome prediction in parasites (MT, mitochondrial; TM, transmembrane; TMHMM, transmembrane hidden markov model). (C) A simple scheme of how the orthology transfer is implemented.

�

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Human parasites analyzed

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�� Common and specific mechanisms targeted by the studied parasites�

We used annotations from both biological processes GO terms and Reactome pathways in human to get an overview of the shared pathways targeted by the studied parasites. In total, 1,910 GO terms (biological process) were identified in human proteins targeted by parasites across all the interactomes.

In all the inferred interactomes, GANAB (neutral alpha glucosidase AB) and P4HB (protein disulfide isomerase) proteins were predicted to interact with parasite proteins. GANAB protein is related to the host defense mechanisms and P4HB is relevant in the internalization of broad spectrum of pathogens such as Leishmania, HIV, dengue virus, and rotavirus.

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S. mansoni – H. sapiens interactome

Network centrality helped to prioritize proteins by identifying nodes with a relevant role in the communication flow in the network, which may translate into biological relevant essentiality

The nodes in these networks represent parasite and human proteins and their sizes correspond to their betweenness centrality in the network.

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This method is available as a web–interface to allow visualization and comparison of interactomes across parasites. OrthoHPI (Orthology–based method to predict Human-Parasite Interactions).

http://orthohpi.jensenlab.org/

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Looking for High Definition Interactomes...�

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tissues.jensenlab.org

Brain

Intestine

Skin

Blood

Lymph

Eye

Heart

Muscle

Lung

Liver

Mouth

Nose

Spleen

Bone marrow

L. braziliensis

L. mexicana

Cutaneous leishmaniasis

Visceral leishmaniasis

L. donovani

L. infantum

Trypanosomatida

Apicomplexa

P. falciparum

C. parvum

C. hominis

S. mansoni

T. spiralis

Helminths

G. lamblia

Diplomonadida

T. gondii

P. knowlesi

P. vivax

E. multilocularis

E. histolytica

G. intestinalis

L. major

L. panamensis

T. vaginalis

C. muris

B. bovis

OrthoHPI 2.0

New versions of databases and cellular localization predictor (LLM based) LAProtT5 Stark et al., 2021

Proteomes of 24 eukaryotic parasites.

We included single-cell RNA-seq datasets from the human protein atlas.

Structural predictions using AlphaFold

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Single-cell definition of host-pathogen interactions

The coreography between pathogen, target host cells, and immune system dictates the course of disease, and is likely to define transitions between acute, chronic and latent infection, as well transmition.

Host-pathogen interactions can be studied with a resolution and depth not previously posible.

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OrthoHPI 2.0 – Methodology

Filtering by cell-type

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OrthoHPI 2.0 – Host-Parasite PPIs at tissue and single-cell level

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OrthoHPI 2.0 - Host-Parasite PPIs at tissue and single-cell level

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OrthoHPI 2.0 – Host-Parasite PPIs at tissue, single-cell and structural level

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Findable : data should be easy to find�Accesible: the users finds the required data�

These methods are available as a web–interface to allow visualization and comparison of interactomes across parasites. OrthoHPI (Orthology–based method to predict Human-Parasite Interactions).

https://orthohpi.streamlit.app/

http://orthohpi.jensenlab.org/

OrthoHPI 1.0

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�Interoperable: the data usually need to be integrated with other data�Reusable: optimise the reuse of data�Toxoplasmosis transcriptomics data integrated with OrthoHPI PPIs predictions

(Hu et al., 2020)

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Project JAGUAR:�mapping immune cell diversity across Latin America

Ancestry Networks for the Human Cell Atlas

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AIM: address how genetic diversity shapes immunity resulting in differences in responses to infections and susceptibility to diseases

STRENGTH: genetic richness of Latin American populations and environmental diversity

Project JAGUAR

1

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1

Mapping immune cell diversity across Latin America

HOW: use of single-cell technologies (scRNA-seq, scATAC-seq and scCITE-seq) to identify how diverse ancestries impact gene expression and the composition of immune cells

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Work Team

Gosia Trynka

WELLCOME SANGER INSTITUTE

UNITED KINGDOM

Alejandra Medina-Rivera

UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO

Yesid Cuesta Astroz

UNIVERSIDAD DE ANTIOQUIA

Benilton de Sá Carvalho

UNIVERSITY OF CAMPINAS - UNICAMP

Pablo Alberto Romagnoli INSTITUTO UNIVERSITARIO DE CIENCIAS BIOMÉDICAS DE CÓRDOBA

Luis Alberto Tataje Lavanda

ESCUELA PROFESIONAL DE MEDICINA HUMANA, UNIVERSIDAD PRIVADA SAN JUAN BAUTISTA

Marcela Katherine Sjöberg Herrera

PONTIFICIA UNIVERSIDAD CATÓLICA DE CHILE

Maximiliano Berro Castiglioni

HOSPITAL DE CLÍNICAS, UNIVERSITY OF THE REPUBLIC

Mexico

Brazil

Argentina

UK

Uruguay

Chile

Colombia

Peru

3

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“To understand various disease mechanisms, it is not sufficient to know the precise list of “disease genes”; instead, we should try to map out the detailed wiring diagram of the various cellular components that are influenced by these genes and gene products”.

Network medicine

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Network medicine

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The transition from omics to multiomics represents a shift from studying individual molecular components of a biological system in isolation (a reductionist approach) to an integrated, holistic analysis of multiple layers of biological data.

Conclusions

Feature	Single Omics	Multiomics
Scope	Focuses on a single molecular layer (e.g., only DNA or only proteins).	Integrates data from multiple layers (e.g., genomics, transcriptomics, and proteomics).
Understanding	Provides a partial, often associative, view of biological mechanisms.	Offers a more comprehensive, holistic, and systematic understanding of molecular interactions.
Causality	Often limited to identifying correlations; establishing direct causality can be difficult.	Helps in establishing a chain of causality in molecular events by showing how changes in one layer (e.g., the genome) affect others (e.g., the proteome).
Predictive Power	Provides insights, but with limitations.	Improves the diagnostic and predictive power for disease states and treatment responses.

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Data integration or connection?

Yes, but please provide context… biological context

Only the objectives and context can define what data to use when and how to combine them