Several imaging modalities, including T1-weighted structural imaging, diffusion tensor imaging, and functional MRI can show chronological age related changes. Employing machine learning algorithms, an individual's imaging data can predict their age with reasonable accuracy. While details vary according to modality, the general strategy is to: (1) extract image-related features, (2) build a model on a training set that uses those features to predict an individual's age, (3) validate the model on a test dataset, producing a predicted age for each individual, (4) define the “Brain Age Gap Estimate” (BrainAGE) as the difference between an individual's predicted age and his/her chronological age, (5) estimate the relationship between BrainAGE and other variables of interest, and (6) make inferences about those variables and accelerated or delayed brain aging. For example, a group of individuals with overall positive BrainAGE may show signs of accelerated aging in other variables as well. There is inevitably an overestimation of the age of younger individuals and an underestimation of the age of older individuals due to “regression to the mean.” The correlation between chronological age and BrainAGE may significantly impact the relationship between BrainAGE and other variables of interest when they are also related to age. In this study, we examine the detectability of variable effects under different assumptions. We use empirical results from two separate datasets [training = 475 healthy volunteers, aged 18–60 years (259 female); testing = 489 participants including people with mood/anxiety, substance use, eating disorders and healthy controls, aged 18–56 years (312 female)] to inform simulation parameter selection. Outcomes in simulated and empirical data strongly support the proposal that models incorporating BrainAGE should include chronological age as a covariate. We propose either including age as a covariate in step 5 of the above framework, or employing a multistep procedure where age is regressed on BrainAGE prior to step 5, producing BrainAGE Residualized (BrainAGER) scores.

Deciphering the signaling pathways that govern stimulation of naïve CD4+ T helper cells by antigen-presenting cells via formation of the immunological synapse is key to a fundamental understanding of the progression of successful adaptive immune response. The study of T cell – APC interactions in vitro is challenging, however, due to the difficulty of tracking individual, nonadherent cell pairs over time. Studying single cell dynamics over time reveals rare, but critical, signaling events that might be averaged out in bulk experiments, but these less common events are undoubtedly important for an integrated understanding of a cellular response to its microenvironment. We describe a novel application of microfluidic technology that overcomes many limitations of conventional cell culture and enables the study of hundreds of passively sequestered hematopoietic cells for extended periods of time. This microfluidic cell trap device consists of 440 18 μm×18 μm×10 μm PDMS, bucket-like structures opposing the direction of flow which serve as corrals for cells as they pass through the cell trap region. Cell viability analysis revealed that more than 70% of naïve CD4+ T cells (TN), held in place using only hydrodynamic forces, subsequently remain viable for 24 hours. Cytosolic calcium transients were successfully induced in TN cells following introduction of chemical, antibody, or cellular forms of stimulation. Statistical analysis of TN cells from a single stimulation experiment reveals the power of this platform to distinguish different calcium response patterns, an ability that might be utilized to characterize T cell signaling states in a given population. Finally, we investigate in real-time contact and non-contact-based interactions between primary T cells and dendritic cells, two main participants in the formation of the immunological synapse. Utilizing the microfluidic traps in a daisy-chain configuration allowed us to observe calcium transients in TN cells exposed only to media conditioned by secretions of lipopolysaccharide-matured dendritic cells, an event which is easily missed in conventional cell culture where large media-to-cell ratios dilute cellular products. Further investigation into this intercellular signaling event indicated that LPS-matured dendritic cells, in the absence of antigenic stimulation, secrete chemical signals that induce calcium transients in TN cells. While the stimulating factor(s) produced by the mature dendritic cells remains to be identified, this report illustrates the utility of these microfluidic cell traps for analyzing arrays of individual suspension cells over time and probing both contact-based and inter-cellular signaling events between one or more cell populations.

