Biology subjects

Arenas, A.

Publications and source records attributed to Arenas, A..

4 recordsLinked to original sources

bioRxiv2026

Intermediate phenotypic heterogeneity creates an optimal coexistence window in bacteria-phage communities

Phenotypic heterogeneity can expand ecological niche space even within a genetically homogeneous population. Recent experiments showed that growth-rate heterogeneity in a clonal bacterial host can stabilize coexistence among multiple phages. Yet this discovery leaves unresolved whether increasing host heterogeneity monotonically promotes coexistence. Here we show that it does not. Using a trait-based bacteria--phage coexistence framework, we find that phage richness is maximized at an intermediate scale of host growth-rate heterogeneity, determined by the match between host phenotypic dispersion and phage niche width. Our results show that optimal phage diversity emerges from an ecological trade-off between two competing effects: increasing phenotypic heterogeneity enables niche differentiation but eventually reduces the host abundance in the preferred niche of each phage. Therefore, our findings predict that experimentally increasing host growth-rate variance at a fixed mean growth rate should produce a nonmonotonic phage richness response, providing a quantitative principle for how intraspecific phenotypic variation shapes biodiversity in microbial ecosystems.

ecology↗

bioRxiv2025

Identification of a novel papillary renal cell carcinoma molecular subtype characterized by HIF-pathway over-activation

PurposePapillary renal cell carcinoma (pRCC), the second most common subtype of renal cancer, exhibits heterogeneity in molecular features and response to targeted therapies, including antiangiogenic drugs. Discovering molecular biomarkers able to stratify pRCC patients into clinically relevant subgroups and understanding the underlying mechanisms are urgently needed to advance precision medicine. Here, we molecularly dissect a large pRCC series through the expression of the targets of hypoxia inducible factors (HIF), master regulators of angiogenesis, metabolic reprogramming and immune microenvironment. Experimental DesignWe merged and analyzed multi-omic and clinical data of 346 patients derived from two pRCC series (TCGA-KIRP and a Spanish metastatic series). Altered pathways and differences in tumor microenvironment were identified through tumor transcriptomic analyses. Tumor metabolome analysis was performed in selected cases. ResultsMolecular revision of driver mutations classified 302 patients as pRCC, while uncovering misclassified cases. Analysis of HIF targets gene expression identified a subset of pRCC tumors with increased HIF activity (31%; "HIF-active"). These tumors were characterized by high hypoxia scores, increased angiogenesis, low expression of Krebs cycle genes and mitochondrial activity, high immune infiltration and increased epithelial-mesenchymal transition (P<0.0001; each feature) and they were associated with increased metastasis risk and worse overall survival (P<0.005). Metabolome analyses revealed that HIF-active tumors accumulated L-2-hydroxyglutarate (L-2-HG), an oncometabolite that leads to pseudohypoxia by inhibiting HIF prolyl hydroxylases and preventing HIF degradation. L-2-HG-accumulation agreed with diminished L2HGDH expression, associated with losing one copy of the gene and low expression of PPARGC1A, which regulates its transcription. ConclusionsHIF-pathway overactivation defines a pseudohypoxic pRCC molecular subgroup characterized by high angiogenesis, immune infiltration and aggressive features. These findings advance our understanding of pRCC diversity, revealing potential clues for the variable response of patients to targeted therapies and paving the way to more personalized pRCC treatments.

cancer biology↗

bioRxiv2023

Foraging task specialization in honey bees (Apis mellifera): the contribution of floral rewards on the learning performance of pollen and nectar foragers

Social insects live in communities where cooperative actions heavily rely on the individual cognitive abilities of their members. In the honey bee (Apis mellifera), the specialization in nectar or pollen collection is associated with variations in gustatory sensitivity, affecting both associative and non-associative learning. Gustatory sensitivity fluctuates as a function of changes in motivation for the specific floral resource throughout the foraging cycle, yet differences in learning abilities between nectar and pollen foragers at the onset of recollection remains unexplored. Here, we examined nectar and pollen foragers captured upon arrival at food sources. We subjected them to an olfactory PER conditioning using a 10% sucrose solution paired (S10%+P) or unpaired (S10%) with pollen as a co-reinforcement. For non-associative learning, we habituated foragers with a 10% sucrose solution paired (S10%+P) or unpaired (S10%) with pollen, followed by dishabituation tests with either S50% or S10%+P. Our results indicate that pollen foragers show lower performance than nectar foragers when conditioned with S10%. Interestingly, performance improves to levels similar to those of nectar foragers when pollen is included as rewarding stimulus (S10%+P). In non-associative learning, pollen foragers tested with S10%+P displayed a lower degree of habituation than nectar foragers and a higher degree of dishabituation when pollen was used as the dishabituating stimulus (S10%+P). Altogether, our results support the idea that pollen and nectar honey bee foragers differ in their perception of rewards, leading to inter-individual differences in learning that contribute to foraging specialization.

animal behavior and cognition↗

bioRxiv2017

The multiplex network of human diseases

Untangling the complex interplay between phenotype and genotype is crucial to the effective characterization and subtyping of diseases. Here we build and analyze the multiplex network of 779 human diseases, which consists of a genotype-based layer and a phenotype-based layer. We show that diseases with common genetic constituents tend to share symptoms, and uncover how phenotype information helps boost genotype information. Moreover, we offer a flexible classification of diseases that considers their molecular underpinnings alongside their clinical manifestations. We detect cohesive groups of diseases that have high intra-group similarity at both the molecular and the phenotypic level. Inspecting these disease classes, we demonstrate the underlying pathways that connect diseases mechanistically. We observe monogenic disorders grouped together with complex diseases for which they increase the risk factor. We propose potentially new disease associations that arise as a unique feature of the information flow within and across the two layers.

bioinformatics↗