Phenotype-Agnostic Molecular Subtyping of Neurodegenerative Disorders: The Cincinnati Cohort Biomarker Program (CCBP).

Ongoing biomarker development programs have been designed to identify serologic or imaging signatures of clinico-pathologic entities, assuming distinct biological boundaries between them. Identified putative biomarkers have exhibited large variability and inconsistency between cohorts, and remain inadequate for selecting suitable recipients for potential disease-modifying interventions. We launched the Cincinnati Cohort Biomarker Program (CCBP) as a population-based, phenotype-agnostic longitudinal study. While patients affected by a wide range of neurodegenerative disorders will be deeply phenotyped using clinical, imaging, and mobile health technologies, analyses will not be anchored on phenotypic clusters but on bioassays of to-be-repurposed medications as well as on genomics, transcriptomics, proteomics, metabolomics, epigenomics, microbiomics, and pharmacogenomics analyses blinded to phenotypic data. Unique features of this cohort study include (1) a reverse biology-to-phenotype direction of biomarker development in which clinical, imaging, and mobile health technologies are subordinate to biological signals of interest; (2) hypothesis free, causally- and data driven-based analyses; (3) inclusive recruitment of patients with neurodegenerative disorders beyond clinical criteria-meeting patients with Parkinson's and Alzheimer's diseases, and (4) a large number of longitudinally followed participants. The parallel development of serum bioassays will be aimed at linking biologically suitable subjects to already available drugs with repurposing potential in future proof-of-concept adaptive clinical trials. Although many challenges are anticipated, including the unclear pathogenic relevance of identifiable biological signals and the possibility that some signals of importance may not yet be measurable with current technologies, this cohort study abandons the anchoring role of clinico-pathologic criteria in favor of biomarker-driven disease subtyping to facilitate future biosubtype-specific disease-modifying therapeutic efforts.

The anti-sigma factor MucA of Pseudomonas aeruginosa: Dramatic differences of a mucA22 vs. a ΔmucA mutant in anaerobic acidified nitrite sensitivity of planktonic and biofilm bacteria in vitro and during chronic murine lung infection.

Mucoid mucA22 Pseudomonas aeruginosa (PA) is an opportunistic lung pathogen of cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD) patients that is highly sensitive to acidified nitrite (A-NO2-). In this study, we first screened PA mutant strains for sensitivity or resistance to 20 mM A-NO2- under anaerobic conditions that represent the chronic stages of the aforementioned diseases. Mutants found to be sensitive to A-NO2- included PA0964 (pmpR, PQS biosynthesis), PA4455 (probable ABC transporter permease), katA (major catalase, KatA) and rhlR (quorum sensing regulator). In contrast, mutants lacking PA0450 (a putative phosphate transporter) and PA1505 (moaA2) were A-NO2- resistant. However, we were puzzled when we discovered that mucA22 mutant bacteria, a frequently isolated mucA allele in CF and to a lesser extent COPD, were more sensitive to A-NO2- than a truncated ΔmucA deletion (Δ157-194) mutant in planktonic and biofilm culture, as well as during a chronic murine lung infection. Subsequent transcriptional profiling of anaerobic, A-NO2--treated bacteria revealed restoration of near wild-type transcript levels of protective NO2- and nitric oxide (NO) reductase (nirS and norCB, respectively) in the ΔmucA mutant in contrast to extremely low levels in the A-NO2--sensitive mucA22 mutant. Proteins that were S-nitrosylated by NO derived from A-NO2- reduction in the sensitive mucA22 strain were those involved in anaerobic respiration (NirQ, NirS), pyruvate fermentation (UspK), global gene regulation (Vfr), the TCA cycle (succinate dehydrogenase, SdhB) and several double mutants were even more sensitive to A-NO2-. Bioinformatic-based data point to future studies designed to elucidate potential cellular binding partners for MucA and MucA22. Given that A-NO2- is a potentially viable treatment strategy to combat PA and other infections, this study offers novel developments as to how clinicians might better treat problematic PA infections in COPD and CF airway diseases.

BpV(pic) confers neuroprotection by inhibiting M1 microglial polarization and MCP-1 expression in rat traumatic brain injury.