BackgroundThe overall influence of gene interaction in human disease is unknown. In cystic fibrosis (CF) a single allele of the cystic fibrosis transmembrane conductance regulator (CFTR-ΔF508) accounts for most of the disease. In cell models, CFTR-ΔF508 exhibits defective protein biogenesis and degradation rather than proper trafficking to the plasma membrane where CFTR normally functions. Numerous genes function in the biogenesis of CFTR and influence the fate of CFTR-ΔF508. However it is not known whether genetic variation in such genes contributes to disease severity in patients. Nor is there an easy way to study how numerous gene interactions involving CFTR-ΔF would manifest phenotypically.MethodsTo gain insight into the function and evolutionary conservation of a gene interaction network that regulates biogenesis of a misfolded ABC transporter, we employed yeast genetics to develop a 'phenomic' model, in which the CFTR-ΔF508-equivalent residue of a yeast homolog is mutated (Yor1-ΔF670), and where the genome is scanned quantitatively for interaction. We first confirmed that Yor1-ΔF undergoes protein misfolding and has reduced half-life, analogous to CFTR-ΔF. Gene interaction was then assessed quantitatively by growth curves for approximately 5,000 double mutants, based on alteration in the dose response to growth inhibition by oligomycin, a toxin extruded from the cell at the plasma membrane by Yor1.ResultsFrom a comparative genomic perspective, yeast gene interactions influencing Yor1-ΔF biogenesis were representative of human homologs previously found to modulate processing of CFTR-ΔF in mammalian cells. Additional evolutionarily conserved pathways were implicated by the study, and a ΔF-specific pro-biogenesis function of the recently discovered ER membrane complex (EMC) was evident from the yeast screen. This novel function was validated biochemically by siRNA of an EMC ortholog in a human cell line expressing CFTR-ΔF508. The precision and accuracy of quantitative high throughput cell array phenotyping (Q-HTCP), which captures tens of thousands of growth curves simultaneously, provided powerful resolution to measure gene interaction on a phenomic scale, based on discrete cell proliferation parameters.ConclusionWe propose phenomic analysis of Yor1-ΔF as a model for investigating gene interaction networks that can modulate cystic fibrosis disease severity. Although the clinical relevance of the Yor1-ΔF gene interaction network for cystic fibrosis remains to be defined, the model appears to be informative with respect to human cell models of CFTR-ΔF. Moreover, the general strategy of yeast phenomics can be employed in a systematic manner to model gene interaction for other diseases relating to pathologies that result from protein misfolding or potentially any disease involving evolutionarily conserved genetic pathways.

Complex interactions among genes and environmental factors are known to play a role in common human disease aetiology. There is a growing body of evidence to suggest that complex interactions are 'the norm' and, rather than amounting to a small perturbation to classical Mendelian genetics, interactions may be the predominant effect. Traditional statistical methods are not well suited for detecting such interactions, especially when the data are high dimensional (many attributes or independent variables) or when interactions occur between more than two polymorphisms. In this review, we discuss machine-learning models and algorithms for identifying and characterising susceptibility genes in common, complex, multifactorial human diseases. We focus on the following machine-learning methods that have been used to detect gene-gene interactions: neural networks, cellular automata, random forests, and multifactor dimensionality reduction. We conclude with some ideas about how these methods and others can be integrated into a comprehensive and flexible framework for data mining and knowledge discovery in human genetics.

Evidence from human genetic studies of several disorders suggests that interactions between alleles at multiple genes play an important role in influencing phenotypic expression. Analytical methods for identifying Mendelian disease genes are not appropriate when applied to common multigenic diseases, because such methods investigate association with the phenotype only one genetic locus at a time. New strategies are needed that can capture the spectrum of genetic effects, from Mendelian to multifactorial epistasis. Random Forests (RF) and Relief-F are two powerful machine-learning methods that have been studied as filters for genetic case-control data due to their ability to account for the context of alleles at multiple genes when scoring the relevance of individual genetic variants to the phenotype. However, when variants interact strongly, the independence assumption of RF in the tree node-splitting criterion leads to diminished importance scores for relevant variants. Relief-F, on the other hand, was designed to detect strong interactions but is sensitive to large backgrounds of variants that are irrelevant to classification of the phenotype, which is an acute problem in genome-wide association studies. To overcome the weaknesses of these data mining approaches, we develop Evaporative Cooling (EC) feature selection, a flexible machine learning method that can integrate multiple importance scores while removing irrelevant genetic variants. To characterize detailed interactions, we construct a genetic-association interaction network (GAIN), whose edges quantify the synergy between variants with respect to the phenotype. We use simulation analysis to show that EC is able to identify a wide range of interaction effects in genetic association data. We apply the EC filter to a smallpox vaccine cohort study of single nucleotide polymorphisms (SNPs) and infer a GAIN for a collection of SNPs associated with adverse events. Our results suggest an important role for hubs in SNP disease susceptibility networks. The software is available at http://sites.google.com/site/McKinneyLab/software.

