• Alejandra Medina-Rivera

Patient registries are structured systems designed to collect, store, and analyze information about individuals living with specific health conditions. When combined with genomic data, they create new opportunities for genomic medicine and research. The MexOMICS Consortium was established to develop infrastructure that integrates electronic databases, enabling the collection, comparison, cross-referencing, and sharing of valuable information on various health conditions in the Mexican population, including the Mexican Lupus Registry (LupusGMX).

LupusRGMX is a digital data platform created to enhance understanding of the characteristics of Mexican people with Systemic Lupus Erythematosus (SLE) and to support longitudinal studies. Led by a multidisciplinary research team and engaging patient communities, the platform offers tailored surveys focusing on different aspects of patients' lives. This resource has empowered demographic, socioeconomic, and genomic research.

Building on the insights gained from MexOMICS and LupusRGMX, we have launched the JAGUAR project to map the immune cell diversity across Latin America.

• Maryam Mares

Understanding the intricate interactions that take place between hosts and microbes is essential for uncovering how microbial communities influence host physiology, adaptation, and health. This keynote will present computational strategies to decode these complex interactions by integrating functional genomics data analyses with ecological modeling. Focusing on secreted bacterial proteins, small RNAs, and their genetic targets in emerging model organisms, notably invertebrates, I explore the molecular mechanisms underlying symbiosis. The methodologies combine comparative and functional genomics with dynamic modeling to predict microbial processes such as competition, cooperation, and functional complementarity, and their effects on host traits like stress response and immunity. Co-divergence analyses will also be highlighted, tracing the evolutionary history of hosts and their symbionts to reveal the co-evolutionary dynamics that shape these associations. Through case studies, this presentation will showcase how computational tools can bridge molecular mechanisms and broader ecological processes, offering new insights into microbial contributions to host biology across scales. This integrated approach underscores the value of computational methods in advancing our understanding of symbiosis, with broader implications that demonstrate how these tools can identify key microbial players and pathways, supporting the development of new approaches to modulate host-microbiome interactions, potentially enhancing animal health and nutrition.

• Alejandro Reyes, Department of Biological Sciences, Universidad de los Andes, Bogotá, Colombia, Colombia
• Claudio Durán, Host-Microbiota Interactions Laboratory, Wellcome Sanger Institute, Hinxton, United Kingdom, United Kingdom
• Yan Shao, Host-Microbiota Interactions Laboratory, Wellcome Sanger Institute, Hinxton, United Kingdom, United Kingdom
• Alexandre Almeida, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, United Kingdom., United Kingdom
• Isaac González-Santoyo, NeuroEcology Lab, Faculty of Psychology, National Autonomous University of Mexico (UNAM), Mexico City, México., Mexico
• Alberto Penas-Steinhardt, Computational Genomics Laboratory, Department of Basic Sciences, Universidad Nacional de Luján, Luján, Argentina., Argentina
• Fiorella Belforte, Computational Genomics Laboratory, Department of Basic Sciences, Universidad Nacional de Luján, Luján, Argentina., Argentina
• Paul Cárdenas, Institute of Microbiology, Universidad San Francisco de Quito, Quito, Ecuador., Ecuador
• Manuel Baldeón-Tixe, Faculty of Medical Health and Life Sciences, Universidad Internacional del Ecuador, Quito, Ecuador., Ecuador
• Cecilia Salazar, Microbial Genomics Laboratory, Institut Pasteur de Montevideo, Montevideo, Uruguay., Uruguay
• José Boggia, Nephrology Centre and Physiopathology Department, U. de la República, Montevideo, Uruguay., Uruguay
• Paula Moliterno, Clinical Nutrition Department, Nutrition School, Universidad de la República, Montevideo, Uruguay., Uruguay
• Nicolás Saavedra-Cuevas, Center of Molecular Biology and Pharmacogenetics, Universidad de la Frontera, Temuco, Chile., Chile
• Patricio López-Jaramillo, Faculty of Health Sciences, Masira Research Institute, Universidad de Santander, Bucaramanga, Colombia., Colombia
• Marina Muñoz, CIMBIUR, Faculty of Natural Sciences, Universidad del Rosario, Bogotá, Colombia., Colombia
• Juan David Ramírez, CIMBIUR, Faculty of Natural Sciences, Universidad del Rosario, Bogotá, Colombia, Colombia
• María Teresa Álvarez-Aliaga, Pharmacobiochemical Research Institute, Universidad Mayor de San Andrés, La Paz, Bolivia., Bolivia
• Liliane Costa Conteville, Laboratory of Molecular Genetics of Microorganisms, Oswaldo Cruz Institute, Rio de Janeiro, Brazil., Brazil
• Ana Carolina Paulo Vicente, Laboratory of Molecular Genetics of Microorganisms, Oswaldo Cruz Institute, Rio de Janeiro, Brazil., Brazil
• Verónica Antelo, Microbial Genomics Laboratory, Institut Pasteur de Montevideo, Montevideo, Uruguay., Uruguay
• Mark Stares, Wellcome Sanger Institute, Hinxton, United Kingdom, United Kingdom
• Hilary Browne, Wellcome Sanger Institute, Hinxton, United Kingdom, United Kingdom
• Robert D. Finn, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, United Kingdom., United Kingdom
• Henry Cohen, Gastroenterology Clinic, Faculty of Medicine, Universidad de la República, Montevideo, Uruguay., Uruguay
• Gregorio Iraola, Microbial Genomics Laboratory, Institut Pasteur de Montevideo, Montevideo, Uruguay., Uruguay
• Pablo Tsukayama, Departamento de Ciencias Celulares y Moleculares, Universidad Peruana Cayetano Heredia, Lima, Peru, Peru
• Trevor D. Lawley, Host-Microbiota Interactions Laboratory, Wellcome Sanger Institute, Hinxton, United Kingdom, United Kingdom

The human gut microbiome plays a critical role in mediating the interactions between our environment and health, impacting from digestion to immune function. Advancing personalized medicine requires a comprehensive understanding of how the microbiome is shaped and manipulated to promote health. The structure of the microbiome is primarily influenced by diet and early environmental exposures, with a significant hereditary component but minimal direct genetic effect. Early studies emphasize two key issues: the over-representation of industrialized countries in public datasets and the detrimental impact of processed foods on bacterial diversity, which, in turn, affects health outcomes. Research on African populations has identified VANISH (volatile and/or associated negatively with industrialized societies of humans) taxa, which are largely absent in industrialized countries. The lack of data on Latin American gut microbiomes, coupled with their transitional status towards industrialization, presents a unique opportunity to study the loss of VANISH bacteria and uncover unique characteristics of these populations.

Latinbiota, an international consortium founded in 2019 by Sanger International Fellow Gregorio Iraola (Instituto Pasteur Montevideo, Uruguay), includes nearly 50 scientists from Uruguay, Argentina, Chile, Bolivia, Brazil, Colombia, Ecuador, and Mexico. The consortium aims to elucidate the composition and variability of the human gut microbiome in Latin America. Collaborating with the Wellcome Sanger Institute and the European Bioinformatics Institute, the team employs metagenomic and bioinformatic approaches to discover and characterize gut microbes associated with health and disease in this understudied region.

In its initial phase, Latinbiota collected and sequenced fecal samples from 600 individuals across participating countries. Preliminary findings have revealed intriguing properties of the Latin American gut microbiome. Notably, samples from large urban centers resemble those from European and North American microbiomes, while rural microbiomes display significant divergence. Additionally, two distinct variation processes were observed in rural samples from Ecuador and Mexico—one showing a large share of microbes common in African populations, and the other lacking similarity with other analyzed microbiomes. Moreover, urban Latin American microbiomes appear to converge regardless of country of origin, suggesting a loss of diversity and a convergence linked to similar exposures to processed foods.

The increased bacterial diversity and novelty observed in rural and indigenous populations offer a valuable opportunity to deepen our understanding of gut microbiome diversity, its shaping factors, and strategies to recover VANISH bacteria. This research holds the potential to significantly impact our approach to personalized medicine and global health.

• Laura Carolina Camelo Valera, McGill University, Canada
• Corinne Maurice, McGill University, Canada

Malnutrition is one of the leading causes of mortality of infants under the age of five. Its prevalence is highest in low- and middle-income countries due to food supply limitations, where it can lead to stunting and wasting. Stunting is a multifactorial condition with long-lasting effects such as cognitive and growth impairment as well as delayed bacterial colonization patterns leading to increased susceptibility to enteric infections. Changes in bacterial abundances are intrinsically linked to their viral predators, called bacteriophages or phages. Previous in vitro work in our lab showed that phages contribute to shaping a malnourished gut microbiome by exacerbating gut bacterial pathogen loads in stunted children. However, further work is needed to understand fundamental phage-host ecology patterns that can only be seen at fine-level resolution and how these dynamics serve to modulate bacterial communities in malnutrition. We explored the links between viral population dynamics and nutritional health status in a cohort of 232 children from Zimbabwe sampled monthly from 30 to 650 days after birth, including stunted and non-stunted controls. We developed a framework to assess macro- and micro-diversity patterns in the infant gut. So far, we found that there is a natural gap at 99.5% ANI and 95% coverage suggesting sequence discrete viral populations (vPOPs). These vPOPs show high individuality (86% of the vOTUs are found in < 10 infants), consistent with previous reports at the genus level. Still, we found 249 vPOPs present in at least 50% of the children, some of which were persistent and others sporadic (only present at a certain age period). Phage prevalence also differed with respect to nutritional status, likely depending on bacterial host availability. Overall, these findings provide essential information to understand the eco-evolutionary patterns of viral communities in the human gut and their links to malnutrition. The framework we developed offers a robust strategy for other researchers to explore viral populations at a high resolution, facilitating a deeper understanding of microbiome dynamics. Understanding phage dynamics at sub-species level in the infant gut may enable us to study their influence on gut bacterial communities during early colonization.

• Rafael Pérez-Estrada, Centro de Ciencias Matemáticas, UNAM, Mexico
• Nelly Sélem Mojica, Centro de Ciencias Matemáticas, UNAM, Mexico
• Shaday Guerrero Flores, Centro de Ciencias Matemáticas, UNAM, Mexico
• Juan Francisco Espinosa Maya, Centro de Ciencias Matemáticas, UNAM, Mexico
• Mario Jardon, Centro de Ciencias Matemáticas, UNAM, Mexico
• Orlando Camargo Escalante, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Mexico
• Miguel Nakamura, Centro de Investigación en Matemáticas, Mexico
• David Alberto García, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Mexico
• Luis Yovanny Galvan, Escuela Nacional de Estudios Superiores unidad Morelia, UNAM, Mexico
• Goretty Mendoza, Instituto de Investigaciones en Ecosistemas y Sustentabilidad UNAM, Mexico
• José Abel Lovaco, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Mexico
• Mario Enrique Carranza Barragán, Centro de Investigación en Matemáticas, Mexico
• Daniel Chávez, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Mexico
• July Stephany Gámez Valdez, Instituto Tecnológico y de Estudios Superiores de Monterrey, Mexico
• Axel Alejandro Ramos García, Instituto Tecnológico y de Estudios Superiores de Monterrey, Mexico

The gut microbiome, crucial in human health and disease pathogenesis, serves as biomarkers for health states. Current approaches to microbiome health indices, like Alpha Diversity, Gut Microbiome Health Index, and hiPCA, focus on taxonomic profiles. A comprehensive approach includes the Theatre of Activity (ToA), considering microbiota functions and interactions. The CAMDA 2024 challenge aims to develop a superior gut microbiome health index using ToA and differentiate COVID-19 patients from healthy controls. The dataset includes 613 samples from the Human Microbiome Project 2 and the American Gut Project with taxonomic (MetaPhlAn) and functional (Humann) profiles, and indices like Shannon entropy, GMHI, and hiPCA. We adapted GMHI for functional profiles and created RF-GMHI using a Random Forest model. Additionally, we developed code for the hiPCA index and explored KS-hiPCA with logistic regression. Results showed our methods outperforming given indices: GMHI (given) with 62.4% accuracy, RF-GMHI 89.3%, hiPCA (given) around 70%, and KS-hiPCA + Logistic Regression 82%. Using these methods, we attempted to classify COVID-19 samples, with some unclear results. Exploratory analysis using alpha and beta diversity metrics, metagenomic alpha-diversity index (MD index), co-occurrence networks, and Topological Data Analysis (TDA) provided further insights. Our methods integrating taxonomic and functional profiles outperformed provided metrics, showing promise in distinguishing controls from COVID-19 patient samples. Further examination of these methods may yield valuable insights. All code is documented for reproducibility and available on our Git repository.