Traumatic brain injury (TBI) is a major cause of motor and cognitive impairment in young adults. It is associated with high mortality rates and very few effective treatment options. Bisperoxovanadium (pyridine-2-carboxyl) [bpV(pic)] is an commercially available inhibitor of Phosphatase and tensin homolog (PTEN). Previous studies have shown that bpV(pic) has protective effects in central nervous system. However, the role of bpV(pic) in TBI is unclear. In this study we aimed to investigate the neuroprotective role of bpV(pic) in rat TBI model. We found that injection of bpV(pic) significantly reduces brain edema and neurological dysfunction after TBI and this is mediated by AKT pathway. TBI is known to promote the M1 pro-inflammatory phenotype of microglial polarization and this effect is inhibited by bpV(pic) treatment which, instead promotes M2 microglial polarization in vivo and in vitro. We also found evidence of bpV(pic)-regulated neuroinflammation mediated by AKT activation and NF-κB p65 inhibition. BpV(pic) treatment also suppressed microglia in the peri-TBI region. MCP-1 is known to recruit monocytes and macrophages to promote inflammation, we show that bpV(pic) can inhibit TBI-induced up-regulation of MCP-1 via the AKT/NF-κB p65 signaling pathway. Taken together, our findings demonstrate that bpV(pic) plays a neuroprotective role in rat TBI, which may be achieved by inhibiting M1 microglia polarization and MCP-1 expression by modulating AKT/NF-κB p65 signaling pathway.

Glycine Exhibits Neuroprotective Effects in Ischemic Stroke in Rats through the Inhibition of M1 Microglial Polarization via the NF-κB p65/Hif-1α Signaling Pathway.

Glycine is a simple nonessential amino acid known to have neuroprotective properties. Treatment with glycine results in reduced infarct volume of the brain, neurologic function scores, and neuronal and microglial death in ischemic stroke injury. Neuroinflammation has been considered a major contributor to cerebral ischemia-induced brain damage. However, the role of glycine in neuroinflammation following ischemic stroke is unclear. The present study aimed to determine whether neuroinflammation is involved in the neuroprotective effects of glycine in cerebral ischemia injury. Ischemic stroke promotes M1 microglial polarization. Interestingly, we found that the injection of glycine in rats after injury can inhibit ischemia-induced inflammation and promote M2 microglial polarization in vivo (Sprague-Dawley rats) and in vitro (cortical microglia and BV-2 cells). We show that glycine suppresses Hif-1α by inhibiting the upregulation of NF-κB p65 after ischemia-reperfusion injury, resulting in the inhibition of proinflammatory activity. The activation of AKT mediates the inhibition of NF-κB p65/Hif-1α signaling by glycine. Moreover, we confirm that glycine-regulated AKT activation is mediated by the inhibition of PTEN in a PTEN depletion cell line, U251 cells. Glycine modulates microglial polarization after ischemic stroke, which indirectly inhibits ischemia-induced neuronal death and functional recovery. Taken together, our findings provide a new understanding of glycine in neuroprotection by inhibiting M1 microglial polarization and promoting anti-inflammation by suppressing NF-κB p65/Hif-1α signaling.

Feasibility of a plasma bioassay to assess oxidative protection of low-density lipoproteins by high-density lipoproteins.

BACKGROUND: Traditionally, the impact of lipoproteins on vascular disease has been evaluated in light of their quantity, that is, cholesterol content, in plasma. However, recent studies of high-density lipoproteins (HDLs) have focused on functionality with regard to atheroprotection. For example, bioassays have emerged to assess the ability of HDL, in its near native plasma environment, to promote cholesterol removal (efflux) from cells. As a result, attention has focused on developing plasma-based assays for other putative HDL protective functions including protecting low-density lipoproteins (LDLs) from oxidative damage. OBJECTIVE: To determine the feasibility of such an assay in a complex sample such as plasma, we evaluated the contribution of HDL vs other plasma factors in preventing LDL oxidation. METHODS: We separated normolipidemic human plasma by gel filtration chromatography and assessed each fraction for its ability to prevent LDL modification by water soluble radical and copper-initiated oxidation mechanisms. RESULTS: Using proteomics and selective precipitation methods, we identified major antioxidative contributions for fibrinogen, immunoglobulin G, albumin, and small soluble molecules like uric acid and ascorbate, with albumin being especially dominant in copper-initiated mechanisms. HDL particles were minor contributors (∼1%-2%) to the antioxidant capacity of plasma, irrespective of oxidation mechanism. CONCLUSIONS: Given the overwhelming background of antioxidant capacity inherent to highly abundant plasma proteins, specific bioassays of HDL antioxidative function will likely require its complete separation from plasma.

fMRI as a Preimplant Objective Tool to Predict Children's Postimplant Auditory and Language Outcomes as Measured by Parental Observations.

