- by Li, L., Roy, P. G., Liu, Y., Zhang, Z., Xiong, D., Savan, R., Gokhale, N. S., Schang, L. M., Das, J., Yu, H.Viral-human protein interactions are critical for viral replication and modulation of the host immune response. Structural modeling of these interactions is vital for developing effective antiviral therapies and vaccines. However, 99% of experimentally determined binary host-viral interactions currently lack structural information. We aimed to address this gap by leveraging computational protein structure prediction methods. Using extensive benchmarking, we found AlphaFold to be the most accurate structure prediction model for host-pathogen protein interactions. We then predicted the structures of 11,666 binary […]
- by Mair, A., Gomez Peral, E., Ptashnyk, M., Dupuy, L. X.The chemical compounds produced by plant roots, referred to generally as rhizodeposits, affect several soil hydraulic properties. For example, the surface tension of soil water, and the contact angle between menisci and the pore surface. What remains less clear is how these effects manifest when considering soil water infiltration and retention, and the consequent impact on the availability of water for uptake by plant roots. By modifying the Richards equation, a novel model for soil water transport was developed which […]
- by Chowdhury, S., Ansari, A., Verma, P., Mavlankar, A., Tripathi, A., Pena, M., Sharma, R., Caskey, J., Truman, R., Singh, P.M. leprae adapts to the host cell environment and disrupts protein-protein interactions inside the host cell for its own survival and proliferation. This study is attempts to comprehend these host-pathogen interactions at the systems level. We utilised RNA-Seq data set of leprosy-resistant and leprosy-susceptible armadillos and investi-gated how five different human signaling pathways get hijacked by M. leprae. We also identified the corresponding human homolog proteins. By applying graph theory on these protein networks, we predicted 25 proteins which play […]
- by Nihant, B., Verdonschot, J., Balan, S., Thielecke, E., Luiken, J., Nabben, M., Heymans, S., Breuer, M., Adriaens, M.Dilated cardiomyopathy (DCM) is associated with shifts in cardiac metabolism. Those shifts are inconsistent between patients, possibly due to heterogeneity in DCM etiologies. Identifying metabolic subtypes, or metabotypes, in DCM patients may open personalized treatment opportunities. Developing a methodology to identify metabotypes would be a boon in this regard. Here, we describe a metabotyping pipeline, integrating advanced metabolic modeling methods optimized for cardiac research, to uncover these subtypes using widely available transcriptomics data. We applied our method to publicly available […]
- by Guo, X., Sen, S., Gonzalez, J., Hoffmann, A.As immune sentinel cells, macrophages are required to respond specifically to diverse immune threats and initiate appropriate immune responses. This stimulus-response specificity (SRS) is in part encoded in the signaling dynamics of the NF{kappa}B transcription factor. While experimental stimulus-response studies have typically focused on single defined ligands, in physiological contexts cells are generally exposed to mixtures of ligands. It remains unclear how macrophages process exposure to ligand mixtures and particularly whether they are able to maintain SRS in such complex […]
- by Moyd, S. A., Xiao, S., Gaskins, A. J., Zhang, Q.Introduction: Human ovaries begin development in utero. Through oogenesis, the numbers of oocytes and primordial follicles peak to a few million during fetal development, then decline to hundreds of thousands per ovary at birth. These primordial follicles do not regenerate and are thus regarded as the ovarian reserve. Over the life course, the reserve continues to deplete, due to atresia and activation, until menopause when about 1000 primordial follicles remain. Exposure to chemotherapy drugs and environmental pollutants can accelerate follicular […]
- by Arazkhani, N., Luo, H., Tang, D., Cochran, B., Miskov-Zivanov, N.In this work, our goal was twofold: (1) improve an existing glioblastoma multiforme (GBM) executable mechanistic model and (2) evaluate the effectivenes traditional natural language processing (NLP) pipeline and the generative AI approach in the process of model improvement. We used a suite of graph metrics and tools for interaction filtering and classification to collect data and conduct the analysis. Our results suggest that a more comprehensive literature search is necessary to collect enough information through automated paper retrieval and […]