• David Bacsik, University of Washington; Fred Hutchinson Cancer Center, United States
• Bernadeta Dadonaite, Fred Hutchinson Cancer Center, United States
• Andrew Butler, Fred Hutchinson Cancer Center, United States
• Jesse Bloom, HHMI; Fred Hutchinson Cancer Center, United States

Background
Single-cell RNA sequencing generally uses short sequencing reads to identify two important pieces of information: first, the identity of the cDNA transcript; and second, the cell-specific barcode that was added during reverse transcription. This approach provides quantitative measurements of gene expression in each cell. However, it does not offer much information about the sequence of the transcript, or about the genetic variation that may present between cells.

In some biological systems, there can be significant genetic variation between individuals from a clonal source. Viruses with RNA genomes, for example, have very high mutation rates. Over the course of an infection, each virion produces related, but uniquely-mutated progeny. This results in a diverse population of viral particles, with distinct mutations in each virion’s genome.

In order to study this genetic variation, we developed new methods to incorporate long-read PacBio sequencing with 10X single-cell RNA sequencing. We used these to reconstruct the genome sequence of individual influenza virions that had infected single mammalian cells. We generated more than 125 complete viral genomes.

Methods
We infected mammalian cells with influenza virus at a low multiplicity of infection and followed the typical protocol to quantify viral gene expression in each infected cell. Then, using targeted DNA oligomers, we circularized and amplified the viral transcripts in a way that retained the complete viral sequence and the cell-specific barcode. Finally, we computationally integrated the long-read and short-read sequencing data by linking cell-specific barcodes from both sources.

Results
Our results indicate that viral gene expression varies by orders of magnitude between cells infected with the same virus stock. Mutation and structural variation explain a portion of this heterogeneity. Specifically, mutations that cause loss of function of the nuclear export protein are strongly associated with high levels of viral transcripts.

Conclusions
In sum, this work demonstrates a novel technique for the focused study of sequence variation between single cells. We have applied this technique to uncover a mechanistic relationship between mutations in the influenza virus genome and expression of influenza transcripts.

• Fausto Cabezas Mera, Programa de Doctorado en Informática Aplicada a Salud y Medio Ambiente, Universidad Tecnológica Metropolitana, Chile
• María José Barros, Laboratorio de RNAs Bacterianos, Facultad de Ciencias de la Vida, Universidad Andrés Bello, Chile
• Diego Martínez, Laboratorio de RNAs Bacterianos, Facultad de Ciencias de la Vida, Universidad Andrés Bello, Chile
• Lillian Acuña, Laboratorio de RNAs Bacterianos, Facultad de Ciencias de la Vida, Universidad Andrés Bello, Chile
• Iván Calderón, Laboratorio de RNAs Bacterianos, Facultad de Ciencias de la Vida, Universidad Andrés Bello, Chile
• Ana Moya-Beltrán, Departamento de Informática y Computación, Facultad de Ingeniería, Universidad Tecnológica Metropolitana, Chile

Yersinia ruckeri, the causative agent of enteric red mouth disease (ERM) in salmonids, is a major threat to Chilean aquaculture, with outbreaks causing mortality rates up to 70%. This study employs a bioinformatic pipeline, integrating pangenome analysis with multi-omics data, to identify potential antigens and novel targets for disease control.
We analyzed 165 Y. ruckeri genomes, including the Chilean strain 'CD2', through a comprehensive pangenome approach. Our pipeline identified 9,737 genes, with 26.1% classified as core genes conserved across all strains, underscoring their essential role in pathogen survival and functionality. These core genes represent prime candidates for broad-spectrum interventions. The 'CD2' strain formed a distinct clade, harboring 25 unique genes, 23 of which are uncharacterized hypothetical proteins, indicating potential virulence factors specific to this strain.
We detected expression patterns of small RNAs (sRNAs) distinctive to each stage of biofilm formation. Computational analyses identified key genes and sRNAs involved in pathogenic pathways, providing a deeper understanding of the molecular mechanisms underlying Y. ruckeri virulence and resistance.
Based on these findings, we propose candidate epitopes using pangenome-reverse vaccinology strategies, which combine core gene conservation with sRNA targets. However, the limited availability of local genome sequences hinders a comprehensive genetic understanding of Y. ruckeri diversity in Chile. Ongoing efforts to sequence additional local isolates and validate vaccine candidates in vivo will further refine these strategies, enhancing biosecurity and supporting the sustainability of Chilean aquaculture.

• Rob Finn

Metagenomics, the analysis of the sum of genetic material from a sample, has started to shed light on the huge diversity of micro-organisms that occupy different such as the human body, soil and ocean environment. For example, humans host trillions of microbes, which have adapted to a range of body sites, such as the gut, oral cavity and skin. It is only since the application of metagenomic approaches have we started to understand the huge diversity of micro-organisms that occupy these different environments provided by the human host. MGnify hosts the catalogs of genomes, which is driving new avenues of research, especially with the aim to develop new model organisms and experimental systems. We have also conducted parallel research to recover genomes from the human skin microbiome, which not only harbours a very distinct microbial composition compared to the gut, but also carries additional challenges such as low DNA yield. Nevertheless, this analysis has provided multi-kingdom catalogues from the skin. Finally, the same approaches have started to reveal the diversity harboured in the marine environment. While this dataset is by no means complete, we are starting to understand how this data can be utilised to identify potential new products or enzymes tractable for bioremediation.

• Pierre Baldi

AI today can pass the Turing test and is in the process of transforming science, technology, society, humans, and beyond.
Surprisingly modern AI is built out of two very simple and old ideas, rebranded as deep learning: neural networks and
gradient descent learning. I will describe several applications of AI to problems in biomedicine developed in my laboratory,
from the molecular level to the patient level using omic data, imaging data, clinical data, and beyond.
I will discuss the opportunities and challenges for developing, integrating, and deploying
AI in the first AI-driven hospitals of the future and present two frameworks for addressing some of the most pressing societal issues related to AI research.

• Jorge Duitama

Computational biology is a fundamental tool for modern research in life sciences, with different applications in diverse fields such as medicine, conservation of ecosystems, and food production. Given the importance of computational biology in both research and industry, Universidad de los Andes was a pioneer in launching a masters program aiming to provide specialized knowledge in programming and big data analytics for life sciences. In this talk we provide an overview of our experience with the masters program in computational biology throughout these years. The program provides a solid foundation in bioinformatics, advanced molecular genetics and algorithms, combined with a set of elective courses which allow students to follow different paths for specialized knowledge. We highlight the role of the students, not only as trainees, but also as young scientists who have made significant contributions to different research projects. Our alumni have successfully worked in different Colombian organizations, including research centers, institutions of the health sector, and even government agencies. This experience shows the importance of formal training for the development of competences needed to conduct work in computational biology, considering not only specialized technical skills, but also high ethics standards and capacities to work in multidisciplinary and multicultural environments.

• Juan Rincón

• Laura Natalia Gonzalez-Garcia, Universidad de los Andes, Colombia
• Maria Paula Rodriguez, Universidad de los Andes, Colombia
• Marcela Parra-Muñoz, Universidad Nacional de Colombia, Colombia
• Ana M Clavijo, Universidad Nacional de Colombia, Colombia
• Laura Levy, Universidad de Los Andes, Colombia
• Clemencia Ovalle-Bracho, Hospital Universitario Centro Dermatológico Federico Lleras Acosta E.S.E, Colombia
• Claudia Colorado, Hospital Universitario Centro Dermatológico Federico Lleras Acosta E.S.E, Colombia
• Carolina Camargo, Hospital Universitario Centro Dermatológico Federico Lleras Acosta E.S.E, Colombia
• Eyson Quiceno, Universidad de Antioquia, Colombia
• Maria Juliana Moncada, Universidad de Antioquia, Colombia
• Carlos Muskus, Universidad de Antioquia, Colombia
• Daniel Alfonso Urrea, Universidad del Tolima, Colombia
• Felipe Baez-Aguirre, Universidad de los Andes, Colombia
• Silvia Restrepo, Boyce Thompson Institute, Colombia
• María Clara Echeverry, Universidad Nacional de Colombia, Colombia
• Jorge Duitama, Universidad de los Andes, Colombia

La leishmaniasis es una enfermedad transmitida por vectores que representa un importante problema de salud pública a nivel mundial. Esta parasitosis, considerada una antropozoonosis, muestra un amplio espectro de características clínicas y epidemiológicas asociadas con la diversidad genómica y el complejo ciclo de vida del parásito. Sin embargo, la diversidad genética de Leishmania en los escenarios naturales de transmisión es poco conocida. Este estudio aborda el análisis de datos de lectura corta de 205 aislados de Leishmania de los países del norte de América del Sur, incluidos 66 aislados clínicos colombianos recién secuenciados del subgénero Leishmania (Viannia) y ensamblajes de genomas a nivel de cromosoma para 10 de esos aislados colombianos, sobre la base de datos de secuenciación de lectura larga. La alta diversidad observada de variantes de Leishmania en todas las especies que circulan en Colombia sugiere una rápida adaptación de estos parásitos a diferentes ambientes. Además, las muestras se pueden agrupar según su origen geográfico. Los híbridos dentro de los grupos principales desafían la delimitación de las especies, especialmente entre L. (V.) panamensis y L. (V.) guyanensis, y requieren una comprensión profunda de la hibridación natural entre L. (V) braziliensis/peruviana y L. (V.) guyanensis/panamensis. Aunque los genomas están altamente conservados en todas las especies, el análisis pangenómico de ensamblajes de alta calidad muestra una variación constante en el número de copias para diferentes familias de genes entre especies. Este pangenoma también mostró que las familias de genes de astina tienen un mayor número y diversidad que los informes anteriores basados en el análisis de datos de lectura corta. Este trabajo proporciona recursos genómicos completos para identificar marcadores poblacionales de Leishmania spp, que se utilizarán para generar nuevos conocimientos sobre la biología, la dinámica de transmisión y la evolución de los mecanismos de virulencia del parásito.