BACKGROUND: The trends in cochlear implantation candidacy and benefit have changed rapidly in the last two decades. It is now widely accepted that early implantation leads to better postimplant outcomes. Although some generalizations can be made about postimplant auditory and language performance, neural mechanisms need to be studied to predict individual prognosis. PURPOSE: The aim of this study was to use functional magnetic resonance imaging (fMRI) to identify preimplant neuroimaging biomarkers that predict children's postimplant auditory and language outcomes as measured by parental observation/reports. RESEARCH DESIGN: This is a pre-post correlational measures study. STUDY SAMPLE: Twelve possible cochlear implant candidates with bilateral severe to profound hearing loss were recruited via referrals for a clinical magnetic resonance imaging to ensure structural integrity of the auditory nerve for implantation. INTERVENTION: Participants underwent cochlear implantation at a mean age of 19.4 mo. All children used the advanced combination encoder strategy (ACE, Cochlear Corporation™, Nucleus® Freedom cochlear implants). Three participants received an implant in the right ear; one in the left ear whereas eight participants received bilateral implants. Participants' preimplant neuronal activation in response to two auditory stimuli was studied using an event-related fMRI method. DATA COLLECTION AND ANALYSIS: Blood oxygen level dependent contrast maps were calculated for speech and noise stimuli. The general linear model was used to create z-maps. The Auditory Skills Checklist (ASC) and the SKI-HI Language Development Scale (SKI-HI LDS) were administered to the parents 2 yr after implantation. A nonparametric correlation analysis was implemented between preimplant fMRI activation and postimplant auditory and language outcomes based on ASC and SKI-HI LDS. Statistical Parametric Mapping software was used to create regression maps between fMRI activation and scores on the aforementioned tests. Regression maps were overlaid on the Imaging Research Center infant template and visualized in MRIcro. RESULTS: Regression maps revealed two clusters of brain activation for the speech versus silence contrast and five clusters for the noise versus silence contrast that were significantly correlated with the parental reports. These clusters included auditory and extra-auditory regions such as the middle temporal gyrus, supramarginal gyrus, precuneus, cingulate gyrus, middle frontal gyrus, subgyral, and middle occipital gyrus. Both positive and negative correlations were observed. Correlation values for the different clusters ranged from -0.90 to 0.95 and were significant at a corrected p value of <0.05. Correlations suggest that postimplant performance may be predicted by activation in specific brain regions. CONCLUSIONS: The results of the present study suggest that (1) fMRI can be used to identify neuroimaging biomarkers of auditory and language performance before implantation and (2) activation in certain brain regions may be predictive of postimplant auditory and language performance as measured by parental observation/reports.

Diagnosing Autism Spectrum Disorder from Brain Resting-State Functional Connectivity Patterns Using a Deep Neural Network with a Novel Feature Selection Method.

