Biology subjects

Sepulveda, J.

Publications and source records attributed to Sepulveda, J..

6 recordsLinked to original sources

bioRxiv2026

Genetic drivers shape TREM2-dependent myeloid responses and glioblastoma progression

Glioblastoma (GBM) is characterized by a heterogeneous, myeloid-enriched, and highly immunosuppressive microenvironment. TREM2 regulates microglial and macrophage functions, but its role in GBM remains unclear. We investigated whether genetic drivers determine the effects of TREM2 using de novo and orthotopic transplantable murine models of Nf1-silenced/Ras-driven and PDGFB-driven GBM. To this end, we combined immune profiling, functional assays, secretome analysis, and single-cell RNA sequencing with bone marrow chimeras and conditional Trem2 deletion strategies. Trem2 deficiency prolonged survival and reduced tumor burden in Nf1-silenced/Ras-driven GBM, whereas it increased tumor burden without affecting survival in PDGFB-driven GBM. These different outcomes were accompanied by distinct changes in myeloid composition and soluble mediators. TREM2 was required for bone marrow-derived macrophage migration and microglial process extension towards Ras-derived, but not PDGFB-derived, tumor-conditioned medium. In contrast, both models showed impaired microglial efferocytosis. Single-cell RNA sequencing analysis of Nf1-silenced GBM showed that Trem2 loss shifted myeloid cells from disease-associated, pro-inflammatory states to a more homeostatic state. Bone marrow chimeras supported a tumor-promoting role for TREM2 in microglia, rather than infiltrating myeloid cells in Ras-driven GBM. In addition, conditional deletion of Trem2 before tumor implantation prolonged survival, whereas deletion after tumor establishment did not. These findings identify genetic context and timing of TREM2 activity as determinants of its effects on GBM progression, highlighting driver-dependent myeloid responses as considerations for developing TREM2-directed therapies in glioblastoma.

neuroscience↗

bioRxiv2026

BiomarkerKB: FAIR and Integrated Biomarker Knowledge Connecting Biomolecular and Clinical Data Types

Biomarkers are essential tools for disease detection, risk assessment, therapeutic monitoring, and precision medicine. However, biomarker data are dispersed across heterogeneous resources, inconsistently reported in the literature, and rarely standardized for computational use. This fragmentation limits reproducibility, cross-study integration, and the discovery of novel biomarker and disease relationships. We developed BiomarkerKB, a knowledgebase designed to harmonize and integrate biomarker information under a standardized data model. The model follows the FDA-NIH BEST biomarker definition and captures both core fields (biomarker entity, disease/condition, exposure agent) and contextual metadata (specimen, biomarker role, evidence, provenance). Biomarker data and related annotations were either curated from publications or collected from public resources (e.g., OpenTargets, GWAS Catalog, ClinVar, CIViC, OncoMX) and were also contributed by the Common Fund Data Coordinating Centers and the Early Detection Research Network (EDRN). Standardization was achieved using ontologies and reference resources such as Disease Ontology, UBERON, UniProtKB, and HUGO Gene Nomenclature Committee (HGNC) gene symbols. BiomarkerKB data were ingested into a Neo4j-based knowledge graph and integrated with the Common Fund Data Ecosystem (CFDE) Knowledge Graph. The initial release of BiomarkerKB contains over 200,000 biomarker-disease associations spanning genes, proteins, metabolites, glycans, and chemical elements. The knowledge graph comprises more than 300,000 nodes and 1.2 million edges, enabling structured exploration of biomarker relationships within CFDE data as demonstrated through the knowledge graph query-based use cases presented in this study. A publicly accessible web portal (https://biomarkerkb.org) provides keyword search, filtering, data downloads, and access to graph visualization to support both researchers and computational analyses. BiomarkerKB addresses a critical gap in biomarker informatics by providing an integrated, FAIR (Findable, Accessible, Interoperable, and Reusable), and unified framework for biomarker knowledge exploration and discovery.