• Gabriel Garcia, Departamento de Química Biológica, FCEN, Universidad de Buenos Aires, Argentina. IQUIBICEN-CONICET, Argentina
• German Jurado, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina., Argentina
• Miranda Palumbo, Instituto de Cálculo, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina., Argentina
• Florencia Castello, Instituto de Cálculo, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina., Argentina
• Federico Serral, Departamento de Química Biológica, FCEN, Universidad de Buenos Aires, Argentina. IQUIBICEN-CONICET, Argentina
• Ezequiel Sosa, Departamento de Sistema, Universidad Tecnologica Nacional, Buenos Aires, Argentina., Argentina
• Rafael Terra, Laboratório Nacional de Computação Científica (pt-br) National Laboratory for Scientific Computing, Petrópolis, Brazil., Brazil
• Dario Fernandez Do Porto, Instituto de Cálculo, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina., Argentina
• Adrian Turjanski, Departamento de Química Biológica, FCEN, Universidad de Buenos Aires, Argentina. IQUIBICEN-CONICET, Argentina

Diseases caused by infectious agents have been a major cause of death worldwide from ancient times to the 21st century. Currently, a multidisciplinary approach combined with advances in bioinformatics tools and sequencing techniques has led to significant progress in the development of antimicrobials. However, despite this, many resistant bacteria still result in prolonged and ineffective treatments.
In this context, we present Target Pathogen 2.0, a bioinformatics tool designed to automate the drug discovery process for potential treatments based on a bacterial genome. From this genome, all information related to sequences, structural, and functional annotations is generated and stored in a PostgreSQL database using AlphaFold and the PDB databases. Subsequently, druggable sites and the subcellular localization of proteins are identified using P2rank and PSORTb, respectively. All these platform functionalities are supported using Django, providing an online service that allows users to interact with all the information.
Additionally, we are also incorporating an original module capable of predicting, through a machine learning model, the drugs capable of interacting with the pathogen's proteins, offering researchers links to available compounds for in vitro research. Moreover, for those looking to enrich the analysis, Target Pathogen 2.0 allows users to import their own data from tools or experiments not included in the main pipeline, providing a personalized experience for the requirements of each research group.
Unlike its predecessor[1], this new version incorporates Parsl as its backbone. Parsl is a library that allows for the parallelization of Python code, and this robust manager guarantees the project's scalability and facilitates the incorporation of new modules capable of capturing the functionalities of emerging tools in the future. Target Pathogen 2.0 aims to offer a user-friendly and easily accessible environment that can be used by researchers without a background in informatics.

Sosa, E. J., Burguener, G., Lanzarotti, E., Defelipe, L., Radusky, L., Pardo, A. M., … Fernández Do Porto, D. (2018). Target-Pathogen: a structural bioinformatic approach to prioritize drug targets in pathogens. Nucleic Acids Research, 46(D1), D413–D418. https://doi.org/10.1093/nar/gkx1015

• David Bacsik, University of Washington; Laboratorio de Genómica Microbiana, UPCH, Peru
• Diego Cuicapuza, Laboratorio de Genómica Microbiana, UPCH, Peru
• Anne Martinez-Ventura, Laboratorio de Genómica Microbiana, UPCH, Peru
• Ericka Meza, Instituto de Medicina Tropical Alexander von Humboldt, UPCH, Peru
• Carlos Zamudio, Instituto de Medicina Tropical Alexander von Humboldt, UPCH, Peru
• Eduardo Gotuzzo, Instituto de Medicina Tropical Alexander von Humboldt, UPCH, Peru
• Pablo Tsukayama, Laboratorio de Genómica Microbiana and Instituto de Medicina Tropical Alexander von Humboldt, UPCH, Peru

Respiratory syncytial virus (RSV) is a paramyxovirus which causes symptoms ranging from rhinorrhea to hypoxemia and respiratory failure. Like influenza virus, acute RSV infection is a common antecedent of bacterial pneumonia in vulnerable populations. In 2023, the first RSV vaccine received regulatory approval. Now, there are three different RSV vaccines available to patients at significant risk for severe RSV disease. Because vaccine-induced immunity could potentially focus selection pressure, there is an increased need for monitoring of RSV transmission and evolution. Global surveillance is now critical to detect any vaccine escape mutations that arise.

To proactively address this need, we conducted a phylogenetic study of RSV circulating in Lima, Peru during the 2024 Southern Hemisphere respiratory virus season. We obtained clinical specimens from local patients with respiratory symptoms and generated whole-genome viral sequences from specimens that tested positive by qPCR.

We built a reproducible computational pipeline using Nextstrain and generated a maximum likelihood phylogenetic tree based on the complete genome sequence. We included a diverse sample of publicly-available context sequences. We described the RSV lineages circulating in Lima this year, and assessed the current patterns of local, regional, and long-range transmission.

Peruvian sequences generally nested within clades of South American sequences from the current respiratory virus season. North American and European sequences from the same time period were frequently interspersed. We detected one 2024 Peruvian sequence that was closely related to 2023 sequences from Peru, suggesting that local strains can persist between seasons, albeit infrequently. We did not detect an isolated Peruvian clade, nor did we detect high frequency amino acid substitutions suggestive of recent adaptation.

In summary, we have generated and analyzed whole-genome RSV sequences from samples collected in Peru during the 2024 respiratory virus season. The RSV strains we sampled were consistently related to contemporaneous sequences from multiple continents, and were rarely related to Peruvian strains from previous seasons. Regular surveillance continues to be necessary to support both local public health efforts in Peru and to inform vaccine updates globally.

• Mathias Witte Paz, Institute for Bioinformatics and Medical Informatics – University of Tübingen, Germany
• Alina Bitzer, Interfaculty Institute of Microbiology and Infection Medicine – University of Tübingen, Germany
• Simon Heilbronner, Faculty of Biology – Ludwig-Maximilians-Universität München, Germany
• Kay Nieselt, Institute for Bioinformatics and Medical Informatics – University of Tübingen, Germany

Iron acquisition is crucial for bacterial proliferation, especially in iron-limited environments like the nasal microbiome. Bacteria secrete siderophores (SIDs), iron-binding secondary metabolites that allow its intake. While Staphylococcus aureus produces SIDs, others, such as Corynebacteria, can exploit these metabolites without synthesizing these themselves, therefore leading to reciprocal competition and support [Zhao, et al., 2024]. A key mechanism for metallophore uptake are lipoproteins. Here, we investigated the SID-intake potential of 51 Corynebacteria based on a genome-wide lipoprotein analysis. By characterizing the SID-intake systems in nasal Corynebacteria, we aim to understand the influence of SID sharing within microbial communities by analyzing the abundance of S. aureus in metagenomic samples, particularly in the presence of Corynebacterium cheaters.

Since we hypothesize that orthologous lipoproteins will bind equal or similar SIDs, we identified lipoprotein orthologs in Corynebacteria to assess their SID-intake potential. Given the low sequence similarity of lipoproteins across genera, we increased the sensitivity of the homology search by developing a Nextflow pipeline that combines homology search methods based on the protein’s primary and tertiary structure. This pipeline encompasses SignalP6 for lipoprotein identification, MMseqs2 for the sequence homology computation of a non-redundant set of proteins, as well as ColabFold and Foldseek for tertiary structure prediction and orthology detection. We identified 366 proteins across all 51 samples showing homologous structures to known S. aureus lipoproteins involved in SID-intake.

Next, we verified the genes’ response in an iron-deprived condition for two Corynebacterium species via an RNA-seq experiment, one with chelator EDDHA vs. a control environment. A gene ontology enrichment analysis returned multiple GO terms related to iron-intake and homeostasis, confirming a response to iron-depletion. More importantly, all identified homologs showed a strong up-regulation in the EDDHA environment.

Moreover, the pipeline uses XSTREME to identify conserved sequence motifs within putative promoter regions of the homologs and compares these to databases. For many bacteria, including S. aureus, Fur is a known regulator for iron homeostasis. In our Corynebacteria isolates, the results show binding sites for Fur and IdeR upstream of the identified genes, suggesting that both transcription factors play a role in the regulation of iron-related genes in this genus.

Our pipeline enables the first high-throughput, comprehensive analysis of lipoproteins across genera, allowing us, in this case, to characterize the SID-intake potential of Corynebacteria. Future work will resolve the SID-lipoprotein mapping to provide a foundation for a wet-lab screening, and hence identify exploitable SIDs for each Corynebacteria strain.

• Maria Peña, Research Group on Computational Biology and Microbial Ecology, Universidad de Los Andes, Bogotá, Colombia, Colombia
• Felipe Sierra, Research Group on Computational Biology and Microbial Ecology, Universidad de Los Andes, Bogotá, Colombia, Colombia
• Diego Jimenez, Biological and Environmental Sciences and Engineering Division, KAUST, Thuwal, 23955-6900, Kingdom of Saudi Arabia, Saudi Arabia
• Alejandro Reyes, Research Group on Computational Biology and Microbial Ecology, Universidad de Los Andes, Bogotá, Colombia, Colombia

Mangroves stand out for their high microbial diversity, which allows varied metabolic acEvity and
supports mulEple ecosystem services. They are fundamental in the context of climate change due
to their role as large carbon reservoirs. However, the applicaEon of the metabolic potenEal of
mangrove soil microbial diversity in waste management, including plasEcs and lignocellulose, has
been li\le studied. PlasEc polluEon is a major environmental problem with mulEple negaEve
effects on health and biodiversity. Similarly, lignocellulose, a renewable resource used for
bioplasEcs and biofuels, presents biodegradaEon challenges due to the resistance of plant cell
walls. AddiEonally, the expansion of agricultural borders into flood-prone areas like mangroves can
lead to increased deposits of lignocellulose-rich waste in these ecosystems. Then, uElizing the
metabolic abiliEes of resident microorganisms, which use these polymers as carbon sources, could
significantly contribute to a circular economy for bioplasEcs.
In this context, a project was iniEated to evaluate microbial diversity and its funcEonal potenEal for
polymer biodegradaEon in a mangrove forest in Cartagena, Colombia. In the first phase, a
microcosm experiment using lignocellulose and PET as carbon sources, with seawater and without
water, and incubaEon periods of 30 and 90 days was conducted. Significant taxonomic differences
were found by sequencing the 16s RNA gene. Following these results, a metagenomic analysis by
Shotgun sequencing of the 90-day incubated treatments was performed. Preliminary results show
the recovery of 74 MAGs of medium and high quality, belonging to the domains of archaea and
bacteria (4 and 70 MAGs, respecEvely). Shannon and Simpson indices suggested high alpha
diversity (3.85 and 0.97 respecEvely), with significant differences between lignocellulose
treatments with water and control treatments (Kruskal-Wallis and Dunn test p <0.05).
Proteobacteria, AcEnobacteria, Desulfobacterota, and Acidobacteriota were predominant in all
treatments. NMDS analysis using the Bray-CurEs index showed significant differences in beta
diversity between treatments, influenced by the presence of water and lignocellulose. Families
such as Streptomycetaceae and Paenibacillaceae were unique to lignocellulose treatments.
Future work will use these MAGs to idenEfy genes encoding carbohydrate-acEve enzymes
(CAZymes) in microcosms with lignocellulose and plasEc-degrading enzymes (PAZymes) in
microcosms with plasEc. This research could idenEfy key microorganisms and metabolic pathways
that facilitate the biodegradaEon of plasEcs and lignocellulose, contribuEng to miEgaEng plasEc
polluEon and promoEng sustainable waste management pracEces.

