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The conditions used to describe the presence of an immune disease are often represented by interaction graphs. These informative, but intricate structures are susceptible to perturbations at different levels. The mode in which that perturbation occurs is still of utmost importance in areas such as cell reprogramming and therapeutics models. In this sense, module identification can be useful to well characterise the global graph architecture. To help us with this identification, we perform topological overlap-related measures. Thanks to these measures, the location of highly disease-specific module regulators is possible. Such regulators can perturb other nodes, potentially causing the entire system to change behaviour or collapse. We provide a geometric framework explaining such situations in the context of inflammatory bowel diseases (IBD). IBD are severe chronic disorders of the gastrointestinal tract whose incidence is dramatically increasing worldwide. Our approach models different IBD status as Riemannian manifolds defined by the graph Laplacian of two high throughput proteome screenings. It also identifies module regulators as singularities within the manifolds (the so-called singular manifolds). Furthermore, it reinterprets the characteristic nonlinear dynamics of IBD as compensatory responses to perturbations on those singularities. Then, particular reconfigurations of the immune system could make the disease status move towards an innocuous target state.

We evaluated the contribution of artificial intelligence in predicting the risk of acute cellular rejection (ACR) using early plasma levels of soluble CD31 (sCD31) in combination with recipient haematosis, which was measured by the ratio of arterial oxygen partial pressure to fractional oxygen inspired (PaO 2 /FiO 2 ) and respiratory SOFA (Sequential Organ Failure Assessment) within 3 days of lung transplantation (LTx). CD31 is expressed on endothelial cells, leukocytes and platelets and acts as a “peace-maker” at the blood/vessel interface. Upon nonspecific activation, CD31 can be cleaved, released, and detected in the plasma (sCD31). The study included 40 lung transplant recipients, seven (17.5%) of whom experienced ACR. We modelled the plasma levels of sCD31 as a nonlinear dependent variable of the PaO 2 /FiO 2 and respiratory SOFA over time using multivariate and multimodal models. A deep convolutional network classified the time series models of each individual associated with the risk of ACR to each individual in the cohort.

Fibrosis may be introduced as a severe complication of inflammatory bowel disease (IBD). This is a particular disorder causing luminal narrowing and stricture formation in the inflamed bowel wall of a patient denoting, possibly, need for surgery. Thus, the development of treatments reducing fibrosis is an urgent issue to be addressed in IBD. In this context, we require the finding and development of biomarkers of intestinal fibrosis. Potential candidates such as microRNAs, gene variants or fibrocytes have shown controversial results on heterogeneous sets of IBD patients. Magnetic resonance imaging (MRI) has been already successfully proven in the recognition of fibrosis. Nevertheless, while there are no numerical models capable of systematically reproducing experiments, the usage of MRI could not be considered a standard in the inflammatory domain. Hence, there is an importance of deploying new sequence combinations in MRI methods that enable learning reproducible models. In this work, we provide reproducible deep learning models of intestinal fibrosis severity scores based on MRI novel radiation-induced rat model of colitis that incorporates some unexplored sequences such as the flow-sensitive alternating inversion recovery or diffusion imaging. The results obtained return an 87.5 % of success in the prediction of MRI scores with an associated mean-square error of 0.12. This approach offers practitioners a valuable tool to evaluate antifibrotic treatments under development and to extrapolate such noninvasive MRI scores model to patients with the aim of identifying early stages of fibrosis improving patients’ management.

In complex diseases, the interactions among genes are commonly elucidated through the lens of graphs. Amongst these genes, certain ones form bi-functional modules within the graph, contingent upon their (anti)correlation with a specific functional state, such as susceptibility to a genetic disorder of non-Mendelian traits. Consequently, a disease can be delineated by a finite number of these discernible modules. Within each module, there exist allelic variants that pose a genetic risk, thus qualifying as genetic risk factors. These factors precipitate a permissive state, which if all other modules also align in the same permissive state, can ultimately lead to the onset of the disease in an individual. To gain a deeper insight into the incidence of a disease, it becomes imperative to acquire a comprehensive understanding of the genetic transmission of these factors. In this work, we present a non-linear model for this transmission, drawing inspiration from the classic theory of the Bell experiment. This model aids in elucidating the variances observed in SNP interactions concerning the risk of disease.