The whole-brain functional connectivity (FC) pattern obtained from resting-state functional magnetic resonance imaging data are commonly applied to study neuropsychiatric conditions such as autism spectrum disorder (ASD) by using different machine learning models. Recent studies indicate that both hyper- and hypo- aberrant ASD-associated FCs were widely distributed throughout the entire brain rather than only in some specific brain regions. Deep neural networks (DNN) with multiple hidden layers have shown the ability to systematically extract lower-to-higher level information from high dimensional data across a series of neural hidden layers, significantly improving classification accuracy for such data. In this study, a DNN with a novel feature selection method (DNN-FS) is developed for the high dimensional whole-brain resting-state FC pattern classification of ASD patients vs. typical development (TD) controls. The feature selection method is able to help the DNN generate low dimensional high-quality representations of the whole-brain FC patterns by selecting features with high discriminating power from multiple trained sparse auto-encoders. For the comparison, a DNN without the feature selection method (DNN-woFS) is developed, and both of them are tested with different architectures (i.e., with different numbers of hidden layers/nodes). Results show that the best classification accuracy of 86.36% is generated by the DNN-FS approach with 3 hidden layers and 150 hidden nodes (3/150). Remarkably, DNN-FS outperforms DNN-woFS for all architectures studied. The most significant accuracy improvement was 9.09% with the 3/150 architecture. The method also outperforms other feature selection methods, e.g., two sample t-test and elastic net. In addition to improving the classification accuracy, a Fisher's score-based biomarker identification method based on the DNN is also developed, and used to identify 32 FCs related to ASD. These FCs come from or cross different pre-defined brain networks including the default-mode, cingulo-opercular, frontal-parietal, and cerebellum. Thirteen of them are statically significant between ASD and TD groups (two sample t-test p < 0.05) while 19 of them are not. The relationship between the statically significant FCs and the corresponding ASD behavior symptoms is discussed based on the literature and clinician's expert knowledge. Meanwhile, the potential reason of obtaining 19 FCs which are not statistically significant is also provided.

A Computational Model for the Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Based on Functional Brain Volume.

In this paper, we investigated the problem of computer-aided diagnosis of Attention Deficit Hyperactivity Disorder (ADHD) using machine learning techniques. With the ADHD-200 dataset, we developed a Support Vector Machine (SVM) model to classify ADHD patients from typically developing controls (TDCs), using the regional brain volumes as predictors. Conventionally, the volume of a brain region was considered to be an anatomical feature and quantified using structural magnetic resonance images. One major contribution of the present study was that we had initially proposed to measure the regional brain volumes using fMRI images. Brain volumes measured from fMRI images were denoted as functional volumes, which quantified the volumes of brain regions that were actually functioning during fMRI imaging. We compared the predictive power of functional volumes with that of regional brain volumes measured from anatomical images, which were denoted as anatomical volumes. The former demonstrated higher discriminative power than the latter for the classification of ADHD patients vs. TDCs. Combined with our two-step feature selection approach which integrated prior knowledge with the recursive feature elimination (RFE) algorithm, our SVM classification model combining functional volumes and demographic characteristics achieved a balanced accuracy of 67.7%, which was 16.1% higher than that of a relevant model published previously in the work of Sato et al. Furthermore, our classifier highlighted 10 brain regions that were most discriminative in distinguishing between ADHD patients and TDCs. These 10 regions were mainly located in occipital lobe, cerebellum posterior lobe, parietal lobe, frontal lobe, and temporal lobe. Our present study using functional images will likely provide new perspectives about the brain regions affected by ADHD.

Mapping Atheroprotective Functions and Related Proteins/Lipoproteins in Size Fractionated Human Plasma.

HDL has been shown to possess a variety of cardio-protective functions, including removal of excess cholesterol from the periphery, and inhibition of lipoprotein oxidation. It has been proposed that various HDL subparticles exist, each with distinct protein and lipid compositions, which may be responsible for HDL's many functions. We hypothesized that HDL functions will co-migrate with the operational lipoprotein subspecies when separated by gel filtration chromatography. Plasma from 10 healthy male donors was fractionated and the protein composition of the phospholipid containing fractions was analyzed by mass spectrometry (MS). Each fraction was evaluated for its proteomic content as well as its ability to promote cholesterol efflux and protect low density lipoprotein (LDL) from free radical oxidation. For each function, several peaks of activity were identified across the plasma size gradient. Neither cholesterol efflux or LDL antioxidation activity correlated strongly with any single protein across the fractions. However, we identified multiple proteins that had strong correlations (r values >0.7, p < 0.01) with individual peaks of activity. These proteins fell into diverse functional categories, including those traditionally associated with lipid metabolism, as well as alternative complement cascade, innate immunity and clotting cascades and immunoglobulins. Additionally, the phospholipid and cholesterol concentration of the fractions correlated strongly with cholesterol efflux (r = 0.95 and 0.82 respectively), whereas the total protein content of the fractions correlated best with antioxidant activity across all fractions (r = 0.746). Furthermore, two previously postulated subspecies (apoA-I, apoA-II and apoC-1; as well as apoA-I, apoC-I and apoJ) were found to have strong correlations with both cholesterol efflux and antioxidation activity. Up till now, very little has been known about how lipoprotein composition mediates functions like cholesterol efflux and antioxidation.

fMRI as a Preimplant Objective Tool to Predict Postimplant Oral Language Outcomes in Children with Cochlear Implants.