• Daniel F. Largo, Grupo de Investigación en Microbiodiversidad y Bioprospección, Universidad Nacional de Colombia, Medellín, Colombia, Colombia
• Rafael J. Vivero, Grupo de Investigación en Microbiodiversidad y Bioprospección, Universidad Nacional de Colombia, Medellín, Colombia, Colombia
• Howard Junca, RG Microbial Ecology: Metabolism, Genomics & Evolution, Div. Ecogenomics & Holobionts, Microbiomas Foundation, Colombia
• Claudia X. Moreno-Herrera, Grupo de Investigación en Microbiodiversidad y Bioprospección, Universidad Nacional de Colombia, Medellín, Colombia, Colombia
• Clara Saldamando-Benjumea, Grupo de Biotecnología Vegetal UNALMED-CIB. Facultad de Ciencias, Universidad Nacional de Colombia, Medellín, Colombia, Colombia
• Gloria E. Cadavid-Restrepo, Grupo de Investigación en Microbiodiversidad y Bioprospección, Universidad Nacional de Colombia, Medellín, Colombia, Colombia

Lambda-cyhalothrin is a pyrethroid insecticide used to control fall armyworm (Spodoptera frugiperda), a pest that mainly affects corn, but also rice and other crops of economic importance. While effective for pest control, it accumulates in the environment, posing risks to human health and ecosystem stability. Moreover, due to continuous use, some insects have developed resistance to it. The mechanisms used by this insect to acquire insecticide resistance have been associated with their genetic potential and members of its gut microbiome. The Enterococcus genus has been classified as a core component of S. frugiperda's gut, sparking strong interest in determining its role in this microenvironment. To identify potential genes related to lambda-cyhalothrin degradation, we performed a genomic analysis of a strain we isolated from S. frugiperda's gut identified as belonging to a recently proposed Enterococcus spodopteracolus species. A phylogenomic analysis based on Genome Blast Distance Phylogeny (GBDP) values and intergenomic distances accurately assigned a taxonomic identity to the isolate. Average nucleotide identity values with the type species of E. spodopteracolus higher than 95% further supported this result. An additional analysis using 4 independent bacterial marker genes (16S rRNA, rpoB, gyrB and recA) complemented the taxonomic species identification of this strain. To further characterize this strain, biosynthetic gene clusters, antimicrobial resistance genes, plasmid, and prophage-associated sequences were identified using antiSMASH, Resfinder, CARD, PLASMe and PHASTEST. Genome annotation revealed potential dehydrogenases and esterases that might be involved in the degradation of lambda-cyhalothrin. To complement this data, we ran customized protein alignments identifying a conserved domain previously reported in experimentally validated enzymes for pyrethroid degradation. Finally, a comparative genomics analysis of the core genome of four available E. spodopteracolus genomes and our strain indicated high similarity in the features they share. This information enriches existing data on this recently described species and paves the way for future in silico and transcriptomics analyses to confirm the biodegradation potential of this isolate.

• Alejandro Caro-Quintero, Universidad Nacional de Colombia, Colombia
• Roxana Yockteng, AGROSAVIA, Colombia
• Deisy Lisseth Toloza Moreno, AGROSAVIA, Colombia
• José Ives Pérez Zúñiga, AGROSAVIA, Colombia

The diversity and function of microbiota associated with perennial tropical plants like Theobroma cacao remain relatively unexplored. However, mounting evidence suggests that these plant-microbe interactions have shaped the ecology and evolution of their hosts, influencing critical processes such as development, growth, and health. Understanding how agriculture and domestication affect these microbial communities is vital for unraveling the complex relationship between plants and microbes. Microbiota associated with T. cacao may be recruited from the surrounding environment via the roots or inherited from maternal plants through seeds. Both recruitment and inheritance processes likely play a significant role in shaping the cacao microbiome. In this study, we present recent findings on these mechanisms. Through the CacaoBio program, we conducted an expedition to investigate the microbiomes of T. cacao and its close relatives in their native environments across the Amazon and Chocó regions. Our research identified key drivers of microbial community structures across different plant tissues. Notably, we discovered distinct microbial recruitment strategies in the rhizospheres of geographically isolated T. cacao plants, setting them apart from other Theobroma species. Additionally, we examined seed-borne endophytes to evaluate microbial vertical transmission in commercial, recently liberated, and landrace cacao genotypes. Our results revealed a higher abundance of Pseudomonas and Pantoea in landrace and recently liberated genotypes, while commercial genotypes harbored a wider variety of bacteria, though in lower abundance. Some of these seed-borne endophytes, including Bacillus, Pantoea, and Pseudomonas, were isolated and demonstrated strong plant growth-promoting properties, such as the production of indole acetic acid and ACC deaminase activity. Our findings suggest that domestication may lead to the loss of key microbial associates essential for seedling establishment and development. Furthermore, the biotechnological potential of the cacao microbiota to enhance productivity and quality is being explored. We have successfully leveraged seed-borne plant growth-promoting bacteria to improve seedling establishment and grafting success in regional cacao genotypes.

• Juan Fernando Meza Prada, Universidad de los Andes, Colombia
• Miguel Fernández Niño, Instituto de Agroquímica y Tecnología de alimentos (IATA), Spain
• Eddy Bautista, Corporación colombiana de investigación agropecuaria (Agrosavia), Colombia
• María José Chica, Casa Luker S.A, Colombia
• Ricardo Rivas Hernandez, Universidad de los Andes, Colombia
• Maria Francisca Villegas, Universidad de los Andes, Colombia
• Andres Gonzalez, Universidad de Los Andes, Colombia

Theobroma cacao is an important global crop with two distinct market categories: bulk cocoa and fine flavor cocoa. In Colombia, the production of fine flavor cocoa drives social and economic development in areas affected by armed conflict. However, the transformation process of cocoa into chocolate is hindered by cadmium accumulation, posing significant health risks and limiting its export to new markets. Previous studies have applied physical, chemical, and biological methods for heavy metal remediation; moreover, these methods have been found to be costly and inefficient at the cadmium concentrations present in cocoa crops. The use of microorganisms such as certain Bacillus strains offers a promising alternative for cadmium bioremediation during cocoa processing. Furthermore, the development of genome-scale metabolic models is a powerful technique that allows for the simulation of the complex metabolic networks of these bioremediation microorganisms, providing insights into their metabolic pathways, regulatory mechanisms, and potential to optimize cadmium removal. In this study, we developed a genome-scale metabolic model of cadmium-resistant Bacillus xiamenensis present in cocoa fermentation. The genome was annotated from Illumina sequencing to generate various draft models using CarveMe and Raven Toolbox, which were consolidated into a single model undergoing continuous manual curation using literature and databases such as BiGG models, KEGG and Metacyc, thus identifying genes and proteins in metal transport pathways. The model was contextualized through a laboratory-scale culture, measuring biomass, cadmium, sugars and organic acids. The model presented a metabolic network with 1221 metabolites, 1769 reactions and 890 genes. In addition, it shows over 90% consistency in stoichiometry, mass balance and connectivity. Our results demonstrate the potential of B. xiamenensis as an alternative to mitigate cadmium during cocoa fermentation and the relevance of genome-scale models for understanding metal uptake metabolism in microorganisms.

• Valeria Faggioli

The twenty-first century is the era of omics technologies, which are primarily focused on the generation and analysis of molecular data within organisms. In the last two decades, researchers in laboratories have generated enormous amounts of data due to the rapid development of high-throughput next-generation sequencing (NGS) technologies. The data generated by these technologies can be directly applied to agricultural developments. The agricultural system, which is directly connected to the soil, can promote plant growth either in a free-living state or associated with the rhizosphere region. Pathogenic organisms can affect plant health and remain in the soil for decades, rendering it unproductive. Additionally, a great variety of species remain in grains after harvest, altering further processes such as fermentation, storage, and commercialization. Identification through DNA barcoding in soil microorganisms is also a new avenue, assisted by various bioinformatics tools. Microbial systems biology provides another means to explore data from different metabolic pathways and taxonomic genes for valid conclusions about microbial activity. Overall, in silico tools, including databases and software, can help reduce the

• Helder Nakaya

In this seminar, I will explore the transformative role of Artificial Intelligence (AI) in medicine, focusing on its integration with systems biology at the single-cell level. We will show how spatial transcriptomics is revolutionizing our understanding of tissue architecture and disease progression. By leveraging these cutting-edge technologies, we aim to uncover new insights into human diseases and advance precision medicine approaches.

• Lucia Ramirez-Navarro, Wellcome Sanger Institute, United Kingdom
• Tobi Alegbe, Open Targets, United Kingdom
• Bradley T. Harris, Wellcome Sanger Institute, United Kingdom
• Monika Krzak, Wellcome Sanger Institute, United Kingdom
• Mennatallah Ghouraba, Barts Cancer Institute, United Kingdom
• Michelle Strickland, Wellcome Sanger Institute, United Kingdom
• Marcus Tutert, Wellcome Sanger Institute, United Kingdom
• Noor Wana, Wellcome Sanger Institute, United Kingdom
• May Hu, Wellcome Sanger Institute, United Kingdom
• Jasmin Ostermayer, Pasteur Institute, France
• Jason Skelton, Wellcome Sanger Institute, United Kingdom
• Matiss Ozols, Wellcome Sanger Institute, United Kingdom
• Vivek Iyer, Wellcome Sanger Institute, United Kingdom
• Gareth-Rhys Jones, University of Edinburgh, United Kingdom
• Miles Parkes, Addenbrooke’s Hospital, United Kingdom
• Rebecca McIntyre, Wellcome Sanger Institute, United Kingdom
• Cristina Cotobal Martin, Wellcome Sanger Institute, United Kingdom
• Tim Raine, Addenbrooke’s Hospital, United Kingdom
• Carl A. Anderson, Wellcome Sanger Institute, United Kingdom

Crohn’s disease (CD) is an immune-mediated condition and a subtype of inflammatory bowel disease (IBD) characterised by chronic inflammation and damage in the gastrointestinal tract. Although the exact causes are not fully understood, CD is hypothesised to be driven by a dysregulated immune response against commensal gut bacteria in genetically susceptible individuals. Due to its accessibility, blood is frequently used for IBD research; however, immune cells are highly dynamic and adopt distinct transcriptional profiles depending on their tissue environment. It remains unclear whether circulating immune cells accurately reflect the tissue-specific disease landscape and predict mucosal inflammation.

To address this, we generated a single-cell atlas of paired ileal and circulating immune cells from 125 CD patients, comprising 854,726 immune cells across 47 distinct cell states. We identified two clusters of CD4+ tissue-resident memory T cells (Trm), including a previously characterised pathogenic subset with known innate and effector functions, that are key producers of type 1 inflammatory cytokines. Moreover, we identified their circulating counterparts, termed ex-Trms, which egress from the tissue and enter the bloodstream. While ex-Trms have been previously characterised to exit human skin and murine intestine, we provide evidence of gut-derived ex-Trms in human gut and CD patients.

Notably, the transcriptional profiles of ex-Trms strongly correlated with mucosal inflammation, as measured by the endoscopic scores, similar to their tissue-resident counterparts. These correlations were unique to ex-Trms, as other immune cell types shared between blood and gut did not exhibit the same pattern. Key pathways that were correlating with mucosal inflammation included Interferon gamma (IFNγ), Tumor Necrosis Factor (TNF), JAK-STAT as well as interleukin signalling with expression of IL7, IL22, IL21, IL1, and IL12. Many of these pathways were also shared with intestinal Tregs and macrophages, further reflecting the inflammatory response in the gut.
In summary, our study provides a comprehensive atlas of immune cell states in CD, revealing the potential of circulating ex-Trms to capture tissue inflammation signatures. These findings offer new insights into the immune cell dynamics in CD and suggest that ex-Trms could serve as potential blood-based biomarkers for monitoring disease activity.

