Biology subjects

Arias, P.

Publications and source records attributed to Arias, P..

4 recordsLinked to original sources

bioRxiv2026

Macrophages discriminate and sort pathogenic bacteria and apoptotic material during dual-target phagocytosis

Macrophages are central to immune homeostasis, clearing both pathogens and apoptotic cells. While these processes share some features, they differ functionally: efferocytosis of apoptotic cells is typically anti-inflammatory, whereas bacterial uptake triggers strong inflammatory responses. During infections, both targets may coexist, yet how macrophages handle such complex particles is poorly understood. Previously, we showed that Pseudomonas aeruginosa adheres to apoptotic cells, forming stable composites that can be jointly phagocytosed. Here, using quantitative confocal and live-cell imaging, we investigate how macrophages engage and internalize these dual targets. We find that macrophages employ distinct strategies depending on target composition. Apoptotic cells alone are engulfed intact within tight, actin-rich membrane cups, whereas bacteria-laden apoptotic cells induce protrusive, actin-driven extensions that navigate along the apoptotic scaffold to access bacteria. Individual bacteria are selectively extracted and internalized, while apoptotic material is internalized in a piecemeal manner. Early phagosome analysis shows that most contain either bacteria or apoptotic material alone, with mixed cargo being less frequent. These findings demonstrate that macrophages can discriminate and sort the components of complex targets during uptake, revealing a previously unrecognized sophistication in phagocytic processing. This work provides a framework for understanding how innate immune cells integrate pathogen clearance while managing apoptotic material.

cell biology↗

bioRxiv2022

Outcome of Crash Course Training on Protein Structure Prediction with Artificial Intelligence

Protein structure predictions have broad impact on several science disciplines such as biology, bioengineering, and medical science. AlphaFold2[1] and RoseTTAFold[2] are the current state-of-the-art AI methods to predict the structures of proteins with an accuracy comparable to lower-resolution experimental methods. In its 2021 year review, both these methods were recognized as "breakthrough of the year" by Science magazine[3] and "method of the year" by Nature magazine [4]. It is timely and important to provide training and support on these emerging methods. Our crash course "Enabling Protein Structure Prediction with Artificial Intelligence "was conducted in collaboration with domain experts and research computing professionals. The crash course was well received by the community as there were 750 registrants from all over the world. Here we provide the summary of the crash course, describe our findings in organizing the crash course, and explain what preparation steps helped us with the hands-on training. CCS CONCEPTSComputing methodologies a Machine learning a Machine learning approaches a Bio-inspired approaches

scientific communication and education↗

bioRxiv2019

A language-familiarity effect on the recognition of computer-transformed vocal emotional cues

People are more accurate in voice identification and emotion recognition in their native language than in other languages, a phenomenon known as the language familiarity effect (LFE). Previous work on cross-cultural inferences of emotional prosody has left it difficult to determine whether these native-language advantages arise from a true enhancement of the auditory capacity to extract socially relevant cues in familiar speech signals or, more simply, from cultural differences in how these emotions are expressed. In order to rule out such production differences, this work employed algorithmic voice transformations to create pairs of stimuli in the French and Japanese language which differed by exactly the same amount of prosodic expression. Even though the cues were strictly identical in both languages, they were better recognized when participants processed them in their native language. This advantage persisted in three types of stimulus degradation (jabberwocky, shuffled and reversed sentences). These results provide univocal evidence that production differences are not the sole drivers of LFEs in cross-cultural emotion perception, and suggest that it is the listeners lack of familiarity with the individual speech sounds of the other language, and not e.g. with their syntax or semantics, which impairs their processing of higher-level emotional cues.

neuroscience↗

bioRxiv2017

Molecular characterization of breast cancer cell response to metabolic drugs

Metabolic reprogramming is a hallmark of cancer. We and other authors have previously shown that breast cancer subtypes present metabolism differences. In this study, breast cancer cell lines were treated with metformin and rapamycin. The response was heterogeneous across various breast cancer cells, leading to cell cycle disruption in specific conditions. The molecular effects of these treatments were characterized using sublethal doses, SNP genotyping and mass spectrometry-based proteomics. Protein expression was analyzed using probabilistic graphical models, showing that treatments elicit various responses in some biological processes, providing insights into cell responses to metabolism drugs. Moreover, a flux balance analysis approach using protein expression values was applied, showing that predicted growth rates were comparable with cell viability measurements and suggesting an increase in reactive oxygen species response enzymes due to metformin treatment. In addition, a method to assess flux differences in whole pathways was proposed. Our results show that these various approaches provide complementary information, which can be used to suggest hypotheses about the drugs mechanisms of action and the response to drugs that target metabolism.

systems biology↗