- by Fernandez, J. D., Navarro-Paya, D., Santiago, A., Cerda, A., Canan, J., Contreras-Riquelme, S., Moyano, T. C., Melet, L., Johnson, N. R., Alvarez, J. M., Canales, J., Matus, J. T., Vidal, E. A.Tomato (Solanum lycopersicum) is a globally important crop, yet the gene regulatory networks (GRNs) controlling gene expression remain poorly understood. In this study, we constructed GRNs for roots, leaves, flowers, fruits, and seeds by inferring transcription factor (TF)-target interactions from over 10,000 RNA-seq libraries using the GENIE3 algorithm. We refined these networks with gene co-expression data and computational predictions of TF binding sequences in open chromatin sites. Our networks confirmed key TFs, including TOMATO AGAMOUS LIKE 1 and RIPENING INHIBITOR […]
- by Yin, C., Kindt, A., Harms, A. C., Hartman, R., Hankemeier, T., de Lange, E. C. M.BackgroundGathering information on Alzheimers disease (AD) progression in human poses significant challenges due to the lengthy timelines and ethical considerations involved. Animal AD models provide a valuable alternative for conducting mechanistic studies and testing potential therapeutic strategies. Disturbed lipid homeostasis is among the earliest neuropathological features of AD. AimTo identify longitudinal plasma lipidomic changes associated with age, sex, and AD in male and female TgF344-AD and wild-type rats. MethodsA total of 751 lipids in 141 rats (n=73 TgF344-AD; n= 68 […]
- by Pescher, P., Douche, T., Giai Gianetto, Q., Druart, K., Kovarova, J., Li, B., Proux, C., Legendre, R., Varet, H., Rajan, K. S., Piel, L., Besse, C., Boland, A., Deleuze, J.-F., Matondo, M., Barrett, M. P., Michaeli, S., Späth, G. F.Genetic feedback control represents a central paradigm in regulation of biological systems and their response to environmental change. Vector-borne pathogens have evolved complex developmental programs to adapt to very distinct host environments, but the relevance of feedback regulation in stage differentiation remains to be elucidated. Here we address this open question in the trypanosomatid parasite Leishmania that shows constitutive gene transcription, thus providing a unique model system to assess post-transcriptional mechanisms of feedback regulation in the absence of confounding transcriptional […]
- by Mottaqi, M., Zhang, P., Xie, L.BackgroundAlzheimers disease (AD) is a complex neurodegenerative disorder with substantial molecular variability across different brain regions and individuals, hindering therapeutic development. This study introduces PRISM-ML, an interpretable machine learning (ML) framework integrating multiomics data to uncover patient-specific biomarkers, subtissue-level pathology, and drug repurposing opportunities. MethodsWe harmonized transcriptomic and genomic data of three independent brain studies containing 2105 post-mortem brain samples (1363 AD, 742 controls) across nine tissues. A Random Forest classifier with SHapley Additive exPlanations (SHAP) identified patient-level biomarkers. Clustering […]
- by Ito, T., Ohno, S., Wang, Y., Uematsu, S., Kuroda, S., Shimizu, H.Metabolism, the biochemical reaction network within cells, is crucial for life, health, and disease. Recent advances in multi-omics technologies, enabling the simultaneous measurement of transcripts, proteins, and metabolites, provide unprecedented opportunities to comprehensively analyze metabolic regulation. However, effectively integrating these diverse data types to decipher the complex interplay between enzymes and metabolites remains a significant challenge due to the extensive data requirements of kinetic modeling approaches and the limited interpretability of machine learning approaches. Here, we present MetDeeCINE, a novel […]
- by Abreu, P., Moon, R., Mendelson, J. B., Markowski, T., Higgins, L., Murray, K., Guerrero, C., Blake, J., Prisco, S., Prins, K.BackgroundPulmonary arterial hypertension (PAH) is a rare but debilitating condition that causes exercise intolerance and ultimately death. Skeletal muscle derangements contribute to depressed exercise capacity in PAH, but the mechanisms underlying muscle dysfunction including the changes in muscle biology based on fiber type are understudied. MethodsWe evaluated exercise capacity, muscle histopathology, mitochondrial density, mitochondrial proteomics, and metabolomics/lipidomics of quadriceps (predominately fast fibers) and soleus (predominately slow fibers) muscles in the monocrotaline (MCT) rat model of PAH. ResultsMCT rats exhibited impaired […]
