Jordi Villà-Freixa

Glutathione peroxidase 6 (GPX6) offers a useful system to examine how sequence context alters catalytic chemistry. Primate GPX6 retains catalytic selenocysteine (Sec), whereas rodents have independently replaced it with cysteine (Cys), but the energetic effect of this exchange in each protein remains unclear. Here, empirical valence bond (EVB) simulations were used to quantify the proton-transfer step that generates the reactive selenolate or thiolate in human and mouse GPX6. Introducing selenocysteine into mouse GPX6 lowers the activation barrier by 2.84 kcal/mol, whereas replacing selenocysteine with cysteine in human GPX6 raises it by 1.43 kcal/mol. Thus, the catalytic benefit of selenium depends on the surrounding protein. EVB calculations across 20 nearby substitutions identify several feasible paths between the two proteins and show repeatedly that the effect of a substitution depends on the sequence in which it occurs. In the context of the GPX6 phylogeny, these results support the view that rodent Cys-GPX6 evolved compensatory changes rather than simply losing selenium chemistry.

Parameter estimation in nonlinear biological dynamical systems is a difficult inverse problem because the governing equations are often stiff or oscillatory, the data are sparse and noisy, and the objective landscape is non-convex. Physics-informed neural networks (PINNs) offer an alternative to purely simulation-based calibration by representing state trajectories with neural networks while penalizing violations of the governingequations.ThispaperstudiestheempiricalreliabilityofPINNs for recovering the parameters of the repressilator, a synthetic genetic oscillator formed by three cyclically repressive genes. We use synthetic time-series generated from the standard ordinary differential equation model and train inverse PINNs to estimate the production parameter β and the Hill coefficient n. The study varies observation noise, partial observation of repressors, sampling density, sensitivity to initial parameter guesses, and the difference between stable and oscillatory regimes. The results show that PINNs can reconstruct trajectories accurately when the model structure is correct and the three repressors are observed, but parameter recovery is more fragile than trajectory fitting. Noise, sparse sampling, unobserved variables, and unfavorable initial guesses increase the risk of biased estimates. The stable regime is easier to reconstruct, whereas the oscillatory regime provides richer information but also ex- poses optimization sensitivity. These findings support PINNs as a useful reverse-engineering tool for small gene-regulatory ODE models, while highlighting the need for repeated runs, uncertainty reporting, and experimental designs that improve identifiability.

Reconstructing dynamic immune cell state transitions from single-cell transcriptomic data requires coordinated analytical strategies that capture both phenotypic progression and underlying regulatory programs. This protocol describes a step-by-step computational workflow for analyzing human tumor-infiltrating T cells using the sequential application of dimensionality reduction, pseudotime trajectory inference, regulon activity analysis, and transcription factor–transcription factor network reconstruction. The workflow outlines data preprocessing and quality control, trajectory rooting and parameter selection, branch-specific differential analysis, and the integration of regulon inference to contextualize transcriptional programs along inferred trajectories. Regulon-based TF–TF network reconstruction is used as a downstream interpretive layer to identify regulatory modules associated with distinct cell-state transitions. Publicly available at GitHub repository https://github.com/rogercasalsfr/immuno-trajectory-grn-integrative-workflow , this protocol emphasizes practical considerations including parameter sensitivity, trajectory robustness, and consistency between phenotypic and regulatory outputs. The protocol supports reproducible analysis and interpretation of immune cell dynamics in human tumor microenvironment studies using single-cell RNA sequencing data.

Glutathione peroxidase 6 (GPX6) provides a natural system for asking how catalytic chemistry is reshaped by evolutionary sequence background. Primate GPX6 retains catalytic selenocysteine, whereas rodents have independently replaced it with cysteine, but the energetic consequences of this Sec/Cys exchange within each homologous scaffold remain unclear. Here, empirical valence bond simulations were used to quantify the proton-transfer step that generates the reactive selenolate or thiolate in human and mouse GPX6. Introducing selenocysteine into mouse GPX6 lowers the activation barrier by 2.84 kcal/mol, whereas replacing selenocysteine with cysteine in human GPX6 raises it by 1.43 kcal/mol, showing that the selenium advantage is amplified or damped by the surrounding protein scaffold. Greedy pathway free energy calculations with the empirical valence bond method across 20 proximal substitutions reveal multiple feasible routes between homologs and a recurring energetic pattern in which the effect of each substitution depends on the catalytic background in which it is introduced. Projection onto the GPX6 phylogeny indicates that rodent Cys-GPX6 is best understood as a compensatory catalytic regime rather than a simple loss of selenium chemistry.