• Susmita Datta, University of Florida, United States
• Dongyuan Wu, Department of Biostatistics, University of Florida, United States
• Michael Sekula, University of Louisville, United States
• Jeremy Gaskins, University of Louisville, United States

Understanding cell microenvironments from spatially resolved transcriptomics data is a cutting-edge approach in biomedical research. This innovative method enables scientists to investigate the spatial organization of cells near diseased tissues and identify their inter- and intracellular communications through biochemical signaling, crucial for elucidating disease mechanisms and developing targeted treatments.
Traditionally, most computational methods provide ad hoc measurements to estimate intercellular communication. While straightforward, these methods often lack the accuracy and reliability that robust statistical models can offer. To address these limitations, our research proposes a novel generalized linear regression model known as Bayesian Tweedie Modeling of Communications (BATCOM) [1]. This model is designed to infer cellular communications from spatially resolved transcriptomics data, particularly spot-based data, by estimating communication scores between cell types while considering their spatial distances.
BATCOM offers a nuanced and statistically sound approach to understanding cellular interactions. By incorporating spatial distance into the communication score estimations, BATCOM provides a more accurate representation of how cells interact within their microenvironments, significantly improving upon traditional methods that often overlook the spatial aspect of cellular communications.
Additionally, we explore a frequentist approach using the generalized additive model (GAM) framework. Implemented in the associated TWCOM [2] software in R, this approach enhances scalability and integration, making it more user-friendly for researchers. We demonstrate the superiority of our method using single-cell and spatial RNA-seq data for cutaneous squamous cell carcinoma, the second most common skin cancer in the USA.
These advancements in statistical modeling are crucial for advancing our understanding of disease mechanisms. Accurate inference of cellular interactions can reveal new insights into how diseases develop and progress at the cellular level, informing the development of more effective treatments and interventions.
By integrating BATCOM and TWCOM into user-friendly software, we ensure these advanced statistical methods are accessible to a wide range of researchers, accelerating biomedical discoveries and improving patient outcomes. Our research enhances our understanding of disease mechanisms, paving the way for new discoveries and therapeutic strategies in biomedical research.
REFERENCES
[1] Wu D, Gaskins JT, Sekula M, Datta S. Inferring Cell-Cell Communications from Spatially Resolved Transcriptomics Data Using a Bayesian Tweedie Model. Genes (Basel). 2023 Jun 28;14(7):1368. doi: 10.3390/genes14071368. PMID: 37510272; PMCID: PMC10379215.
[2] Dongyuan Wu, Susmita Datta, TWCOM: an R package for inference of cell–cell communication on spatially resolved transcriptomics data, Bioinformatics Advances, Volume 4, Issue 1, 2024, vbae101, https://doi.org/10.1093/bioadv/vbae101

• Javier De Las Rivas, Cancer Research Center (CiC), Consejo Superior de Investigaciones Cientificas (CSIC) & University of Salamanca (USAL), Spain
• Natalia Alonso-Moreda, Cancer Research Center (CiC), Consejo Superior de Investigaciones Cientificas (CSIC) & University of Salamanca (USAL), Spain
• Emma Perez-Garcia, Cancer Research Center (CiC), Consejo Superior de Investigaciones Cientificas (CSIC) & University of Salamanca (USAL), Spain
• Enrique De La Rosa, Cancer Research Center (CiC), Consejo Superior de Investigaciones Cientificas (CSIC) & University of Salamanca (USAL), Spain
• Alberto Berral-Gonzalez, Cancer Research Center (CiC), Consejo Superior de Investigaciones Cientificas (CSIC) & University of Salamanca (USAL), Spain
• Jose Manuel Sanchez-Santos, Cancer Research Center (CiC), Consejo Superior de Investigaciones Cientificas (CSIC) & University of Salamanca (USAL), Spain

Single-cell RNA sequencing (scRNA-seq) is a modern technology that aims to provide detailed profiling of the transcriptome of individual cells as well as accurate detection of cell-specific gene markers. However, despite the massive production of thousands of single-cell datasets and many cell atlas resources in recent years, scRNA-seq technology still does not provide full transcriptome coverage (at least equivalent to bulk RNA sequencing) and still contains considerable technical noise (mainly due to the low reproducibility of global expression from one cell to another and the high variability of specific signal between cells of the same type). This situation, together with the complexity of single cell data, makes it urgent to design robust bioinformatics protocols that combine different optimized methods with clear steps and to generate user-friendly workflows. In this context, our group has been working on the development of such protocols for the analysis of scRNA-seq data, focusing on human data and human cells (especially in the complex scenario of hematological and immune cells) in order to obtain reproducible results and a better profile of cell-specific gene markers.
In this paper, we present a detailed R script and step-by-step protocol to show how to perform a scRNA-seq data analysis. From an analytical and methodological point of view, it is important to have some prior knowledge of scRNA-seq technology and also a minimal understanding of the steps required to analyze bulk RNA-seq and scRNA-seq data. Of course, we base our protocol on relevant and useful pipelines and tutorials, such as those generated within Bioconductor: OSCA (Orchestrating Single-Cell Analysis, osca.bioconductor.org) (Amezquita et al. 2020, PMID: 31792435); the protocol of Marioni and collaborators (Lun et al. 2016, PMID: 27909575). As a useful, feasible example, our protocol presents the analysis of a set of 7,643 cells from healthy human bone marrow (BM) and peripheral blood (PB) samples derived from the public dataset: GSE149938 (www.ncbi.nlm.nih.gov/geo/). To facilitate appropriate filtering and subsetting, we work with CD marker genes and remove erythrocytes from the raw data (which are highly differentiated cells, lacking a nucleus, and very abundant in blood). Finally, our previous work comparing deconvolution methods (Alonso-Moreda et al. 2023, PMID: 37445946) allows a good evaluation of the complementary value of scRNA-seq with bulkRNA-seq data, understanding that the combination of both techniques provides a better picture of the cell-specific expression profiles.

• Andrea Riaño, Grupo Biología y Control de Enfermedades Infecciosas, Universidad de Antioquia UdeA, Medellín, Colombia, Colombia
• Omar Triana, Grupo Biología y Control de Enfermedades Infecciosas, Universidad de Antioquia UdeA, Medellín, Colombia, Colombia
• Geysson Fernandez, Grupo Biología y Control de Enfermedades Infecciosas, Universidad de Antioquia UdeA, Medellín, Colombia, Colombia

Chagas disease, caused by the protozoan Trypanosoma cruzi, presents a significant public health challenge in Latin America. Chronic Chagas cardiomyopathy (CCC), a severe disease manifestation, is characterized by symptoms ranging from fever and fatigue to fatal cardiac complications. In different heart failure conditions, the Interactome, specifically the intercellular ligand-receptor communication network, has been identified to be compromised, contributing to disease progression, however, is not fully understood how the interactome affects the remodeling of cardiac tissue. Given the complex physiological interactions between different cellular types, we hypothesize that CCC alters specific patterns of cell-cell communication within cardiac tissue, contributing to systemic severity and disease progression. To elucidate these alterations, we constructed a comprehensive analysis of the CCC interactome using differential expression profiles from mRNA sequencing data of CCC patients, coupled with single-cell RNA-seq from healthy human tissues. We aimed to identify differentially expressed transcripts involved in autocrine and paracrine communication pathways.

Analysis of the gene expression profile of cardiac tissue from eight patients with CCC revealed 3212 differentially expressed transcripts. Among these transcripts we found several types of histones and their variants, protein G receptors, extracellular components, etc, yet a significant amount of transcripts were associated with the immune system, specifically inflammatory pathways and response to the presence of the pathogen, potentially indicating a feedback loop reinforcing the inflammatory response in CCC. Moreover, enrichment analysis shows that most transcripts involved in immune system pathways, were up regulated, and the cardiac specific, cell death, and metabolic pathways showed a range of up and down regulation. Ligand-receptor interactions were assessed, revealing high communication scores for HLA-G_LILRB2, associated with inflammation regulation, and COL6A6-SDC1, linked to ECM remodeling and fibrosis. These results reflect complex interactions that influence tissue remodeling, fibrosis, and inflammation in CCC.

The findings suggest that the interplay between altered gene expression in cardiac tissue communication may significantly contribute to the pathophysiology of CCC. The upregulation of inflammatory pathways and the identification of key ligand-receptor interactions highlight potential targets for therapeutic intervention. These insights into the molecular mechanisms of CCC could guide the development of novel treatment strategies to mitigate the systemic severity and progression of Chagas disease.

• Luis Alberto Meza-Cova, Laboratorio de Biología Computacional, Instituto Nacional de Enfermedades Respiratorias Ismael Cosío Villegas., Mexico
• Yair Romero-López, Laboratorio de Biopatología Pulmonar, Facultad de Ciencias, Universidad Nacional Autónoma de México, Mexico
• Yalbi Itzel Balderas-Martínez, Laboratorio de Biología Computacional, Instituto Nacional de Enfermedades Respiratorias Ismael Cosío Villegas., Mexico

Organoids, three-dimensional systems generated by stem cells, can self-organize into organ-like structures and are used in lung research to overcome limitations of traditional models. However, they do not fully reflect the complexity of the lung, as the cellular populations generated are limited. This underscores the need for further research. Certain transcription factors can activate cellular signaling pathways to differentiate cell types, identified from single-cell sequencing through computational algorithms. This can serve as a basis for constructing biological networks of transcription factors and their target genes, which control cell fate and state in complex circuits. Our objective is to identify the transcriptional regulatory networks of the signaling pathways involved in the development of an alveolar organoid. Constructing these networks will help understand the cellular mechanisms of organoid development and their relationship to the regeneration and development of a healthy lung epithelium.
Single-cell sequencing experiments from mouse lung organoids reported in public NCBI databases were selected. Data quality was assessed, normalized, and cell markers and cell stage of the populations found in organoid development were identified using the Seurat package. Subsequently, data from different experiments were integrated, and transcriptional regulatory networks were constructed with the SCENIC package. The obtained regulons were manually reviewed and associated with relevant signaling pathways in alveolar organoid development.
Specific cell markers were identified to classify AT1, AT2, Krt+, and proliferative AT2 cell populations. Additionally, four transitional populations were identified, showing shared markers among themselves, resembling those of AT2 cells. A total of 316 regulons were identified across all cell populations, and through a regulon specificity test with the SCENIC package, those specific to each cell population involved in alveolar organoid development were identified. An enrichment analysis showed that genes regulated by the transcription factor Atf5, from the population called Krt+, are implicated in processes related to alveolar development, collagen synthesis, response to organic substances, among others.
This study presents the integration of different single-cell experiments and the inference of transcriptional regulatory networks of the different cell populations involved in the development of alveolar organoids, which help us understand the process of alveolar epithelial differentiation. This understanding is key to understanding the development and regeneration of the epithelium. The regulons identified in differentiating populations are crucial for identifying the signaling pathways that trigger the formation of organoids with a higher number of cells or populations.