Vaccines, like drugs and medical procedures, are increasingly amenable to individualization or personalization, often based on novel data resulting from high throughput “omics” technologies. As a result of these technologies, 21st century vaccinology will increasingly see the abandonment of a “one size fits all” approach to vaccine dosing and delivery, as well as the abandonment of the empiric “isolate–inactivate–inject” paradigm for vaccine development. In this review, we discuss the immune response network theory and its application to the new field of vaccinomics and adversomics, and illustrate how vaccinomics can lead to new vaccine candidates, new understandings of how vaccines stimulate immune responses, new biomarkers for vaccine response, and facilitate the understanding of what genetic and other factors might be responsible for rare side effects due to vaccines. Perhaps most exciting will be the ability, at a systems biology level, to integrate increasingly complex high throughput data into descriptive and predictive equations for immune responses to vaccines. Herein, we discuss the above with a view toward the future of vaccinology.

Depressed patients show evidence of both proinflammatory changes and neurophysiological abnormalities such as increased amygdala reactivity and volumetric decreases of the hippocampus and ventromedial prefrontal cortex (vmPFC). However, very little is known about the relationship between inflammation and neuroimaging abnormalities in mood disorders. A whole genome expression analysis of peripheral blood mononuclear cells yielded 12 protein-coding genes (ADM, APBB3, CD160, CFD, CITED2, CTSZ, IER5, NFKBIZ, NR4A2, NUCKS1, SERTAD1, TNF) that were differentially expressed between 29 unmedicated depressed patients with a mood disorder (8 bipolar disorder, 21 major depressive disorder) and 24 healthy controls (HCs). Several of these genes have been implicated in neurological disorders and/or apoptosis. Ingenuity Pathway Analysis yielded two genes networks, one centered around TNF with NFKβ, TGFβ, and ERK as connecting hubs, and the second network indicating cell cycle and/or kinase signaling anomalies. fMRI scanning was conducted using a backward-masking task in which subjects were presented with emotionally-valenced faces. Compared with HCs, the depressed subjects displayed a greater hemodynamic response in the right amygdala, left hippocampus, and the ventromedial prefrontal cortex to masked sad versus happy faces. The mRNA levels of several genes were significantly correlated with the hemodynamic response of the amygdala, vmPFC and hippocampus to masked sad versus happy faces. Differentially-expressed transcripts were significantly correlated with thickness of the left subgenual ACC, and volume of the hippocampus and caudate. Our results raise the possibility that molecular-level immune dysfunction can be mapped onto macro-level neuroimaging abnormalities, potentially elucidating a mechanism by which inflammation leads to depression.

Vast diversity in the antibody repertoire is a key component of the adaptive immune response. This diversity is generated centrally through the assembly of variable, diversity and joining gene segments, and peripherally by somatic hypermutation and class-switch recombination. The peripheral diversification process is thought to only occur in response to antigenic stimulus, producing antigenselected memory B cells. Surprisingly, analyses of the variable, diversity and joining gene segments have revealed that the naïve and memory subsets are composed of similar proportions of these elements. Lacking, however, is a more detailed study, analyzing the repertoires of naïve and memory subsets at the level of the complete V(D)J recombinant. This report presents a thorough examination of V(D)J recombinants in the human peripheral blood repertoire, revealing surprisingly large repertoire differences between circulating B-cell subsets and providing genetic evidence for global control of repertoire diversity in naïve and memory circulating B-cell subsets.