- by Farinas, M., Tsirvouli, E., Zobolas, J., Aittokallio, T., Flobak, A., Lehti, K.Boolean models are widely used for studying dynamic processes of biological systems. However, their inherent discrete nature limits their ability to capture continuous aspects of signal transduction, such as signal strength or protein activation levels. Although existing tools provide some path exploration capabilities that can be used to explore signal transduction circuits, the computational workload often requires simplifying assumptions that compromise the accuracy of the analysis. Here, we introduce BooLEVARD, a Python package designed to efficiently quantify the number of […]
- by Magnusson, R., Söderberg, C., Ward, L. J., Arpe, J., Kugelberg, F. C., Elmsjö, A., Green, H., Nyman, E.An accurate prediction of the time since death, known as the post-mortem interval (PMI), remains a critical research question in forensic and police investigations. Current methods, such as rectal temperature or vitreous potassium levels, only provide reliable PMI estimations up to 48-72 hours. In this study, we utilized metabolomic data from femoral whole blood samples of 4,876 individuals with known PMIs ranging from 1 to 67 days. We developed a neural network model that predicted PMI with a mean/median absolute […]
- by Shin, J., Carothers, J. M., Sauro, H. M.Bayesian Metabolic Control Analysis (BMCA) has emerged as a promising framework for inferring metabolic control coefficients in data-limited scenarios by integrating Bayesian inference with linlog rate laws. However, its predictive accuracy and limitations remain underexplored. This study systematically evaluates BMCAs ability to infer elasticity values, flux control coefficients (FCCs), and concentration control coefficients (CCCs) under varying data availability conditions using three synthetic metabolic network models. Our findings highlight the strengths and weaknesses of BMCA, guiding its application in metabolic engineering […]
- by Rehawi, G., Hagenberg, J., Brueckl, T. M., Kopf-Beck, J., Saemann, P., Moyon, L., Binder, E. B., List, M., Marsico, A., Knauer-Arloth, J.Isoform-specific expression patterns have been implicated in stress-related psychiatric disorders like major depressive disorder (MDD), yet the extent of their involvement and their interrelationships remain unclear. We constructed co-expression networks for individuals affected (n=210, 81% with depressive symptoms) and unaffected (n=95) by stress-related psychiatric disorders. We incorporated total gene expression (TE) and isoform ratio (IR) data and validated the inferred networks using advanced graph generation techniques. Our analysis revealed distinct network topology and structure between the two groups. Investigation of […]
- by Alarcon, T., Menendez, J. A., Sardanyes, J.The maintenance of epigenetic landscapes (EL) requires the precise regulation of chromatin-modifying enzymes (ChME). Competition for ChME can lead to degradation of ELs, triggering large-scale changes in the cell fate information contained in EL. Predicting impending epigenetic tipping points (ETP) by identifying early warning signals (EWS) may help to anticipate the onset of cell identity loss during aging and cancer. We have developed a general mathematical framework that incorporates different connectivity patterns generated by the 3D chromatin folding structure to […]
- by Vibishan, B., Jain, P., Sharma, V., Hari, K., Kadelka, C., George, J. T., Jolly, M. K.Cancer is heterogeneous and variability in drug sensitivity is widely documented across cancer types. Adaptive therapy is an emerging modality of cancer treatment that leverages this drug resistance heterogeneity to improve therapeutic outcomes. Current standard treatments typically eliminate a large fraction of drug-sensitive cells, leading to drug-resistant relapse due to competitive release. Adaptive therapy aims to retain some drug-sensitive cells, thereby limiting resistant cell growth by ecological competition. While early clinical trials of such a strategy have shown promise, optimisation […]
- by Grillo, J. F., Tirpitz, V., Reichert, J., Canesi, M., Reynaud, S., Douville, E., Ziegler, M.The symbiosis between the dinoflagellate Symbiodiniaceae family and reef-building corals underpins the productivity of coral reefs. This relationship facilitates the deposition of calcium- carbonate skeletons that build the reef structure thanks to the energy derived from photosynthesis. The loss of Symbiodiniaceae from coral tissues–resulting in coral bleaching–impedes coral growth and can lead to mass mortality if the symbiosis fails to recover. Given that Symbiodiniaceae communities are dynamic and can shift in response to environmental stressors in the decades- to centuries-long […]
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