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- Buetti-Dinh, Antoine, 1984-, et al.
(author)
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Reverse engineering directed gene regulatory networks from transcriptomics and proteomics data of biomining bacterial communities with approximate Bayesian computation and steady-state signalling simulations
- 2020
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In: BMC Bioinformatics. - : BioMed Central (BMC). - 1471-2105. ; 21:1, s. 1-15
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Journal article (peer-reviewed)abstract
- Background: Network inference is an important aim of systems biology. It enables the transformation of OMICs datasets into biological knowledge. It consists of reverse engineering gene regulatory networks from OMICs data, such as RNAseq or mass spectrometry-based proteomics data, through computational methods. This approach allows to identify signalling pathways involved in specific biological functions. The ability to infer causality in gene regulatory networks, in addition to correlation, is crucial for several modelling approaches and allows targeted control in biotechnology applications. Methods: We performed simulations according to the approximate Bayesian computation method, where the core model consisted of a steady-state simulation algorithm used to study gene regulatory networks in systems for which a limited level of details is available. The simulations outcome was compared to experimentally measured transcriptomics and proteomics data through approximate Bayesian computation. Results: The structure of small gene regulatory networks responsible for the regulation of biological functions involved in biomining were inferred from multi OMICs data of mixed bacterial cultures. Several causal inter- and intraspecies interactions were inferred between genes coding for proteins involved in the biomining process, such as heavy metal transport, DNA damage, replication and repair, and membrane biogenesis. The method also provided indications for the role of several uncharacterized proteins by the inferred connection in their network context. Conclusions: The combination of fast algorithms with high-performance computing allowed the simulation of a multitude of gene regulatory networks and their comparison to experimentally measured OMICs data through approximate Bayesian computation, enabling the probabilistic inference of causality in gene regulatory networks of a multispecies bacterial system involved in biomining without need of single-cell or multiple perturbation experiments. This information can be used to influence biological functions and control specific processes in biotechnology applications.
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| 2. |
- Abrahams, Harriët J. G., et al.
(author)
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Moderators of the effect of psychosocial interventions on fatigue in women with breast cancer and men with prostate cancer : Individual patient data meta-analyses
- 2020
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In: Psycho-Oncology. - : Wiley. - 1057-9249 .- 1099-1611. ; 29:11, s. 1772-1785
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Research review (peer-reviewed)abstract
- ObjectivePsychosocial interventions can reduce cancer‐related fatigue effectively. However, it is still unclear if intervention effects differ across subgroups of patients. These meta‐analyses aimed at evaluating moderator effects of (a) sociodemographic characteristics, (b) clinical characteristics, (c) baseline levels of fatigue and other symptoms, and (d) intervention‐related characteristics on the effect of psychosocial interventions on cancer‐related fatigue in patients with non‐metastatic breast and prostate cancer.MethodsData were retrieved from the Predicting OptimaL cAncer RehabIlitation and Supportive care (POLARIS) consortium. Potential moderators were studied with meta‐analyses of pooled individual patient data from 14 randomized controlled trials through linear mixed‐effects models with interaction tests. The analyses were conducted separately in patients with breast (n = 1091) and prostate cancer (n = 1008).ResultsStatistically significant, small overall effects of psychosocial interventions on fatigue were found (breast cancer: β = −0.19 [95% confidence interval (95%CI) = −0.30; −0.08]; prostate cancer: β = −0.11 [95%CI = −0.21; −0.00]). In both patient groups, intervention effects did not differ significantly by sociodemographic or clinical characteristics, nor by baseline levels of fatigue or pain. For intervention‐related moderators (only tested among women with breast cancer), statistically significant larger effects were found for cognitive behavioral therapy as intervention strategy (β = −0.27 [95%CI = −0.40; −0.15]), fatigue‐specific interventions (β = −0.48 [95%CI = −0.79; −0.18]), and interventions that only targeted patients with clinically relevant fatigue (β = −0.85 [95%CI = −1.40; −0.30]).ConclusionsOur findings did not provide evidence that any selected demographic or clinical characteristic, or baseline levels of fatigue or pain, moderated effects of psychosocial interventions on fatigue. A specific focus on decreasing fatigue seems beneficial for patients with breast cancer with clinically relevant fatigue.
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