Small signalling peptides (SSPs) serve as crucial mediators of cell-to-cell communication in plants, orchestrating diverse physiological processes from development to stress responses. While recent advances in sequencing technologies have improved genome annotation, the identification of novel SSPs remains challenging due to their small size, sequence diversity, and often transient expression patterns. To address this bottleneck, we developed S2-PepAnalyst, a machine learning-powered web tool that integrates plant-specific datasets with advanced computational approaches for SSP prediction and classification. Our platform combines protein language models with geometric-topological feature analysis to capture both sequence and structural characteristics of known SSP families. When validated against experimentally confirmed peptides, S2-PepAnalyst achieved high predictive accuracy (99.5%) while maintaining low false-negative rates. The tool successfully classified peptides into functionally distinct families (e.g., CLE, RALF) and identified non-canonical SSPs that lack traditional signal peptides. Importantly, S2-PepAnalyst demonstrated robust performance across both model plants and agriculturally important species. As a freely available resource (https://www.s2-pepanalyst.uma.es), this tool will empower plant biologists to systematically explore the largely untapped repertoire of plant SSPs, facilitating discoveries in plant cell signalling and potential applications in crop improvement.Small signalling peptides (SSPs) serve as crucial mediators of cell-to-cell communication in plants, orchestrating diverse physiological processes from development to stress responses. While recent advances in sequencing technologies have improved genome annotation, the identification of novel SSPs remains challenging due to their small size, sequence diversity, and often transient expression patterns. To address this bottleneck, we developed S2-PepAnalyst, a machine learning-powered web tool that integrates plant-specific datasets with advanced computational approaches for SSP prediction and classification. Our platform combines protein language models with geometric-topological feature analysis to capture both sequence and structural characteristics of known SSP families. When validated against experimentally confirmed peptides, S2-PepAnalyst achieved high predictive accuracy (99.5%) while maintaining low false-negative rates. The tool successfully classified peptides into functionally distinct families (e.g., CLE, RALF) and identified non-canonical SSPs that lack traditional signal peptides. Importantly, S2-PepAnalyst demonstrated robust performance across both model plants and agriculturally important species. As a freely available resource (https://www.s2-pepanalyst.uma.es), this tool will empower plant biologists to systematically explore the largely untapped repertoire of plant SSPs, facilitating discoveries in plant cell signalling and potential applications in crop improvement.

Across species, animals have diverse sex determination pathways, each consisting of a hierarchical cascade of genes and its associated regulatory mechanism. Houseflies have a distinctive polymorphic sex determination system in which a dominant male determiner, the M-factor, can reside on any of the chromosomes. We identified a gene, Musca domestica male determiner (Mdmd), as the M-factor. Mdmd originated from a duplication of the spliceosomal factor gene CWC22 (nucampholin). Targeted Mdmd disruption results in complete sex reversal to fertile females because of a shift from male to female expression of the downstream genes transformer and doublesex. The presence of Mdmd on different chromosomes indicates that Mdmd translocated to different genomic sites. Thus, an instructive signal in sex determination can arise by duplication and neofunctionalization of an essential splicing regulator.

The mitotic spindle is an essential molecular machine involved in cell division, whose composition has been studied extensively by detailed cellular biology, high-throughput proteomics, and RNA interference experiments. However, because of its dynamic organization and complex regulation it is difficult to obtain a complete description of its molecular composition. We have implemented an integrated computational approach to characterize novel human spindle components and have analysed in detail the individual candidates predicted to be spindle proteins, as well as the network of predicted relations connecting known and putative spindle proteins. The subsequent experimental validation of a number of predicted novel proteins confirmed not only their association with the spindle apparatus but also their role in mitosis. We found that 75% of our tested proteins are localizing to the spindle apparatus compared to a success rate of 35% when expert knowledge alone was used. We compare our results to the previously published MitoCheck study and see that our approach does validate some findings by this consortium. Further, we predict so-called \"hidden spindle hub\", proteins whose network of interactions is still poorly characterised by experimental means and which are thought to influence the functionality of the mitotic spindle on a large scale. Our analyses suggest that we are still far from knowing the complete repertoire of functionally important components of the human spindle network. Combining integrated bio-computational approaches and single gene experimental follow-ups could be key to exploring the still hidden regions of the human spindle system.