OBJECTIVES: Despite the positive effects of cochlear implantation, postimplant variability in speech perception and oral language outcomes is still difficult to predict. The aim of this study was to identify neuroimaging biomarkers of postimplant speech perception and oral language performance in children with hearing loss who receive a cochlear implant. The authors hypothesized positive correlations between blood oxygen level-dependent functional magnetic resonance imaging (fMRI) activation in brain regions related to auditory language processing and attention and scores on the Clinical Evaluation of Language Fundamentals-Preschool, Second Edition (CELF-P2) and the Early Speech Perception Test for Profoundly Hearing-Impaired Children (ESP), in children with congenital hearing loss. DESIGN: Eleven children with congenital hearing loss were recruited for the present study based on referral for clinical MRI and other inclusion criteria. All participants were <24 months at fMRI scanning and <36 months at first implantation. A silent background fMRI acquisition method was performed to acquire fMRI during auditory stimulation. A voxel-based analysis technique was utilized to generate z maps showing significant contrast in brain activation between auditory stimulation conditions (spoken narratives and narrow band noise). CELF-P2 and ESP were administered 2 years after implantation. Because most participants reached a ceiling on ESP, a voxel-wise regression analysis was performed between preimplant fMRI activation and postimplant CELF-P2 scores alone. Age at implantation and preimplant hearing thresholds were controlled in this regression analysis. RESULTS: Four brain regions were found to be significantly correlated with CELF-P2 scores. These clusters of positive correlation encompassed the temporo-parieto-occipital junction, areas in the prefrontal cortex and the cingulate gyrus. For the story versus silence contrast, CELF-P2 core language score demonstrated significant positive correlation with activation in the right angular gyrus (r = 0.95), left medial frontal gyrus (r = 0.94), and left cingulate gyrus (r = 0.96). For the narrow band noise versus silence contrast, the CELF-P2 core language score exhibited significant positive correlation with activation in the left angular gyrus (r = 0.89; for all clusters, corrected p < 0.05). CONCLUSIONS: Four brain regions related to language function and attention were identified that correlated with CELF-P2. Children with better oral language performance postimplant displayed greater activation in these regions preimplant. The results suggest that despite auditory deprivation, these regions are more receptive to gains in oral language development performance of children with hearing loss who receive early intervention via cochlear implantation. The present study suggests that oral language outcome following cochlear implant may be predicted by preimplant fMRI with auditory stimulation using natural speech.

A semi-supervised Support Vector Machine model for predicting the language outcomes following cochlear implantation based on pre-implant brain fMRI imaging.

INTRODUCTION: We developed a machine learning model to predict whether or not a cochlear implant (CI) candidate will develop effective language skills within 2 years after the CI surgery by using the pre-implant brain fMRI data from the candidate. METHODS: The language performance was measured 2 years after the CI surgery by the Clinical Evaluation of Language Fundamentals-Preschool, Second Edition (CELF-P2). Based on the CELF-P2 scores, the CI recipients were designated as either effective or ineffective CI users. For feature extraction from the fMRI data, we constructed contrast maps using the general linear model, and then utilized the Bag-of-Words (BoW) approach that we previously published to convert the contrast maps into feature vectors. We trained both supervised models and semi-supervised models to classify CI users as effective or ineffective. RESULTS: Compared with the conventional feature extraction approach, which used each single voxel as a feature, our BoW approach gave rise to much better performance for the classification of effective versus ineffective CI users. The semi-supervised model with the feature set extracted by the BoW approach from the contrast of speech versus silence achieved a leave-one-out cross-validation AUC as high as 0.97. Recursive feature elimination unexpectedly revealed that two features were sufficient to provide highly accurate classification of effective versus ineffective CI users based on our current dataset. CONCLUSION: We have validated the hypothesis that pre-implant cortical activation patterns revealed by fMRI during infancy correlate with language performance 2 years after cochlear implantation. The two brain regions highlighted by our classifier are potential biomarkers for the prediction of CI outcomes. Our study also demonstrated the superiority of the semi-supervised model over the supervised model. It is always worthwhile to try a semi-supervised model when unlabeled data are available.

