Systematic analysis of NDUFAF6 in complex I assembly and mitochondrial disease.

Isolated complex I (CI) deficiencies are a major cause of primary mitochondrial disease. A substantial proportion of CI deficiencies are believed to arise from defects in CI assembly factors (CIAFs) that are not part of the CI holoenzyme. The biochemistry of these CIAFs is poorly defined, making their role in CI assembly unclear, and confounding interpretation of potential disease-causing genetic variants. To address these challenges, we devised a deep mutational scanning approach to systematically assess the function of thousands of NDUFAF6 genetic variants. Guided by these data, biochemical analyses and cross-linking mass spectrometry, we discovered that the CIAF NDUFAF6 facilitates incorporation of NDUFS8 into CI and reveal that NDUFS8 overexpression rectifies NDUFAF6 deficiency. Our data further provide experimental support of pathogenicity for seven novel NDUFAF6 variants associated with human pathology and introduce functional evidence for over 5,000 additional variants. Overall, our work defines the molecular function of NDUFAF6 and provides a clinical resource for aiding diagnosis of NDUFAF6-related diseases.

Coenzyme Q4 is a functional substitute for coenzyme Q10 and can be targeted to the mitochondria.

Coenzyme Q10 (CoQ10) is an important cofactor and antioxidant for numerous cellular processes, and its deficiency has been linked to human disorders including mitochondrial disease, heart failure, Parkinson's disease, and hypertension. Unfortunately, treatment with exogenous CoQ10 is often ineffective, likely due to its extreme hydrophobicity and high molecular weight. Here, we show that less hydrophobic CoQ species with shorter isoprenoid tails can serve as viable substitutes for CoQ10 in human cells. We demonstrate that CoQ4 can perform multiple functions of CoQ10 in CoQ-deficient cells at markedly lower treatment concentrations, motivating further investigation of CoQ4 as a supplement for CoQ10 deficiencies. In addition, we describe the synthesis and evaluation of an initial set of compounds designed to target CoQ4 selectively to mitochondria using triphenylphosphonium. Our results indicate that select versions of these compounds can successfully be delivered to mitochondria in a cell model and be cleaved to produce CoQ4, laying the groundwork for further development.

Coenzyme Q 4 is a functional substitute for coenzyme Q 10 and can be targeted to the mitochondria.

Coenzyme Q 10 (CoQ 10 ) is an important cofactor and antioxidant for numerous cellular processes, and its deficiency has been linked to human disorders including mitochondrial disease, heart failure, Parkinson's disease, and hypertension. Unfortunately, treatment with exogenous oral CoQ 10 is often ineffective, likely due to the extreme hydrophobicity and high molecular weight of CoQ 10 . Here, we show that less hydrophobic CoQ species with shorter isoprenoid tails can serve as viable substitutes for CoQ 10 in human cells. We demonstrate that CoQ 4 can perform multiple functions of CoQ 10 in CoQ-deficient cells at markedly lower treatment concentrations, motivating further investigation of CoQ 4 as a supplement for CoQ 10 deficiencies. In addition, we describe the synthesis and evaluation of an initial set of compounds designed to target CoQ 4 selectively to mitochondria using triphenylphosphonium (TPP). Our results indicate that select versions of these compounds can successfully be delivered to mitochondria in a cell model and be cleaved to produce CoQ 4 , laying the groundwork for further development.

Agreement between serum estimates of glomerular filtration rate (GFR) and a reference standard of radioisotopic GFR in children with cancer.

BACKGROUND: Accurate assessment of renal function is important in the care of children with cancer because renal function has implications for anti-tumor medication dosing and eligibility for clinical trials. OBJECTIVE: To characterize agreement between serum estimates of glomerular filtration rate (GFR) and a reference standard of radioisotopic GFR in a large pediatric oncology cohort. MATERIALS AND METHODS: We conducted a retrospective cross-sectional study of children who had both radioisotopic GFR (99mTc-diethylenetriaminepentaacetic acid, or 99mTc-DTPA) and serum labs (creatinine, cystatin C) obtained <7 days apart between January 2017 and August 2019. We calculated estimated GFR from serum labs using published equations and calculated agreement using intraclass correlation coefficient (ICC) and Bland-Altman analysis with univariate regression to define predictors of agreement. RESULTS: We included 272 pairs of data. Mean patient age was (mean ± standard deviation) 7.8±5.7 years. Mean radioisotopic GFR was 112±33 mL/min/1.73 m2. Absolute agreement between radioisotopic GFR and serum estimates was only fair (ICC=0.46-0.58) with a mean difference of -26.6 to +0.12 mL/min/1.73 m2. For radioisotopic GFR measurements <60 mL/min/1.73 m2, mean differences were greater, with serum estimates overestimating GFR by a mean of 21.5-39.6 mL/min/1.73 m2. In multivariable modeling, significant predictors of agreement included age, height, acute kidney injury and tumor type. Sensitivity of serum estimates was 14-29% for a GFR <60 mL/min/1.73 m2. CONCLUSION: Agreement between radioisotopic GFR and serum estimates of GFR is only fair and serum estimates of GFR have poor sensitivity for clinically relevant GFR <60 mL/min/1.73 m2. Radioisotopic measurement of GFR likely remains necessary to assess renal function in pediatric oncology patients with decreased renal function.

Prognostic significance of pretreatment 18F-FDG positron emission tomography/computed tomography in pediatric neuroblastoma.