• Hernán Hoyos-Maya, Universidad de Antioquia, Colombia
• Rafael Posada-Duque, Universidad de Antioquia, Colombia
• Said Arevalo-Alquichire, Harvard Medical School, United States
• Geysson Javier-Fernandez, Universidad de Antioquia, Colombia
• Joseph Arboleda-Velasquez, Harvard Medical School, United States
• Kenneth Kosik, University of California, United States
• Santiago Hinestroza-Morales, Universidad de Antioquia, Colombia
• Michael O'Hare, Harvard Medical School, United States
• Paula Perez-Corredor, Harvard Medical School, United States
• Timothy Vanderleest, Harvard Medical School, United States
• Harper Gordon, Harvard Medical School, United States
• Sarah Eger, University of California, United States
• Camila Almeida, University of California, United States
• Andrés Villegas, Universidad de Antioquia, Colombia
• Leo Kim, Harvard Medical School, United States
• Yakeel Quiroz, Harvard Medical School, United States
• Francisco Lopera, Universidad de Antioquia, Colombia

Autosomal dominant cerebral arteriopathy with subcortical infarcts and
leukoencephalopathy (CADASIL) is a rare hereditary neurovascular disorder caused by
mutations in the NOTCH3 gene, affecting approximately 1 in 100,000 individuals globally. Characterized by progressive degeneration of small arteries in the brain, CADASIL primarily impacts mural cells within the gliovascular unit (GVU), with limited understanding of its effects on other cell types and a lack of effective treatment options. This study aims to elucidate the pathology of CADASIL across various GVU cell types to identify potential therapeutic targets. To achieve this, we performed single-cell RNA sequencing (scRNA-seq) on brains from NOTCH3 knock-out (N3KO) mice and postmortem tissues from human CADASIL patients. In N3KO mice, our results in N3KO mice identified 13 cell groups among 39,158 cells, with the predominant types being microglia (44%), oligodendrocytes (30%), astrocytes (16%), and vascular cells (3%). In human samples, we identified 13 cell groups among 88,961 cells, with the most prevalent types being oligodendrocytes (37%), astrocytes (9%), microglia (7%), and vascular cells (5%). Interestingly, when comparing the changes in cellular fractions, we observed a decrease in endothelial and smooth muscle cells in the murine model, whereas this pattern was not found in humans. However, an increase in microglia was noted in humans, indicating microgliosis, a typical characteristic of the disease. Additionally, when we explored the regulated genes, we found that genetic programs related to angiogenesis, inflammation, and the NOTCH pathway were altered in various cell types in both mouse and human samples. Specifically, angiogenesis-related genes were altered in microglia, endothelial
and smooth muscle (SMC) cell types, inflammation-related genes in microglia, astrocytes, endothelial and SMC cell types, and NOTCH pathway-related genes in astrocytes, endothelial and SMC cell types. Transcriptional control analysis revealed regulatory functions of key transcription factors, mainly RUNX1, PPARD, SPI1, and YAP1, which were common to both mouse and human models and are related to the NOTCH, inflammatory and cell proliferating pathways. Overall, these results establish an approach to discern the regulatory mechanisms controlling the genetic program in the brain, not only in SMC but also in other cell types such as microglia and astrocytes, where inflammation and angiogenesis were found. These findings lay the groundwork for future research aimed at developing novel therapeutic strategies for CADASIL.

• Ana Laura Hernández-Ledesma, Laboratorio Internacional de Investigación sobre el Genoma Humano (LIIGH), Universidad Nacional Autónoma de México, Mexico
• Sofia Salazar-Magaña, Laboratorio Internacional de Investigación sobre el Genoma Humano, UNAM. ENES Unidad Juriquilla, UNAM, Mexico
• Evelia Lorena Coss-Navarrete, Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México, Mexico
• Diego Ramírez-Espinosa, Laboratorio Internacional de Investigación sobre el Genoma Humano, UNAM. ENES Unidad Juriquilla, UNAM, Mexico
• Lizbet Tinajero-Nieto, Hospital General Regional No. 1, Instituto Mexicano del Seguro Social, Querétaro, Mexico
• Estefania Torres-Valdez, Hospital General Regional No. 2, Instituto Mexicano del Seguro Social, Querétaro, Mexico
• Angélica Peña-Ayala, Hospital General Regional No. 1, Instituto Mexicano del Seguro Social, Querétaro, Mexico
• Guillermo Felix-Rodríguez, Hospital Star Médica Querétaro, Mexico
• Florencia Rosetti, Dpto de Inmunología y Reumatología Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán., Mexico
• María Gutierrez-Arcelus, Department of Pediatrics, Boston Children’s Hospital and Harvard Medical School., United States
• Deshiré Alpízar-Rodríguez, Unidad de Investigación, Colegio Mexicano de Reumatología., Mexico
• Alejandra Medina-Rivera, Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México., Mexico

BACKGROUND: Systemic lupus erythematosus (SLE) is characterized by the development of immune response towards self-components. Immune tolerance is a group of mechanisms that favors an unresponsive state towards self-antigens; dendritic cells (DCs) are key to regulating the immune response. Tolerogenic dendritic cells (tolDCs) exhibit the potential to restore tolerance due to their capability to induce anti-inflammatory and regulatory responses, which has made them a target of interest in biomedical research.
AIM: To identify the transcriptomic profiles of DCs during tolerance induction on women with SLE.
METHODS: Volunteers were recruited through the Mexican Lupus Registry. Monocytes were isolated from blood with Lymphoprep and a magnetic beads cell-enrichment kit. Monocytes were differentiated to monocyte-derived dendritic cells (moDCs) using GM-CSF and IL-4 (q.48h); whereas tolDCs were obtained using GM-CSF and IL-4 (q.48h) and IL-10 (q.24h). Cells were cultured under these conditions for 8 days. RNA-sequencing data was analyzed using the nf-core/rnaseq pipeline and differential gene expression was assessed using DESeq2 R package. Functional enrichment and regulatory network analyses were performed using g:Profiler and pySCENIC. Data visualization was done using ggplot2 and ComplexHeatmap R packages.
RESULTS: Samples were obtained from 23 women with SLE and 10 controls. In an initial comparison no differences were observed across cell types and between people with SLE and controls. When we compared gene expression in the differentiation process towards moDCs or tolDCs we identified a group of genes that allow us to differentiate moDCs from tolDCs. From these genes, 443 genes showed a differential expression between SLE and controls, functional enrichment analysis associated these genes with metabolic and catabolic processes. Regarding genes expressed differentially between moDCs and tolDCs we identified 944 genes enriched for immune diseases and transport and catabolism processes. We then explored the expression of genes previously associated with SLE, identifying a set of genes including ITGAM, IRF7, ETS1 and DNASE1L3, which allows clustering of samples by cell type. We found that the differential expression profiles among cell types remains even when we consider the use of corticoids and disease activity.
CONCLUSIONS: We identified different transcriptomic profiles between tolDCs and moDCs in dendritic cells from women with SLE. Identifying genes that contribute to the differentiation of DCs towards tolerance, and the further evaluation of the regulatory mechanisms involved in this process will contribute to the design and development of specific and personalized approaches for the diagnosis and treatment of SLE.

• Gwendoline Lozachmeur, French National Sequencing Center / Genoscope, France
• Marco Antonio Mendoza, French National Sequencing Center / Genoscope, France

Developments on spatially-resolved transcriptomics (SrT) are providing means to interrogate organ/tissue architecture from the angle of the gene programs defining their molecular complexity. Beyond the over-discussed aspects concerning their resolution, their capacity to access to other type of molecular readouts (epigenetics, proteomics), their technical limitations issued from the biological specimens conservation (FFPE vs fresh frozen tissues), but also their current elevated costs, avoiding to generate volumetric views of their molecular complexity, represent major challenges for the coming years.
Previously we have presented a double-barcoded DNA array strategy for SrT, as illustrated by the setup of a three-dimensional molecular cartography of human cerebral organoids (Lozachmeur et al; 2023). Herein, we have applied our in-house SrT methodology for performing a spatial cartography in a mdrna1-/- mouse model of spontaneous colitis.
Our current data, provided means to identify a variety of cell-types composing the colon architecture, but also to reveal inflammatory response signatures relative to a control animal condition. Overall, our preliminary data clearly illustrates the power of using this technology for interrogating tissue complexity.

• Ana Beatriz Deleame Medeiros, A.C.Camargo Cancer Center, Brazil
• Gabriel Oliveira dos Santos, A.C.Camargo Cancer Center, Brazil
• José Claudio Casali da Rocha, A.C.Camargo Cancer Center, Brazil
• Samuel Aguiar Junior, A.C.Camargo Cancer Center, Brazil
• Vanessa Nascimento Kozak, Hospital Erasto Gaertner, Brazil
• Gustavo Nóriz Berardinelli, Barretos Cancer Center, Brazil
• Augusto Antuniazzi, Barretos Cancer Center, Brazil
• Rui Manuel Reis, Barretos Cancer Center, Brazil
• Dirce Maria Carraro, A.C.Camargo Cancer Center, Brazil
• Giovana Tardin Torrezan, A.C.Camargo Cancer Center, Brazil

MUTYH-associated polyposis (MAP) is responsible for 0.7% of all colorectal cancers (CRC) and 13% of all polyposis. MUTYH is a base excision repair gene and, when mutated, leads to somatic G:C-T:A transversions. The KRAS c.34G>T; G12C and the PIK3CA c.1636C>A; Q546K are rare somatic mutations in CRC, occurring in less than 2% of sporadic tumors and in 90% and 37% (respectively) of MAP CRC. Previous studies have shown that the identification of either of these two mutations could identify MAP patients with around 90% of sensitivity because both are associated to the presence of MAP-related tumor mutational signatures (TMS) SBS18 and SBS36 in adenocarcinomas. The aims of the study are: to evaluate the detection rate of germline pathogenic/likely pathogenic variants (GPVs) in MUTYH in patients with CRC and KRAS-G12C somatic mutation; to evaluate the utility of investigate KRAS-G12C, PIK3CA-Q546K and the TMS SBS18/36 in MAP patients tumors for VUS reclassification. For the first aim, we evaluated the five most common Brazilian variants in MUTYH (p.Tyr151Cys, p.Gly368Asp, p.Arg213Trp, p.Ala357fs and exon 4-16 deletion) through multiplex PCR followed by NGS of amplicons. Patients with only 1 variant in heterozygosis were evaluated by MUTYH complete sequencing. For the second aim, we are evaluating the detection frequency of KRAS-G12C and PIK3CA-Q546K in tumor tissues of diagnosed MAP patients through amplicon-based NGS, and the detection of SBS18/36 through whole exome sequencing (WES) using the Genome Analysis Toolkit (GATK) algorithms. Of the 220 KRAS-G12C patients assessed, 25 (11.3%) have shown GPVs in MUTYH and 16 (7.2%) were classified as MAP. The MAP detection rate in patients under 60 years old was 12% (13/110). GPVs were associated with an earlier age of CRC onset and the presence of polyps (p=0.002 and p=0.004). Regarding the second aim, of the 16 MAP patients, 8/17 adenomas (47%) and 12/13 (92.3%) adenocarcinomas were positive for KRAS-G12C; for PIK3CA-Q546K, the values were 0 and 4/13 (30.7%), respectively. Only one low-grade adenoma (1/8 – 12.5%) showed the SBS36 mutational signature with a frequency of 81.6%. An adenocarcinoma from a suspected MAP patient harboring the VUS p.Pro273Arg revealed the presence of KRAS-G12C, supporting its reclassification as likely pathogenic (PP3_strong, PM2_supp, PP4). To the moment, it can be concluded that the high detection rate of GPVs in MUTYH in CRC and KRAS-G12C patients indicates its potential use as a biomarker to expand the detection of MAP patients.

• David Requena, New York University, United States
• Jack Medico, Rockefeller University, United States
• Luis Soto-Ugaldi, Rockefeller University, United States
• Mahsa Shirani, Rockefeller University, United States
• James Saltsman, Rockefeller University, United States
• Michael Torbenson, Mayo Clinic, United States
• Philip Coffino, Rockefeller University, United States
• Sanford Simon, Rockefeller University, United States

Fibrolamellar Hepatocellular Carcinoma (FLC) is a rare liver cancer affecting adolescents and young adults without history of underlying viral hepatitis, cirrhosis, or other known risk factors. FLC is misclassified as a subtype of HCC, causing patients to receive HCC drug therapy, ineffective against FLC. Therefore, defining the molecular identity of FLC is critical for developing and administering proper drug treatment.