Detecting brain structural changes as biomarker from magnetic resonance images using a local feature based SVM approach.

Detecting brain structural changes from magnetic resonance (MR) images can facilitate early diagnosis and treatment of neurological and psychiatric diseases. Many existing methods require an accurate deformation registration, which is difficult to achieve and therefore prevents them from obtaining high accuracy. We develop a novel local feature based support vector machine (SVM) approach to detect brain structural changes as potential biomarkers. This approach does not require deformation registration and thus is less influenced by artifacts such as image distortion. We represent the anatomical structures based on scale invariant feature transform (SIFT). Likelihood scores calculated using feature-based morphometry is used as the criterion to categorize image features into three classes (healthy, patient and noise). Regional SVMs are trained to classify the three types of image features in different brain regions. Only healthy and patient features are used to predict the disease status of new brain images. An ensemble classifier is built from the regional SVMs to obtain better prediction accuracy. We apply this approach to 3D MR images of Alzheimer's disease, Parkinson's disease and bipolar disorder. The classification accuracy ranges between 70% and 87%. The highly predictive disease-related regions, which represent significant anatomical differences between the healthy and diseased, are shown in heat maps. The common and disease-specific brain regions are identified by comparing the highly predictive regions in each disease. All of the top-ranked regions are supported by literature. Thus, this approach will be a promising tool for assisting automatic diagnosis and advancing mechanism studies of neurological and psychiatric diseases.

Combined analysis of sMRI and fMRI imaging data provides accurate disease markers for hearing impairment.

In this research, we developed a robust two-layer classifier that can accurately classify normal hearing (NH) from hearing impaired (HI) infants with congenital sensori-neural hearing loss (SNHL) based on their Magnetic Resonance (MR) images. Unlike traditional methods that examine the intensity of each single voxel, we extracted high-level features to characterize the structural MR images (sMRI) and functional MR images (fMRI). The Scale Invariant Feature Transform (SIFT) algorithm was employed to detect and describe the local features in sMRI. For fMRI, we constructed contrast maps and detected the most activated/de-activated regions in each individual. Based on those salient regions occurring across individuals, the bag-of-words strategy was introduced to vectorize the contrast maps. We then used a two-layer model to integrate these two types of features together. With the leave-one-out cross-validation approach, this integrated model achieved an AUC score of 0.90. Additionally, our algorithm highlighted several important brain regions that differentiated between NH and HI children. Some of these regions, e.g. planum temporale and angular gyrus, were well known auditory and visual language association regions. Others, e.g. the anterior cingulate cortex (ACC), were not necessarily expected to play a role in differentiating HI from NH children and provided a new understanding of brain function and of the disorder itself. These important brain regions provided clues about neuroimaging markers that may be relevant to the future use of functional neuroimaging to guide predictions about speech and language outcomes in HI infants who receive a cochlear implant. This type of prognostic information could be extremely useful and is currently not available to clinicians by any other means.

Prediction and analysis of the protein interactome in Pseudomonas aeruginosa to enable network-based drug target selection.

Pseudomonas aeruginosa (PA) is a ubiquitous opportunistic pathogen that is capable of causing highly problematic, chronic infections in cystic fibrosis and chronic obstructive pulmonary disease patients. With the increased prevalence of multi-drug resistant PA, the conventional "one gene, one drug, one disease" paradigm is losing effectiveness. Network pharmacology, on the other hand, may hold the promise of discovering new drug targets to treat a variety of PA infections. However, given the urgent need for novel drug target discovery, a PA protein-protein interaction (PPI) network of high accuracy and coverage, has not yet been constructed. In this study, we predicted a genome-scale PPI network of PA by integrating various genomic features of PA proteins/genes by a machine learning-based approach. A total of 54,107 interactions covering 4,181 proteins in PA were predicted. A high-confidence network combining predicted high-confidence interactions, a reference set and verified interactions that consist of 3,343 proteins and 19,416 potential interactions was further assembled and analyzed. The predicted interactome network from this study is the first large-scale PPI network in PA with significant coverage and high accuracy. Subsequent analysis, including validations based on existing small-scale PPI data and the network structure comparison with other model organisms, shows the validity of the predicted PPI network. Potential drug targets were identified and prioritized based on their essentiality and topological importance in the high-confidence network. Host-pathogen protein interactions between human and PA were further extracted and analyzed. In addition, case studies were performed on protein interactions regarding anti-sigma factor MucA, negative periplasmic alginate regulator MucB, and the transcriptional regulator RhlR. A web server to access the predicted PPI dataset is available at http://research.cchmc.org/PPIdatabase/.

