Continuous time Bayesian networks identify Prdm1 as a negative regulator of TH17 cell differentiation in humans

T helper 17 (TH17) cells represent a pivotal adaptive cell subset involved in multiple immune disorders in mammalian species. Deciphering the molecular interactions regulating TH17 cell differentiation is particularly critical for novel drug target discovery designed to control maladaptive inflammatory conditions. Using continuous time Bayesian networks over a time-course gene expression dataset, we inferred the global regulatory network controlling TH17 differentiation. From the network, we identified the Prdm1 gene encoding the B lymphocyte-induced maturation protein 1 as a crucial negative regulator of human TH17 cell differentiation. The results have been validated by perturbing Prdm1 expression on freshly isolated CD4+ naïve T cells: reduction of Prdm1 expression leads to augmentation of IL-17 release. These data unravel a possible novel target to control TH17 polarization in inflammatory disorders. Furthermore, this study represents the first in vitro validation of continuous time Bayesian networks as gene network reconstruction method and as hypothesis generation tool for wet-lab biological experiments.ASTAR (Agency for Sci., Tech. and Research, S’pore)Published versio

Towards the Personalized Treatment of Glioblastoma: Integrating Patient-Specific Clinical Data in a Continuous Mechanical Model.

Glioblastoma multiforme (GBM) is the most aggressive and malignant among brain tumors. In addition to uncontrolled proliferation and genetic instability, GBM is characterized by a diffuse infiltration, developing long protrusions that penetrate deeply along the fibers of the white matter. These features, combined with the underestimation of the invading GBM area by available imaging techniques, make a definitive treatment of GBM particularly difficult. A multidisciplinary approach combining mathematical, clinical and radiological data has the potential to foster our understanding of GBM evolution in every single patient throughout his/her oncological history, in order to target therapeutic weapons in a patient-specific manner. In this work, we propose a continuous mechanical model and we perform numerical simulations of GBM invasion combining the main mechano-biological characteristics of GBM with the micro-structural information extracted from radiological images, i.e. by elaborating patient-specific Diffusion Tensor Imaging (DTI) data. The numerical simulations highlight the influence of the different biological parameters on tumor progression and they demonstrate the fundamental importance of including anisotropic and heterogeneous patient-specific DTI data in order to obtain a more accurate prediction of GBM evolution. The results of the proposed mathematical model have the potential to provide a relevant benefit for clinicians involved in the treatment of this particularly aggressive disease and, more importantly, they might drive progress towards improving tumor control and patient's prognosis

In vitro temperature dependent activation of T-lymphocytes in Common wall lizards (Podarcis muralis) in response to PHA stimulation

Ecological immunology attempts to explain the variability of immune response among individuals by invoking costs and trade-offs, which may optimize the immune defence against pathogens. In ectotherms body temperature is correlated to that of the surrounding environment, so that their entire physiology, including immune functions, is influenced by the environmental temperature. We used in vitro phytohaemoagglutinin (PHA) stimulation in order to assess the effects of temperature on cell mediated adaptive response in male and female Common wall lizards (Podarcis muralis). Cell cultures were prepared from blood samples, inoculated with PHA and incubated at 22°C, 25°C, 32°C, and 38°C for three days. PHA stimulation caused proliferation of T-lymphocytes, but the effect depended on the incubation temperature. Lymphocyte proliferation was significantly impaired at both 22°C and 38°C compared to 32°C, which represented the highest levels of activation. Furthermore, lymphocyte activation was more variable in males while females were less immune suppressed than males at low temperatures. Differences between sexes suggest a possible influence of steroid hormones

Sensitivity analysis of the parameters k_n and M.

<p>The influence of the parameters <i>k</i>_<i>n</i> and <i>M</i> on the cells volume fraction distribution at time <i>t</i> = 6 days is studied. The resulting tumor are characterized in terms of: the ratio between the maximum volume fraction at the final time, <i>ϕ</i>^<i>M</i>, and maximum initial volume fraction, </p><p></p><p></p><p></p><p><mi>ϕ</mi><mn>0</mn><mi>M</mi></p><p></p><p></p><p></p>; the ratio between the final and the initial volume; the ratio between the major semi-axis, Δ<i>x</i>, and the two minor semi-axes, Δ<i>y</i> and Δ<i>z</i>, defined as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132887#pone.0132887.g004" target="_blank">Fig 4</a>.<p></p

Sensitivity analysis of the parameters S_n and δ_n.

<p>The influence of the parameters <i>S</i>_<i>n</i> and <i>δ</i>_<i>n</i> on the cell volume fraction and on the dimensionless nutrient concentration is reported at time <i>t</i> = 9 days.</p

Influence of brain fibers’ alignment on tumor growth.

<p>(A) Tumor concentration plotted over the <i>T</i>_<i>xx</i> component (in transparency), at times <i>t</i> = 5 day, <i>t</i> = 15 day, <i>t</i> = 25 day: the cellular fraction shows an anisotropic distribution that follows the preferential direction determined by the <i>T</i>_<i>xx</i> component. (B) Tumor volume at <i>t</i> = 25 day overlapped to the maps of <i>T</i>_<i>xx</i> over the brain mesh cut along <i>xy</i> and <i>xz</i> planes and to the map of <i>T</i>_<i>zz</i> over the brain mesh cut along <i>xz</i>-plane: the glioblastoma assumes an elongated shape along the <i>x</i> direction, whereas it has a flat top in the <i>z</i>-direction, as <i>T</i>_<i>zz</i> is almost null there.</p

Patient-specific medical and numerical DTI data, depicted on a slice cut along the plane xy.

<p>(A) A single component of the tensor <b>D</b>, obtained from the DTI medical images, is represented for each image: the intensity of the voxels is related to the diffusion coefficient along the relative direction (see the gray-scale at the bottom). (B) Numerical patient-specific components of the <i>diffusion tensor</i><b>D</b> depicted on the same slice of the medical images: the diffusion coefficient is higher in the region occupied by the cerebrospinal fluid (red colored areas), where the diffusion is unconstrained. (C) Corresponding patient-specific components of the <i>tensor of preferential directions</i><b>T</b>: in isotropic region, e.g. the cerebrospinal fluid and the grey matter, <i>T</i>_<i>xx</i> ≈ <i>T</i>_<i>yy</i> ≈ <i>T</i>_<i>zz</i> ≈ 1 and <i>T</i>_<i>xy</i> ≈ <i>T</i>_<i>xz</i> ≈ <i>T</i>_<i>yz</i> ≈ 0, while in the white matter, instead, 0 < <i>T</i>_<i>ii</i> < 3 with i = (x, y, z) and 0 < <i>T</i>_<i>ij</i> < 1 with i, j = (x, y, z) and <i>i</i> ≠ <i>j</i>, denoting an anisotropic region.</p

Post-contrast T1-MR of a patient affected by GBM and corresponding segmented slices.

<p>(A) Axial, sagittal and coronal slices of post-contrast T1-MR in a patient with right parietal GBM (white arrow), used for image segmentation. (B) In the segmented brain image, the white region represents the white matter, the grey areas indicate the grey matter, while the cerebrospinal fluid is labeled by the blue color.</p