Understanding the tumor microenvironment is crucial for optimizing anti-cancer immune responses. At single-cell resolution, trajectory inference methods can reconstruct the dynamic transitions between cell states during differentiation. Immune checkpoint blockade (ICB) therapies, such as PD-1/PD-L1 inhibitors, are used across multiple cancers, including non-melanoma skin cancers (NMSCs), yet the transcriptional mechanisms that shape T cell responses in this context remain unclear. Here, we analyzed a publicly available squamous cell carcinoma (SCC) single-cell RNA-seq dataset comprising 25,581 tumor-infiltrating T-cell profiles to map differentiation trajectories before and after anti-PD-1 therapy. In CD8 ⁺ T cells, therapy enhanced the transition from memory to activated states, prominently involving IL-12–associated pathways, and revealed a distinct memory-to-exhaustion trajectory driven by EOMES and TCF7 regulatory activity. Gene regulatory network inference further revealed therapy-induced transcriptional rewiring distinguishing precursor exhausted (Tpex) from terminally exhausted (Tex) states. CD4 ⁺ T cell populations also underwent substantial reshaping, with trajectory and functional analyses highlighting therapy-driven programs that enhanced CXCL13 ⁺ Tfh responses while generating fewer but more transcriptionally active Tregs. Together, these findings reveal a dual remodeling of helper and cytotoxic T cell compartments upon PD-1 blockade, define key transcriptional regulators controlling cell-state transitions, and identify potential molecular targets and biomarkers to predict and enhance treatment response.

Ponds are key freshwater habitats supporting biodiversity and ecosystem services, yet they remain understudied in the context of land use and climate change. We examined 240 ponds across eight countries (seven in Europe and Uruguay) to assess how internal pond characteristics, surrounding land cover and livestock intensity, seasonal climatic variation, and climate influence nutrient concentrations across spatial and temporal scales. Nutrient concentrations were strongly associated with internal features: shallow ponds and short hydroperiods had higher total nitrogen (TN) and total phosphorus (TP) concentrations, while thermal stratification, typically found in deeper ponds, was associated with higher TN, indicating enhanced internal nutrient recycling. Land use also played a significant role with agricultural intensity increasing nutrient concentration (both TN and TP), whereas forest cover reduced TP. Seasonal variation modulated these patterns, with higher TP concentrations observed in summer, and with dilution effects during wetter and cooler periods, particularly for TN in semi-permanent ponds. These findings underscore the combined influence of physical characteristics, landscape context, and climate variability on nutrient concentrations in ponds and highlight the need for integrated, multi-scale approaches to anticipate the impacts of global climate change effects on these ecologically valuable ecosystems.

Selenocysteine, the 21st amino acid specified by the genetic code, is a rare selenium-containing residue found in the catalytic site of selenoprotein oxidoreductases. Selenocysteine is analogous to the common cysteine amino acid, but its selenium atom offers physical–chemical properties not provided by the corresponding sulfur atom in cysteine. Catalytic sites with selenocysteine in selenoproteins of vertebrates are under strong purifying selection, but one enzyme, glutathione peroxidase 6 (GPX6), independently exchanged selenocysteine for cysteine <100 million years ago in several mammalian lineages. We reconstructed and assayed these ancient enzymes before and after selenocysteine was lost and up to today and found them to have lost their classic ability to reduce hydroperoxides using glutathione. This loss of function, however, was accompanied by additional amino acid changes in the catalytic domain, with protein sites concertedly changing under positive selection across distant lineages abandoning selenocysteine in glutathione peroxidase 6. This demonstrates a narrow evolutionary range in maintaining fitness when sulfur in cysteine impairs the catalytic activity of this protein, with pleiotropy and epistasis likely driving the observed convergent evolution. We propose that the mutations shared across distinct lineages may trigger enzymatic properties beyond those in classic glutathione peroxidases, rather than simply recovering catalytic rate. These findings are an unusual example of adaptive convergence across mammalian selenoproteins, with the evolutionary signatures possibly representing the evolution of novel oxidoreductase functions.

The microtubule-associated protein tau (MAPT) has a critical role in the development and preservation of the nervous system. However, tau's dysfunction and accumulation in the human brain can lead to several neurodegenerative diseases, such as Alzheimer's disease, Down's syndrome, and frontotemporal dementia. The microtubule binding (MTB) domain plays a significant, important role in determining the tau's pathophysiology, as the core of paired helical filaments PHF6* (²⁷⁵VQIINK²⁸⁰) and PHF6 (³⁰⁶VQIVYK³¹¹) of R2 and R3 repeat units, respectively, are formed in this region, which promotes tau aggregation. Post-translational modifications, and in particular lysine acetylation at K280 of PHF6* and K311 of PHF6, have been previously established to promote tau misfolding and aggregation. However, the exact aggregation mechanism is not known. In this study, we established an atomic-level nucleation-extension mechanism of the separated aggregation of acetylated PHF6* and PHF6 hexapeptides, respectively, of tau. We show that the acetylation of the lysine residues promotes the formation of β-sheet enriched high-ordered oligomers. The Markov state model analysis of ac-PHF6* and ac-PHF6 aggregation revealed the formation of an antiparallel dimer nucleus which could be extended from both sides in a parallel manner to form mixed-oriented and high-ordered oligomers. Our study describes the detailed mechanism for acetylation-driven tau aggregation, which provides valuable insights into the effect of post-translation modification in altering the pathophysiology of tau hexapeptides.