The orphan disease networks.

The low prevalence rate of orphan diseases (OD) requires special combined efforts to improve diagnosis, prevention, and discovery of novel therapeutic strategies. To identify and investigate relationships based on shared genes or shared functional features, we have conducted a bioinformatic-based global analysis of all orphan diseases with known disease-causing mutant genes. Starting with a bipartite network of known OD and OD-causing mutant genes and using the human protein interactome, we first construct and topologically analyze three networks: the orphan disease network, the orphan disease-causing mutant gene network, and the orphan disease-causing mutant gene interactome. Our results demonstrate that in contrast to the common disease-causing mutant genes that are predominantly nonessential, a majority of orphan disease-causing mutant genes are essential. In confirmation of this finding, we found that OD-causing mutant genes are topologically important in the protein interactome and are ubiquitously expressed. Additionally, functional enrichment analysis of those genes in which mutations cause ODs shows that a majority result in premature death or are lethal in the orthologous mouse gene knockout models. To address the limitations of traditional gene-based disease networks, we also construct and analyze OD networks on the basis of shared enriched features (biological processes, cellular components, pathways, phenotypes, and literature citations). Analyzing these functionally-linked OD networks, we identified several additional OD-OD relations that are both phenotypically similar and phenotypically diverse. Surprisingly, we observed that the wiring of the gene-based and other feature-based OD networks are largely different; this suggests that the relationship between ODs cannot be fully captured by the gene-based network alone.

A Drosophila model of the neurodegenerative disease SCA17 reveals a role of RBP-J/Su(H) in modulating the pathological outcome.

Expanded polyglutamine (polyQ) tract in the human TATA-box-binding protein (hTBP) causes the neurodegenerative disease spinocerebellar ataxia 17 (SCA17). To investigate the pathological effects of polyQ expansion, we established a SCA17 model in Drosophila. Similar to SCA17 patients, transgenic flies expressing a mutant hTBP protein with an expanded polyQ tract (hTBP80Q) exhibit progressive neurodegeneration, late-onset locomotor impairment and shortened lifespan. Microarray analysis reveals that hTBP80Q causes widespread and time-dependent transcriptional dysregulation in Drosophila. In a candidate screen for genetic modifiers, we identified RBP-J/Su(H), a transcription factor that contains Q/N-rich domains and participates in Notch signaling. Knockdown of Su(H) by RNAi further enhances hTBP80Q-induced eye defects, whereas overexpression of Su(H) suppresses such defects. While the Su(H) transcript level is not significantly altered in hTBP80Q-expressing flies, genes that contain Su(H)-binding sites are among those that are dysregulated. We further show that hTBP80Q interacts more efficiently with Su(H) than wild-type hTBP, suggesting that a reduction in the fraction of Su(H) available for its normal cellular functions contributes to hTBP80Q-induced phenotypes. While the Notch signaling pathway has been implicated in several neurological disorders, our study suggests a possibility that the activity of its nuclear component RBP-J/Su(H) may modulate the pathological progression in SCA17 patients.

Investigating the predictability of essential genes across distantly related organisms using an integrative approach.

Rapid and accurate identification of new essential genes in under-studied microorganisms will significantly improve our understanding of how a cell works and the ability to re-engineer microorganisms. However, predicting essential genes across distantly related organisms remains a challenge. Here, we present a machine learning-based integrative approach that reliably transfers essential gene annotations between distantly related bacteria. We focused on four bacterial species that have well-characterized essential genes, and tested the transferability between three pairs among them. For each pair, we trained our classifier to learn traits associated with essential genes in one organism, and applied it to make predictions in the other. The predictions were then evaluated by examining the agreements with the known essential genes in the target organism. Ten-fold cross-validation in the same organism yielded AUC scores between 0.86 and 0.93. Cross-organism predictions yielded AUC scores between 0.69 and 0.89. The transferability is likely affected by growth conditions, quality of the training data set and the evolutionary distance. We are thus the first to report that gene essentiality can be reliably predicted using features trained and tested in a distantly related organism. Our approach proves more robust and portable than existing approaches, significantly extending our ability to predict essential genes beyond orthologs.

