Biology subjects

Atallah, C.

Publications and source records attributed to Atallah, C..

5 recordsLinked to original sources

bioRxiv2026

Effects of Polyunsaturated Fatty Acids and Selenoneine on Growth and Lipidomic Profiles in Patient-Derived Prostate Cancer Organoids

Prostate cancer (PCa) is the second most frequent cancer in men and represents a major public health issue worldwide. To complement current therapeutic approaches, our work aims to understand the impact of nutritional interventions on PCa evolution. Recent studies showed that supplementation with omega-3 fatty acids reduces tumour growth in PCa mouse models. Several beneficial effects were observed, particularly on the gut microbiome, suggesting indirect effects on tumours, and whether these fatty acids have a direct molecular impact on the prostate remains elusive. Here, we hypothesized that the prostate directly takes up dietary fatty acids to reprogram metabolism, which could contribute to the beneficial effects on cancer treatment. To test this hypothesis, five series of patient-derived organoids (PDOs), with paired lines originating from normal and tumour samples, were treated with omega-3 and omega-6 fatty acids. While heterogeneity was observed in growth capacities of PDO lines, omega-3 supplementation overall decreased PDO growth, with a stronger decrease in PCa lines. This effect was stronger when co-treated with selenoneine, a nutrient enriched in some marine foods with high omega-3 content. Lipidomic experiments were then performed, revealing that PDOs can take up fatty acids, both omega-3 and omega-6. Furthermore, PDOs could metabolize omega-3 fatty acids into downstream metabolites, even if high inter-patient heterogeneity was observed. Altogether, this study, using PDOs as a working model to study the human prostate, provides a better understanding of the molecular impacts of fatty acid supplementation on prostate and PCa biology for the development of future nutritional interventions.

cancer biology↗

bioRxiv2025

PIMENTO: A PrIMEr infereNce TOolkit to facilitate large-scale calling of amplicon sequence variants

The identification of amplicon sequence variants from DNA metabarcoding data is a common method for revealing the taxonomic makeup of environmental samples, and for allowing comparative studies between similar datasets. A significant hurdle to the large-scale calling of amplicon sequence variants from publicly available nucleotide datasets is the heterogeneous presence of primer sequences in reads, the removal of which is a necessary pre-processing step for this form of analysis. Furthermore, as the details of the experimental primers are rarely captured in the metadata associated with the sequence records, there is a need for a method that can automatically infer the presence and identity of primers in sequencing data. In this work, we introduce PIMENTO, a Python package which uses a dual-strategy approach for identifying primers that are present in sequencing reads to enable their removal, and therefore facilitate amplicon sequence variant calling at scale.

bioinformatics↗

bioRxiv2019

Functional Analysis of Enzyme Families Using Residue-Residue Coevolution Similarity Networks

MotivationResidue-residue coevolution has been used to elucidate structural information of enzymes. Networks of coevolution patterns have also been analyzed to discover residues important for the function of individual enzymes. In this work, we take advantage of the functional importance of coevolving residues to perform network-based clustering of subsets of enzyme families based on similarities of their coevolution patterns, or \"Coevolution Similarity Networks\". The power of these networks in the functional analysis of sets of enzymes is explored in detail, using Sequence Similarity Networks as a benchmark.\n\nResultsA novel method to produce protein-protein networks showing the similarity between proteins based on the matches in the patterns of their intra-residue residue coevolution is described. The properties of these co-evolution similarity networks (CSNs) was then explored, especially in comparison to widely used sequence similarity networks (SSNs). We focused on the predictive power of CSNs and SSNs for the annotation of enzyme substrate specificity in the form of Enzyme Commission (EC) numbers using a label propagation approach. A method for systematically defining the threshold necessary to produce the optimally predictive CSNs and SSNs is described. Our data shows that, for the two protein families we analyse, CSNs show higher predictive power for the reannotation of substrate specificity for previously annotated enzymes retrieved from Swissprot. A topological analysis of both CSNs and SSNs revealed core similarities in the structure, topology and annotation distribution but also reveals a subset of nodes and edges that are unique to each network type, highlighting their complementarity. Overall, we propose CSNs as a new method for analysing the function enzyme families that complements, and offers advantages to, other network based methods for protein family analysis.\n\nAvailabilitySource code available on request.

bioinformatics↗

bioRxiv2018

Capturing Multicellular System Designs Using the Synthetic Biology Open Language (SBOL)

Synthetic biology aims to improve the development of biological systems and in-crease their reproducibility through the use of engineering principles, such as stan-dardisation and modularisation. It is important that these systems can be represented and shared in a standard way to ensure they are easily understood, reproduced, and utilised by other researchers. The Synthetic Biology Open Language (SBOL) is a data standard for sharing biological designs and information about their implementation and characterisation. Thus far, this standard has been used to represent designs in homogeneous systems, where the same design is implemented in every cell. In recent years there has been increasing interest in multicellular systems, where biological designs are split across multiple cells to optimise the system behaviour and function. Here we show how the SBOL standard can be used to represent such multicellular systems, and hence how researchers can better share designs with the community.

synthetic biology↗

bioRxiv2018

Using Network Analysis to Localize the Epileptogenic Zone from Invasive EEG Recordings in Intractable Focal Epilepsy

Treatment of medically intractable focal epilepsy (MIFE) by surgical resection of the epileptogenic zone (EZ) is often effective provided the EZ can be reliably identified. Even with the use of invasive recordings, the clinical differentiation between the EZ and normal brain areas can be quite challenging, mainly in patients without MRI detectable lesions. Consequently, despite relatively large brain regions being removed, surgical success rates barely reach 60-65%. Such variable and unfavorable outcomes associated with high morbidity rates are often caused by imprecise and/or inaccurate EZ localization. We developed a localization algorithm that uses network-based data analytics to process invasive EEG recordings. This network algorithm analyzes the centrality signatures of every contact electrode within the recording network and characterizes contacts into susceptible EZ based on the centrality trends over time. The algorithm was tested in a retrospective study that included 42 patients from four epilepsy centers. Our algorithm had higher agreement with EZ regions identified by clinicians for patients with successful surgical outcomes and less agreement for patients with failed outcomes. These findings suggest that network analytics and a network systems perspective of epilepsy may be useful in assisting clinicians in more accurately localizing the EZ.\n\nAUTHOR SUMMARYEpilepsy is a disease that results in abnormal firing patterns in parts of the brain that comprise the epileptogenic network, known as the epileptogenic zone (EZ). Current methods to localize the EZ for surgical treatment often requires observations of hundreds of thousands of EEG data points measured from many electrodes implanted in a patients brain. In this paper, we used network science to show that EZ regions may exhibit specific network signatures before, during and after seizure events. Our algorithm computes the likelihood of each electrode being in the EZ and tends to agree more with clinicians during successful resections and less during failed surgeries. These results suggest that a networked analysis approach to EZ localization may be valuable in a clinical setting.

neuroscience↗