bioinformatics↗

bioRxiv2024

Peptipedia v2.0: A peptide sequence database and user-friendly web platform. A major update

In recent years, peptides have gained significant relevance due to their therapeutic properties. The surge in peptide production and synthesis has generated vast amounts of data, enabling the creation of comprehensive databases and information repositories. Advances in sequencing techniques and artificial intelligence have further accelerated the design of tailor-made peptides. However, leveraging these techniques requires versatile and continuously updated storage systems, along with tools that facilitate peptide research and the implementation of machine learning for predictive systems. This work introduces Peptipedia v2.0, one of the most comprehensive public repositories of peptides, supporting biotechnological research by simplifying peptide study and annotation. Peptipedia v2.0 has expanded its collection by over 45% with peptide sequences that have reported biological activities. The functional biological activity tree has been revised and enhanced, incorporating new categories such as cosmetic and dermatological activities, molecular binding, and anti-ageing properties. Utilizing protein language models and machine learning, more than 90 binary classification models have been trained, validated, and incorporated into Peptipedia v2.0. These models exhibit average sensitivities and specificities of 0.877 {+/-} 0.0530 and 0.873 {+/-}0.054, respectively, facilitating the annotation of more than 3.6 million peptide sequences with unknown biological activities, also registered in Peptipedia v2.0. Additionally, Peptipedia v2.0 introduces description tools based on structural and ontological properties and user-friendly machinelearning tools to facilitate the application of machine-learning strategies to study peptide sequences. Peptipedia v2.0 is accessible under the Creative Commons CC BY-NC-ND 4.0 license at https://peptipedia.cl/.

bioinformatics↗

bioRxiv2023

Delayed postglacial colonization of Betula in Iceland and the circum North Atlantic

As the Arctic continues to warm, woody shrubs are expected to expand northward. This process, known as "shrubification", has important implications for regional biodiversity, food web structure, and high-latitude temperature amplification. While the future rate of shrubification remains poorly constrained, past records of plant immigration to newly deglaciated landscapes in the Arctic may serve as useful analogues. We provide one new postglacial Holocene sedimentary ancient DNA (sedaDNA) record of vascular plants from Iceland and place a second Iceland postglacial sedaDNA record on an improved geochronology; both show Salicaceae present shortly after deglaciation whereas Betulaceae first appears more than 1000 years later. We find a similar pattern of delayed Betulaceae colonization in eight previously published postglacial sedaDNA records from across the glaciated circum North Atlantic. In nearly all cases, we find that Salicaceae colonizes earlier than Betulaceae and that Betulaceae colonization is increasingly delayed for locations farther from glacial-age woody plant refugia. These trends in Salicaceae and Betulaceae colonization are consistent with the plant families environmental tolerances, species diversity, reproductive strategies, seed sizes, and soil preferences. As these reconstructions capture the efficiency of postglacial vascular plant migration during a past period of high-latitude warming, a similarly slow response of some woody shrubs to current warming in glaciated regions, and possibly non-glaciated tundra, may delay Arctic shrubification and future changes in the structure of tundra ecosystems and temperature amplification.

ecology↗

bioRxiv2021

Divergent Acyl Carrier Protein Mediates Mitochondrial Fe-S Cluster Biosynthesis in Malaria Parasites

