A method to decipher pleiotropy by detecting underlying heterogeneity driven by hidden subgroups applied to autoimmune and neuropsychiatric diseases

There is growing evidence of shared risk alleles between complex traits (pleiotropy), including autoimmune and neuropsychiatric diseases. This might be due to sharing between all individuals (whole-group pleiotropy), or a subset of individuals within a genetically heterogeneous cohort (subgroup heterogeneity). BUHMBOX is a well-powered statistic distinguishing between these two situations using genotype data. We observed a shared genetic basis between 11 autoimmune diseases and type 1 diabetes (T1D, p0.2, 6,670 T1D cases and 7,279 RA cases). Genetic sharing between seronegative and seropostive RA (p<10−9) had significant evidence of subgroup heterogeneity, suggesting a subgroup of seropositive-like cases within seronegative cases (pBUHMBOX=0.008, 2,406 seronegative RA cases). We also observed a shared genetic basis between major depressive disorder (MDD) and schizophrenia (p<10−4) that was not explained by subgroup heterogeneity (pBUHMBOX=0.28 in 9,238 MDD cases)

Predicting Individuals' Learning Success from Patterns of Pre-Learning MRI Activity

Performance in most complex cognitive and psychomotor tasks improves with training, yet the extent of improvement varies among individuals. Is it possible to forecast the benefit that a person might reap from training? Several behavioral measures have been used to predict individual differences in task improvement, but their predictive power is limited. Here we show that individual differences in patterns of time-averaged T2*-weighted MRI images in the dorsal striatum recorded at the initial stage of training predict subsequent learning success in a complex video game with high accuracy. These predictions explained more than half of the variance in learning success among individuals, suggesting that individual differences in neuroanatomy or persistent physiology predict whether and to what extent people will benefit from training in a complex task. Surprisingly, predictions from white matter were highly accurate, while voxels in the gray matter of the dorsal striatum did not contain any information about future training success. Prediction accuracy was higher in the anterior than the posterior half of the dorsal striatum. The link between trainability and the time-averaged T2*-weighted signal in the dorsal striatum reaffirms the role of this part of the basal ganglia in learning and executive functions, such as task-switching and task coordination processes. The ability to predict who will benefit from training by using neuroimaging data collected in the early training phase may have far-reaching implications for the assessment of candidates for specific training programs as well as the study of populations that show deficiencies in learning new skills

Tumor Inhibitory Effect of IRCR201, a Novel Cross-Reactive c-Met Antibody Targeting the PSI Domain

Hepatocyte growth factor receptor (HGFR, c-Met) is an essential member of the receptor tyrosine kinase (RTK) family that is often dysregulated during tumor progression, driving a malignant phenotypic state and modulating important cellular functions including tumor growth, invasion, metastasis, and angiogenesis, providing a strong rationale for targeting HGF/c-Met signaling axis in cancer therapy. Based on its protumorigenic potentials, we developed IRCR201, a potent antagonistic antibody targeting the plexin-semaphorin-integrin (PSI) domain of c-Met, using synthetic human antibody phage libraries. We characterized and evaluated the biochemical properties and tumor inhibitory effect of IRCR201 in vitro and in vivo. IRCR201 is a novel fully-human bivalent therapeutic antibody that exhibits cross-reactivity against both human and mouse c-Met proteins with high affinity and specificity. IRCR201 displayed low agonist activity and rapidly depleted total c-Met protein via the lysosomal degradation pathway, inhibiting c-Met-dependent downstream activation and attenuating cellular proliferation in various c-Met-expressing cancer cells. In vivo tumor xenograft models also demonstrated the superior tumor inhibitory responsiveness of IRCR201. Taken together, IRCR201 provides a promising therapeutic agent for c-Met-positive cancer patients through suppressing the c-Met signaling pathway and tumor growth

Comparison of prediction accuracy for various signal sources.

<p>Predictions based on patterns of T1-weighted images (MPRAGE) in the dorsal striatum were significantly less accurate than those based on time-averaged T2*-weighted images (EPI). Voxels located in white matter allowed for much better predictions than those in gray matter within the dorsal striatum. Finally, decoding was significantly better from the anterior than the posterior half of the left dorsal striatum. Error bars indicate the 95% confidence interval for the Pearson correlation coefficients. *p<0.05, **p<0.01, ***p<0.001.</p

Accuracy of predicting improvements in sub-scores from the time-averaged T2*-weighted signal.

<p>(A) Improvement in the <i>control</i> sub-score is predicted to a limited extent by the time-averaged T2* activity in the left ventral striatum (nucleus accumbens). (B) The <i>velocity</i> sub-score shows small but significant correlations in the left caudate nucleus and the left nucleus accumbens. (C) Improvement in the <i>speed</i> sub-score is predicted highly significantly by time-averaged T2*-weighted activity in the dorsal striatum, in particular the caudate nucleus, but not by the ventral striatum. Correlation of predicted and measured score improvements is higher in the left than the right hemisphere. This pattern of results matches that of the total score shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0016093#pone-0016093-g004" target="_blank">figure 4</a>. (D) The <i>points</i> sub-score shows no significant prediction except for a small but significant correlation of predicted and measures score improvement in the left caudate nucleus. *p<0.05, **p<0.01, ***p<0.001.</p

Space Fortress game, experimental time line and pre-processing flow.

<p>(A) Schematic interface of the Space Fortress video game. The objective of the game is to destroy the space fortress (yellow, center of the display) by shooting missiles at it from a space ship (yellow, upper-left corner), while moving the space ship inside the hexagon with thruster commands to evade mines (red diamond) and to collect resources (‘$’ sign). (B) Timeline of the experiment for a typical participant. After initial instructions, participants played Space Fortress in the MRI scanner while their brain activity was recorded. Next, participants underwent a total of 20 hours of training, followed by a second MRI session. We used the difference in total game score between the two MRI sessions (i.e. the score improvement) as a measure of learning success. (C) MRI preprocessing workflow: EPI volume series (1^st MR session) of different subjects are registered to the common space (MNI space) by linear and non-linear registration. After normalization, temporal averages of the EPI volumes are used for the subsequent analysis.</p

Zero and second order partial correlations.

<p>Zero (Pearson correlation) and second-order partial correlations are calculated for a linear regression model with measured score improvements as the predicted variable and three explanatory variables: score improvement predicted by the SVR algorithm from time-averaged T2*-weighted activity in the dorsal striatum, volume of the dorsal striatum, and initial score.</p

Predicting score improvement from MRI activity in the dorsal striatum.

<p>(A) Correlation of measured score improvement with the spatial mean of the time-averaged T2*-weighted signal in the dorsal striatum. Mean activity of 34 subjects is significantly correlated with score improvement. (B) Correlation of measured score improvements with score improvement predicted from multi-voxel patterns of the T2*-weighted signal in the dorsal striatum. It shows an even higher correlation than in A). The dashed lines show the least-squares best linear fits in figures A and B. **p<0.01, ***p<0.001.</p