Exploring the optimal strategy to predict essential genes in microbes.

Accurately predicting essential genes is important in many aspects of biology, medicine and bioengineering. In previous research, we have developed a machine learning based integrative algorithm to predict essential genes in bacterial species. This algorithm lends itself to two approaches for predicting essential genes: learning the traits from known essential genes in the target organism, or transferring essential gene annotations from a closely related model organism. However, for an understudied microbe, each approach has its potential limitations. The first is constricted by the often small number of known essential genes. The second is limited by the availability of model organisms and by evolutionary distance. In this study, we aim to determine the optimal strategy for predicting essential genes by examining four microbes with well-characterized essential genes. Our results suggest that, unless the known essential genes are few, learning from the known essential genes in the target organism usually outperforms transferring essential gene annotations from a related model organism. In fact, the required number of known essential genes is surprisingly small to make accurate predictions. In prokaryotes, when the number of known essential genes is greater than 2% of total genes, this approach already comes close to its optimal performance. In eukaryotes, achieving the same best performance requires over 4% of total genes, reflecting the increased complexity of eukaryotic organisms. Combining the two approaches resulted in an increased performance when the known essential genes are few. Our investigation thus provides key information on accurately predicting essential genes and will greatly facilitate annotations of microbial genomes.

Investigating the validity of current network analysis on static conglomerate networks by protein network stratification.

BACKGROUND: A molecular network perspective forms the foundation of systems biology. A common practice in analyzing protein-protein interaction (PPI) networks is to perform network analysis on a conglomerate network that is an assembly of all available binary interactions in a given organism from diverse data sources. Recent studies on network dynamics suggested that this approach might have ignored the dynamic nature of context-dependent molecular systems. RESULTS: In this study, we employed a network stratification strategy to investigate the validity of the current network analysis on conglomerate PPI networks. Using the genome-scale tissue- and condition-specific proteomics data in Arabidopsis thaliana, we present here the first systematic investigation into this question. We stratified a conglomerate A. thaliana PPI network into three levels of context-dependent subnetworks. We then focused on three types of most commonly conducted network analyses, i.e., topological, functional and modular analyses, and compared the results from these network analyses on the conglomerate network and five stratified context-dependent subnetworks corresponding to specific tissues. CONCLUSIONS: We found that the results based on the conglomerate PPI network are often significantly different from those of context-dependent subnetworks corresponding to specific tissues or conditions. This conclusion depends neither on relatively arbitrary cutoffs (such as those defining network hubs or bottlenecks), nor on specific network clustering algorithms for module extraction, nor on the possible high false positive rates of binary interactions in PPI networks. We also found that our conclusions are likely to be valid in human PPI networks. Furthermore, network stratification may help resolve many controversies in current research of systems biology.

Distance measurements via the morphogen gradient of Bicoid in Drosophila embryos.

BACKGROUND: Patterning along the anterior-posterior (A-P) axis in Drosophila embryos is instructed by the morphogen gradient of Bicoid (Bcd). Despite extensive studies of this morphogen, how embryo geometry may affect gradient formation and target responses has not been investigated experimentally. RESULTS: In this report, we systematically compare the Bcd gradient profiles and its target expression patterns on the dorsal and ventral sides of the embryo. Our results support a hypothesis that proper distance measurement and the encoded positional information of the Bcd gradient are along the perimeter of the embryo. Our results also reveal that the dorsal and ventral sides of the embryo have a fundamentally similar relationship between Bcd and its target Hunchback (Hb), suggesting that Hb expression properties on the two sides of the embryo can be directly traced to Bcd gradient properties. Our 3-D simulation studies show that a curvature difference between the two sides of an embryo is sufficient to generate Bcd gradient properties that are consistent with experimental observations. CONCLUSIONS: The findings described in this report provide a first quantitative, experimental evaluation of embryo geometry on Bcd gradient formation and target responses. They demonstrate that the physical features of an embryo, such as its shape, are integral to how pattern is formed.