Plasmodium falciparum malaria parasites are early-diverging eukaryotes with many unusual metabolic adaptations. Understanding these adaptations will give insight into parasite evolution and unveil new parasite-specific drug targets. Most eukaryotic cells retain a mitochondrial fatty acid synthesis (FASII) pathway whose acyl carrier protein (mACP) and 4-phosphopantetheine (Ppant) prosthetic group provide a soluble scaffold for acyl chain synthesis. In yeast and humans, mACP also functions to biochemically couple FASII activity to electron transport chain (ETC) assembly and Fe-S cluster biogenesis. In contrast to most eukaryotes, the Plasmodium mitochondrion lacks FASII enzymes yet curiously retains a divergent mACP lacking a Ppant group. We report that ligand-dependent knockdown of mACP is lethal to parasites, indicating an essential FASII-independent function. Decyl-ubiquinone rescues parasites temporarily from death, suggesting a dominant dysfunction of the mitochondrial ETC followed by broader cellular defects. Biochemical studies reveal that Plasmodium mACP binds and stabilizes the Isd11-Nfs1 complex required for Fe-S cluster biosynthesis, despite lacking the Ppant group required for this association in other eukaryotes, and knockdown of parasite mACP causes loss of both Nfs1 and the Rieske Fe-S protein in ETC Complex III. This work reveals that Plasmodium parasites have evolved to decouple mitochondrial Fe-S cluster biogenesis from FASII activity, and this adaptation is a shared metabolic feature of other Apicomplexan pathogens, including Toxoplasma and Babesia. This discovery also highlights the ancient, fundamental role of ACP in mitochondrial Fe-S cluster biogenesis and unveils an evolutionary driving force to retain this interaction with ACP independent of its eponymous function in FASII. Significance StatementPlasmodium malaria parasites are single-celled eukaryotes that evolved unusual metabolic adaptations. Parasites require a mitochondrion for blood-stage viability, but essential functions beyond the electron transport chain are sparsely understood. Unlike yeast and human cells, the Plasmodium mitochondrion lacks fatty acid synthesis enzymes but retains a divergent acyl carrier protein (mACP) incapable of tethering acyl groups. Nevertheless, mACP is essential for parasite viability by binding and stabilizing the core mitochondrial Fe-S cluster biogenesis complex via a divergent molecular interface lacking an acyl-pantetheine group that contrasts with other eukaryotes. This discovery unveils an essential metabolic adaptation in Plasmodium and other human parasites that decouples mitochondrial Fe-S cluster biogenesis from fatty acid synthesis and evolved at or near the emergence of Apicomplexan parasitism.

microbiology↗

bioRxiv2019

Computational design of improved standardized chemotherapy protocols for grade II oligodendrogliomas

The use of mathematical models for personalization of cancer therapies and raising hypothesis of potential clinical impact is an emerging topic in the interface between mathematics and oncology. Here we put forward a mathematical model describing the response of low-grade (WHO grade II) oligodendrogliomas (LGO) to temozolomide (TMZ). The model described the longitudinal volumetric dynamics of tumor response to TMZ of a cohort of 11 LGO patients treated with TMZ. After finding patient-specific parameters, different therapeutical strategies were tried computationally on the in-silico twins of those patients. Chemotherapy schedules with larger-than-standard rest periods between consecutive cycles had either the same or better long-term efficacy than the standard 28-day cycles. The results were confirmed in a large virtual clinical trial including 2000 patients. These long-cycle schemes would also have reduced toxicity and defer the appearance of resistances.\n\nOn the basis of those results, a combination scheme consisting of five induction TMZ cycles given monthly plus 12 maintenance cycles given every three months was found to provide substantial survival benefits for the in-silico twins of the 11 LGO patients (median 5.69 years, range: 0.67 to 68.45 years) and in a large virtual trial including 2000 patients. This scheme could be useful for defining a standardized TMZ treatment for LGO patients with survival benefits.\n\nAuthor summaryA mathematical model described the longitudinal volumetric growth data of grade II oligodendrogliomas patients and their response to temozolomide. The model was used to explore alternative therapeutical protocols for the in-silico twins of the patients and in virtual clinical trials. The simulations show that enlarging the time interval between chemotherapy cycles would maintain the therapeutical efficacy, while limiting toxicity and defering the development of resistances. This may allow for improved drug-exposure by administering a larger number of cycles for longer treatment periods. A scheme based on this idea consisting of an induction phase (5 consecutive cycles, 1 per month) and a maintenance phase (12 cycles given in three-months intervals) led to substantial survival benefits in-silico.

cancer biology↗