Most FLC patients present somatic heterozygous deletions in chromosome 19p13.12, causing a fusion transcript connecting exon 1 of DNAJB1 and exons 2-10 of PRKACA (the catalytic subunit of PKA). However, patients with FLC-like histopathology but different genetic alterations involving PKA have been reported.

Current FLC RNA-seq studies have limited agreement in their differentially expressed genes, attributable to the use of small datasets, lack of paired normal samples and inadequate bioinformatics methods. To better understand FLC and its relation with FLC-like and other liver tumors, we collected over 1500 samples in the largest study of liver cancer to date. These were processed using state-of-the-art bioinformatics methods, including filters of detectability, consistency and validation steps that we devised to find transcriptomic signatures unrestricted to experimental processing.

We sequenced the whole transcriptome of 127 FLC and 2 FLC-like frozen tissue samples and reprocessed 73 FLC and 18 FLC-like samples from external datasets. A transcriptomic FLC signature of 287 up- and 406 down-regulated genes was identified, which demonstrated that FLC and FLC-like tumors with diverse dysregulations of PKA are a single disease with a common transcriptome, rather than a collection of diverse diseases with similar pathologic features. We studied this signature at different omic levels. At genome, exome and methylome levels, no recurrent alterations associated with this signature were found. Interestingly, HDAC1 (which interacts with PRKACA) was found to be associated with transcription factors targeting genes of the FLC signature. Moreover, we performed spatial transcriptomics and distinguished which of the different cell types in the tumor are giving origin to the transcriptional dysregulations in the FLC signature. Primary tumors and metastases showed high similarity, with only differential expression in 0.6% of their genes. They are associated with tumor proliferation, maintenance, and immune evasion, and may help elucidate the metastatic process. We further used the FLC signature to validate organoid and PDX models. Our analysis was expanded to other liver cancers, analyzing 1192 tumor and normal samples of HCC, hepatoblastoma, and intrahepatic cholangiocarcinoma, identifying their transcriptomic signatures and comparing them with FLC.

• Ricardo Chinchilla Monge, Universidad de Costa Rica, San José, Costa Rica, Costa Rica
• Alexis G. Murillo Carrasco, Center for Translational Research in Oncology and Comprehensive Center for Precision Oncology, São Paulo, Brazil, Peru
• Annie Cristhine M.S. Squiavinato, Program of Immunology and Tumor Biology, Brazilian National Cancer Institute (INCA), Rio de Janeiro 22071-030, Brazil, Brazil
• Cristóvão Lanna, Laboratory of Bioinformatics and Computational Biology, Brazilian National Cancer Institute (INCA), Brazil, Brazil
• Daniela Bizinelli, Interunit Graduate Program in Bioinformatics, Institute of Chemistry, University of São Paulo, Brazil, Brazil
• Danielle Carvalho, Program of Immunology and Tumor Biology, Brazilian National Cancer Institute (INCA), Rio de Janeiro 22071-030, Brazil, Brazil
• David Adams, Experimental Cancer Genetics. Wellcome Sanger Institute. Hinxton, CB10 1SA. Cambridge, UK, United Kingdom
• Silvana Pereyra, Unidad Académica de Genética, Facultad de Medicina, Universidad de la República. Montevideo, Uruguay, Uruguay
• Flavia Aguiar, Program of Immunology and Tumor Biology, Brazilian National Cancer Institute (INCA), Rio de Janeiro 22071-030, Brazil, Brazil
• Patricia Possik, Program of Immunology and Tumor Biology, Brazilian National Cancer Institute (INCA), Rio de Janeiro 22071-030, Brazil, Brazil

Colorectal cancer (CRC) ranks as the third most frequently diagnosed malignancy and the second leading cause of cancer-related death (both sexes, 2022, Global Cancer Observatory). Although metastases are closely linked to poor prognosis, the mechanisms driving this process are not fully understood. To gain insights into epithelial-mesenchymal transition (EMT), we implemented a computational single-cell approach integrating known EMT-related genes to identify clusters of cells involved in EMT. These clusters were then analyzed to uncover relevant genes for EMT and prognosis.
We gathered single-cell transcriptomics data from the E-MTAB-8410 study available at the Single Cell Expression Atlas (EMBL-EBI, Wellcome Genome Campus). The dataset comprises CRC cells from nine patients. Cells from the surgical margin (normal-adjacent) and all non-tumor microenvironment cells were excluded. We clustered epithelial cells (n=4,693) and non-classified cells (n=174,084) into 22 subpopulations, and additionally clustered sigmoid colon cells (epithelial n=2,478 and non-classified n=19,710) into 15 subpopulations. Overall scores were built to represent EMT and stemness-related features using literature-based gene expression levels.
Furthermore, we evaluated gene sets corresponding to consensus molecular subtypes (CMS) 1-4. Through this analysis, four clusters (out of 22) of total colon cells and two clusters (out of 15) of sigmoid colon cells were highly associated with the EMT (CMS4) profile. We then examined differentially expressed genes (DEGs) within these EMT-putative cells. Among the top 20 DEGs, only one gene (VIM) had previously been described as an EMT marker (literature-based gene list and CMS4).
Additionally, tissue development, cell differentiation, and signaling pathways were enriched using the complete list of overexpressed genes in the EMT-putative cluster. Pseudo-temporal trajectory analysis revealed significant differences in EMT evolution and high VIM expression. We further assessed the contribution of the top 20 DEGs to prognosis in a CRC bulk dataset. Higher levels of IGFBP7 and TIMP1 were significantly associated (p<0.05, log-rank test) with poor prognosis in the Colon Adenocarcinoma (COAD) cohort from The Cancer Genome Atlas (TCGA). HSPB1 and CD63 were confirmed as prognostic factors (p<0.05, log-rank test) in the Rectal Adenocarcinoma cohort (READ-TCGA).
Finally, three DEGs from GSE72970 (TUB4A4, SPARCL1, and CRACR2B) and one DEG from GSE1046445 (PPP1R14D) were consistent with our previous EMT gene list, highlighting the utility of this single-cell approach for identifying novel EMT-related prognostic biomarkers and therapeutic targets in CRC patients.

• Diego Rodríguez

• Gosia Trynka

• Jorge Octavio Lannot, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) - Universidad de Buenos Aires (UBA), Argentina
• Esteban Luciano Rey, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) - Universidad de Buenos Aires (UBA), Argentina
• Marcelo Daniel Gamarra, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) - Universidad de Buenos Aires (UBA), Argentina
• Marcelo Marti, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) - Universidad de Buenos Aires (UBA), Argentina
• Carlos Pablo Modenutti, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) - Universidad de Buenos Aires (UBA), Argentina

Molecular docking is a prominent computational method for predicting protein-ligand complex structures. As expected for an algorithm trained with a very small fraction of protein diversity and even less of the ligand chemical space, the performance is highly system-dependent. Guided docking involves incorporating information about specific protein-ligand interactions for the system under study to modify the scoring function, thus compensating for the weaknesses of multipurpose algorithms. This approach significantly improves conventional docking by enhancing accuracy, reducing computational resources, integrating experimental data, better predicting ligand efficacy, and facilitating novel drug discovery. Previous research conducted by our group demonstrated that including solvent structure information or pharmacophoric restraints in AutoDock4 (AD4) increases accuracy and precision in RMSD values.

Due to their structural complexity and diversity, carbohydrates pose unique challenges in docking simulations, including low binding affinities and significant conformational variability. These issues are exacerbated by scoring functions optimized for small and rigid hydrophobic drugs, resulting in poor performance for carbohydrates. Among docking tools, AutoDock Vina (ADV) is one of the most popular due to its efficiency and performance. Recent ADV derivatives, such as Vina Carb (VC) and GlycoTorch Vina (GTV), have improved prediction accuracy for smaller sugars but still face difficulties with larger saccharides.

In this context, we developed AutoDock Vina Site (VS), integrating guided docking strategies, and evaluated its performance in the challenging task of carbohydrate docking. We built a dataset of protein-carbohydrate complexes and tested it with the following docking protocols/programs: i) ADV, ii) VC, iii) GTV, and iv) VS with water and pharmacophoric-derived information as a positive control. To analyze the docking results comparatively, clustered results were ranked by the resulting 3D-score, specifically the -Zene + Zpop score. This score aims to provide an analysis approach independent of RMSD, to assess performance in cases where no reference structure is available.

Evaluation of VS demonstrates its superior performance compared to existing methods, especially for larger oligosaccharides (tetra- to nonasaccharides). Our findings highlight the effectiveness of guided docking in improving the accuracy and precision of carbohydrate complex predictions, paving the way for better computational tools in glycoinformatics.

• Cleidy Mirela Mogollón, University of Sao Paulo, Brazil
• Leticia Cioca Alves, University of Sao Paulo, Brazil
• Diego Leonardo, University of Sao Paulo, Brazil
• Richard Garratt, University of Sao Paulo, Brazil
• Munira Muhammad Abdel Baqui, University of Sao Paulo, Brazil
• Clarice Izumi, University of Sao Paulo, Brazil

Trypanosoma brucei, the causative agent of Sleeping Sickness, is a neglected tropical disease endemic in sub-Saharan Africa. The parasite has a complex structural element, the Flagellum Attachment Zone (FAZ), which is crucial for linking the single flagellum to the cell body. Within the FAZ, high-molecular-mass proteins are essential for understanding the maintenance of cellular morphology, cytokinesis, and survival. Among these, FAZ10 is a prominent protein we have identified and characterized. FAZ10 assumes a critical function in cytokinesis, furrow positioning, and the overall organization of the FAZ. However, we have limited knowledge about the molecular structure of this intriguing protein. Our objective is to conduct a structural analysis of the central region of FAZ10, which is characterized by low intrinsic disorder and the potential presence of coiled-coil motifs. To generate FAZ10 3D models, we used AlphaFold2. Additionally, we utilized LOGICOIL, MARCOIL2, and LIGPLOT to characterize coiled-coil motifs, IUPRED2A to predict disordered regions, and PyMOL for visualizing and rendering these models. For experimental analyzes, we expressed the central region of FAZ10 through a heterologous system, and purified it using the NiNTA system, Size Exclusion Chromatography (SEC), and SEC-MALS. The structural characterization was made by circular dichroism and Cryo-EM. The central region comprises two confirmed globular domains interconnected by a coiled-coil motif, potentially facilitating protein-protein interactions. Furthermore, we postulate that FAZ10 has the capability to form a protein dimer stabilized by this motif. Expression of the central region yields a protein of ~67 kDa, while the globular domain results in a protein of ~11 kDa. Circular dichroism showed that there is a concordance between the in silico and experimental analyses of the recombinant proteins. In addition, the central region of FAZ10 is a dimer protein, which would lead us to think that the entire protein has a high potential to be dimeric. The structure is shown as a fiber approximately 40 nm in length. Subsequently, we aim to shed light on its role and interactions with other FAZ region proteins, so our forthcoming experiments will involve pulldown assays to capture proteins present in the FAZ of T. brucei in conjunction with recombinant protein expressed. These will be followed by mass spectrometry analysis. Ultimately, this will enhance our understanding of the biology of this parasite—a significant public health concern in Africa. Such insights will be crucial for identifying potential new therapeutic targets.