Accurate modelling of biological systems requires a deeper and more complete knowledge about the molecular components and their functional associations than we currently have. Traditionally, new knowledge on protein associations generated by experiments has played a central role in systems modelling, in contrast to generally less trusted bio-computational predictions. However, we will not achieve realistic modelling of complex molecular systems if the current experimental designs lead to biased screenings of real protein networks and leave large, functionally important areas poorly characterised. To assess the likelihood of this, we have built comprehensive network models of the yeast and human proteomes by using a meta-statistical integration of diverse computationally predicted protein association datasets. We have compared these predicted networks against combined experimental datasets from seven biological resources at different level of statistical significance. These eukaryotic predicted networks resemble all the topological and noise features of the experimentally inferred networks in both species, and we also show that this observation is not due to random behaviour. In addition, the topology of the predicted networks contains information on true protein associations, beyond the constitutive first order binary predictions. We also observe that most of the reliable predicted protein associations are experimentally uncharacterised in our models, constituting the hidden or \"dark matter\" of networks by analogy to astronomical systems. Some of this dark matter shows enrichment of particular functions and contains key functional elements of protein networks, such as hubs associated with important functional areas like the regulation of Ras protein signal transduction in human cells. Thus, characterising this large and functionally important dark matter, elusive to established experimental designs, may be crucial for modelling biological systems. In any case, these predictions provide a valuable guide to these experimentally elusive regions.

Efforts to understand the molecular mechanisms of COVID-19 have led to the identification of ACE2 as the main receptor for the SARS-CoV-2 spike protein on cell surfaces. However, there are still important questions about the role of other proteins in disease progression. To address these questions, we modelled the plasma proteome of 384 COVID-19 patients using protein level measurements taken at three different times and incorporating comprehensive clinical evaluation data collected 28 d after hospitalisation. Our analysis can accurately assess the severity of the illness using a metric based on WHO scores. By using topological vectorisation, we identified proteins that vary most in expression based on disease severity, and then utilised these findings to construct a graph convolutional network. This dynamic model allows us to learn the molecular interactions between these proteins, providing a tool to determine the severity of a COVID-19 infection at an early stage and identify potential pharmacological treatments by studying the dynamic interactions between the most relevant proteins.

Small signalling peptides (SSPs) serve as crucial mediators of cell-to-cell communication in plants, orchestrating diverse physiological processes from development to stress responses. While recent advances in sequencing technologies have improved genome annotation, the identification of novel SSPs remains challenging due to their small size, sequence diversity, and often transient expression patterns. To address this bottleneck, we developed S -PepAnalyst, a machine learning-powered web tool that integrates plant-specific datasets with advanced computational approaches for SSP prediction and classification. Our platform combines protein language models with geometric-topological feature analysis to capture both sequence and structural characteristics of known SSP families. When validated against experimentally confirmed peptides, S -PepAnalyst achieved high predictive accuracy (99.5%) while maintaining low false-negative rates. The tool successfully classified peptides into functionally distinct families (e.g., CLE, RALF) and identified non-canonical SSPs that lack traditional signal peptides. Importantly, S -PepAnalyst demonstrated robust performance across both model plants and agriculturally important species. As a freely available resource (https://www.s2-pepanalyst.uma.es), this tool will empower plant biologists to systematically explore the largely untapped repertoire of plant SSPs, facilitating discoveries in plant cell signalling and potential applications in crop improvement.