• Juan Carvajal Dossman, Universidad de los Andes, Colombia
• Laura Guio, HOMI, Fundacion Hospital Pediatrico La Misericordia, Colombia
• Danilo García-Orjuela, Biotecnología y Genética SAS, Colombia
• David Diaz, Universidad de los Andes, Colombia
• Diego Granada, Universidad de los Andes, Colombia
• Andres Delgado Ruiz, Universidad de los Andes, Colombia
• Nestor Gonzalez, Universidad de los Andes, Colombia
• Jennifer Guzmán-Porras, HOMI, Fundación Hospital Pediátrico La Misericordia, Colombia
• Paula Siaucho, Biotecnología y Genética SAS, Colombia
• Jorge Díaz-Riaño, Biotecnología y Genética SAS, Colombia
• Andres Naranjo, HOMI, Fundación Hospital Pediátrico La Misericordia, Colombia
• Silvia Maradei-Anaya, Biotecnología y Genética SAS, Colombia
• Jorge Duitama, Universidad de los Andes, Colombia
• Kelly Garces, Universidad de los Andes, Colombia

Epilepsy is a non-transmissible, chronic neurological disorder characterized by recurrent seizures, affecting approximately 50 million people in the world. Different methods have been developed for efficient diagnosis, including prediction of cases requiring surgical intervention due to lack of effectiveness of drug-based treatments (commonly known as refractory epilepsy). These methods include signal processing using electroencephalography (EEG), analysis of structural magnetic resonance images (MRI), and expression of miRNA biomarkers in peripheral blood. Given the heterogeneity of this data, we developed a software solution to perform an integrated analysis of these data types, to aid diagnosis of epilepsy. Users can load the results of the different exams to generate a common report including the results of the different analyses. The analysis includes a machine learning approach for detection of seizures from EEG data. It also includes a classification model for brain structural anomalies from MRI data. Finally, it includes a classification module based on the expression patterns of blood miRNA data. The software follows a distributed architecture with five main components orchestrated through docker compose. It facilitates the execution of asynchronous processes to run complex predictions by implementing Rabbit message queues. A visualzer of MRI scans was integrated for visualization and interaction with the data obtained from these images. Validation experiments show that the application is efficient and easy to use, taking into account the size and complexity of the data that needs to be analyzed together for epilepsy patients. We expect that this software makes a significant contribution towards the development of new tools and methods for epilepsy research.

• Erick I. Navarro-Delgado, The University of British Columbia, BC, Canada, Canada
• Darina Czamara, ) Max-Planck-Institute of Psychiatry, Department of Genes and Environment, Munich, 80804, Germany, Germany
• Karlie Edwards, The University of British Columbia, BC, Canada, Canada
• Sarah S. Merrill, The Warren Alpert Medical School of Brown University., Canada
• Chaini Konwar, The University of British Columbia, BC, Canada, Canada
• Stuart E. Turvey, ) Centre for Molecular Medicine and Therapeutics, BC Children’s Hospital Research Institute, Vancouver, BC, Canada, Canada
• Katri Räikkönen, Department of Psychology , Faculty of Medicine, University of Helsinki, Helsinki, 00014, Finland., Finland
• Keegan Korthauer, The University of British Columbia, BC, Canada, Canada
• Michael S. Kobor, The University of British Columbia, BC, Canada, Canada

Background: DNA methylation (DNAme) is an epigenetic mark that has gained attention under the Developmental Origins of Health and Disease framework due to its association with phenotypic changes and potential long-term stability. Genomic (G) and exposomic (E; totality of exposures a person experiences) differences have been reported as main sources of inter-individual DNAme variability. However, further research is needed to understand the genomic location and characteristics of regions with variable DNAme, the degree to which genetics and environmental factors contribute to DNAme variability in these regions, and whether genetics and environment associate individually with DNAme or in combination. Here, we estimate the genome-exposome contribution to cord blood methylome variability in an integrative multi-omics analysis.
Method: We identify genome-wide cord blood Variable Methylated Regions in two independent cohorts (CHILD and PREDO; overall n=1,662) accounting for the sparse and non-homogeneous coverage in the Illumina EPIC v1 array. To reduce the universe of compared models, we implement VMR-wise E and G variable selection procedure based on LASSO. We then fit single-variable G, E, pairwise additive (G+E) and interaction (GxE) linear models of VMR’s DNAme, and select the best one per VMR based on their AIC. To control for spurious associations, we implement a computationally fast permutation approach.
Results: With a high percentage of winner models (59%), a higher proportion of DNAme variance explained (M = 0.22), and a full consistency across cohorts (100%), we identify genetics as a consistent main contributor to DNAme variability in cord blood usually in additive and interaction combinations with the prenatal exposome (44.4%). These results showcase the relevance of genetics in diverging DNAme patterns in early life. Furthermore, we introduce RAMEN (Regional Association of Methylome variability with Exposone and geNome; github.com/ErickNavarroD/RAMEN), a user-friendly R package for DNAme microarrays that improves the extraction of regional DNAme variability patterns and models their contributors while controlling for spurious associations expected by chance through state-of-the-art statistical techniques.

• Alejandro Correa Rojo, Hasselt University and VITO, Belgium
• Pieter Moris, Institute of Tropical Medicine, Belgium
• Hanne Meuwissen, Hasselt University, Belgium
• Pieter Monsieurs, Institute of Tropical Medicine, Belgium
• Dirk Valkenborg, Hasselt University, Belgium

Summary: The Discriminant Analysis of Principal Components method is a pivotal tool in population genetics,
combining principal component analysis and linear discriminant analysis to assess the genetic structure of populations
using genetic markers, focusing on the description of variation between genetic clusters. Despite its utility, the
original R implementation in the adegenet package faces computational challenges with large genomic datasets. To
address these limitations, we introduce DAPCy, a Python package leveraging the scikit-learn library to enhance
the method’s scalability and efficiency. DAPCy supports large datasets by utilizing compressed sparse matrices and
truncated singular value decomposition for dimensionality reduction, coupled with k-fold cross-validation for robust
model evaluation. It also includes modules for de novo genetic clustering and extensive visualization and reporting
capabilities. Compared to the original R implementation, DAPCy can process genomic datasets with thousands of
samples and features in less computational time and with reduced memory usage. To show DAPCy’s computational
capabilities, we benchmarked it with the R implementation using the Plasmodium falciparum dataset from MalariaGEN
and the 1000 Genomes Project. Availability: DAPCy can be installed as a Python package through pip. Source
code is available on https://gitlab.com/uhasselt-bioinfo/dapcy. Documentation and a tutorial can be found on
https://uhasselt-bioinfo.gitlab.io/dapcy/.

• Felipe James de Almeida Vasquez, School of Medicine of Ribeirao Preto - University of São Paulo, Brazil
• Arthur Scorsolini Fares, School of Medicine of Ribeirao Preto - University of São Paulo, Brazil
• Silvana Giuliatti, School of Medicine of Ribeirao Preto - University of São Paulo, Brazil

COVID-19, caused by the SARS-CoV-2 virus, continues to pose a significant threat to global health, with the potential for new outbreaks. The infection is mediated by the Spike protein, which interacts with human cellular receptors such as angiotensin-converting enzyme 2 (ACE2) and requires sequential processing by proteins like Furin and transmembrane serine protease 2 (TMPRSS2), which cleave the S1/S2 and S2’ sites, respectively, facilitating the infection. SARS-CoV-2 variants may impact these interactions and influence infectivity. While the interaction between Spike and ACE2 has been extensively studied, the interaction mechanisms involving Furin and TMPRSS2, especially in variants, are less understood. This study aims to explore these interactions by integrating molecular dynamics (MD) simulations and machine learning (ML) to identify key residues in the infection process. The structures of Spike, ACE2, and Furin were obtained from the Protein Data Bank, while TMPRSS2 was modeled with AlphaFold 2. The Spike proteins of the Alpha, Beta, Gamma, Delta, Omicron BA.1, BA.2, BA.5, XBB.1.5, and BA.2.86 variants were modeled using MODELLER, and glycans were incorporated using the Glycan Reader & Modeler. HADDOCK was used to perform intermolecular interactions. MD simulations were carried out using GROMACS with the CHARMM36m force field in solvated, neutralized, and equilibrated systems, followed by a 200 ns production step. The MDAnalysis package was used to calculate the distances between alpha carbons of the proteins. A Random Forest (RF) classifier was employed to identify and evaluate the significance of subtle interactions, greater than 1.5 nm in distance, between Spike and its receptors. Twenty protein complexes were modeled and selected based on the lowest HADDOCK scores. RMSD analysis of the ACE2-Spike-Furin complexes trajectories showed deviations of 1.5 ± 0.5 nm and 1.9 ± 0.7 nm for the ACE2-Spike-TMPRSS2 complexes. The classifier identified approximately 25,000 subtle interactions between Spike and Furin, 18,000 between Spike and TMPRSS2, and 12,000 between Spike and ACE2. The top 10 most relevant interactions were identified to highlight the residues with the greatest impact on these interactions. It was observed that subtle interactions, not involving active sites, are crucial for maintaining intermolecular interactions, varying according to the residues involved in different variants. These findings provide a more detailed understanding of the interactions of SARS-CoV-2 with its receptors, which is essential for determining the impact of COVID-19 variants and developing new therapeutic strategies.

• Dary Mendoza Meza, Universidad del Atlántico, Colombia
• Mariam Montenegro Fontalvo, Universidad del Atlántico, Colombia
• Marcela Osses Garay, Universidad del Atlántico, Colombia
• Rosaly Ríos Martínez, Universidad del Atlántico, Colombia

Currently, in silico methods are being widely used for the design and production of peptides with various applications, including therapeutics and immunodiagnostics. Among the enzymes of biomedical interest are nematode cystatins, known for their immunoregulatory and anti-inflammatory properties. The main objective of this study was to obtain polyclonal antibodies directed against nematode cystatins by using immunogenic peptides designed by computational methods. To design the peptides, a multiple alignment of 20 cystatin sequences available in biological databases such as Protein-NCBI and UniProt was performed. The resulting consensus sequence was subjected to a functional analysis, where it was found that it belongs to the cystatin domain and has cysteine-type endopeptidase inhibitory activity. This sequence was then analyzed using epitope prediction algorithms available in the Immune Epitope Database (IEDB) and OptimumAntigen™ software. Taking into account an area of conservation between positions 70 and 200, peptides that met this criterion were chosen and subjected to an analysis of their physicochemical and structural properties. The peptide that showed the best results in terms of antigenicity, hydrophobicity, and synthesis feasibility was selected for three-dimensional modeling using PEP-FOLD, which uses an ab initio method to predict the structure of peptides. Subsequently, the three-dimensional model was validated using the Ramachandran graph, where it was observed that almost 100% of the residues were found in the most favored regions, thus ensuring the structural quality of the designed peptide. The synthesis of the peptide was carried out using the f-moc method in solid phase, obtaining a 14-mer peptide (1562.8 Da) and an antigenicity index of 1.12. To improve the immunogenicity of the peptide, it is conjugated with Megathura crenulata hemocyanin (KLH) and used to immunize Wistar rats. The production of specific antibodies was confirmed by the Western Blot technique, where it was observed that the purified antibodies specifically recognized a protein band of approximately 16 kDa in protein extracts of the nematode Ascaris lumbricoides, suggesting that this could correspond to the native cystatin of the parasite. The results obtained highlight the effectiveness of the methodology used to obtain immunogenic peptides derived from nematode cystatins and the subsequent production of polyclonal antibodies with therapeutic and diagnostic applications in the biomedical field. In conclusion, the integration of in silico methods with traditional experimental techniques has proven to be essential to advance the design of peptides with biomedical potential, opening new possibilities for the development of more effective and specific therapies and diagnostics.

• Yalbi Balderas-Martínez
• Andrea Guzmán-Mesa
• Vinicius Maracaja
• Alejandro Reyes