BACKGROUND: 18F-2-fluoro-2-deoxyglucose (FDG) positron emission tomography (PET) shows tumor activity in most neuroblastomas, but the role of 18F-FDG PET/CT in neuroblastoma remains to be defined. OBJECTIVE: This study explored the prognostic significance of 18F-FDG PET in newly diagnosed neuroblastic tumors. MATERIALS AND METHODS: This retrospective study reviewed all 18F-FDG PET/CT examinations performed for a new diagnosis of suspected neuroblastoma. MYCN amplification status, tumor recurrence and survival were abstracted from the medical record. Primary tumors were manually segmented to measure maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), tumor volume and total lesion glycolysis. Univariate and multivariable analyses using Cox proportional hazards regression testing assessed the predictive performance of PET indices for event-free survival and overall survival with thresholds determined using receiver operating characteristic curve analysis. RESULTS: Fifty-five children were included, with a median age of 2.9 years (interquartile range [IQR] 1.8-3.0 years). SUVmax, tumor volume and total lesion glycolysis were higher in MYCN-amplified tumors (P=0.012, P<0.0001, P<0.0001, respectively) and in higher International Neuroblastoma Risk Group (INRG) stages (P=0.0008, P=0.0017, P=0.0017, respectively). After adjusting for age, tumor SUVmax (P=0.028) and SUVmean (P=0.045) were associated with overall survival. An SUVmax threshold of 4.77 (P=0.028) best predicted overall survival, with median overall survival of 2,604 days (SUVmax>4.77) vs. >2,957 days (SUVmax≤4.77). No PET parameters were independently significantly associated with overall survival or event-free survival after controlling for MYCN status, stage or treatment risk stratification. CONCLUSION: Tumor metabolic activity is higher in higher-stage MYCN-amplified neuroblastic tumors. Higher SUVmax and SUVmean were associated with worse overall survival but were not independent of other prognostic markers.

Pilot Study of an Attention and Executive Function Cognitive Intervention in Children with Autism Spectrum Disorders.

This pilot study investigated the efficacy of a game-based cognitive training program (Caribbean Quest; CQ) for improving attention and executive function (EF) in school-aged children with Autism Spectrum Disorder (ASD). CQ is a 'serious game' that uses a hybrid process-specific/compensatory approach to remediate attention and EF abilities through repetitive, hierarchically graded exercises delivered in an adaptive format. Game-play is accompanied by instruction in metacognitive strategies delivered by an adult trainer. Twenty children diagnosed with ASD (ages 6-12 years) completed 12 h of intervention in schools over 8-10 weeks that was facilitated by a trained Research Assistant. Pre-post testing indicated near transfer gains for visual working memory and selective attention and far transfer effects for math fluency. Exit interviews with parents and school staff indicated anecdotal gains in attention, EF, emotion-regulation, flexibility, communication, and social skills. Overall, this study provides preliminary support for the feasibility and potential efficacy of the CQ when delivered in schools to children with ASD.

Systems Biochemistry Approaches to Defining Mitochondrial Protein Function.

Defining functions for the full complement of proteins is a grand challenge in the post-genomic era and is essential for our understanding of basic biology and disease pathogenesis. In recent times, this endeavor has benefitted from a combination of modern large-scale and classical reductionist approaches-a process we refer to as "systems biochemistry"-that helps surmount traditional barriers to the characterization of poorly understood proteins. This strategy is proving to be particularly effective for mitochondria, whose well-defined proteome has enabled comprehensive analyses of the full mitochondrial system that can position understudied proteins for fruitful mechanistic investigations. Recent systems biochemistry approaches have accelerated the identification of new disease-related mitochondrial proteins and of long-sought "missing" proteins that fulfill key functions. Collectively, these studies are moving us toward a more complete understanding of mitochondrial activities and providing a molecular framework for the investigation of mitochondrial pathogenesis.

Automated CT registration tool improves sensitivity to change in ventricular volume in patients with shunts and drains.

OBJECTIVE: CT is the mainstay imaging modality for assessing change in ventricular volume in patients with ventricular shunts or external ventricular drains (EVDs). We evaluated the performance of a novel fully automated CT registration and subtraction method to improve reader accuracy and confidence compared with standard CT. METHODS: In a retrospective evaluation of 49 ventricular shunt or EVD patients who underwent sequential head CT scans with an automated CT registration tool (CT CoPilot), three readers were assessed on their ability to discern change in ventricular volume between scans using standard axial CT images versus reformats and subtraction images generated by the registration tool. The inter-rater reliability among the readers was calculated using an intraclass correlation coefficient (ICC). Bland-Altman tests were performed to determine reader performance compared to semi-quantitative assessment using the bifrontal horn and third ventricular width. McNemar's test was used to determine whether the use of the registration tool increased the reader's level of confidence. RESULTS: Inter-rater reliability was higher when using the output of the registration tool (single measure ICC of 0.909 with versus 0.755 without the tool). Agreement between the readers' assessment of ventricular volume change and the semi-quantitative assessment improved with the registration tool (limits of agreement 4.1 vs 4.3). Furthermore, the tool improved reader confidence in determining increased or decreased ventricular volume (p < 0.001). CONCLUSION: Automated CT registration and subtraction improves the reader's ability to detect change in ventricular volume between sequential scans in patients with ventricular shunts or EVDs. ADVANCES IN KNOWLEDGE: Our automated CT registration and subtraction method may serve as a promising generalizable tool for accurate assessment of change in ventricular volume, which can significantly affect clinical management.

Pathogenic Bi-allelic Mutations in NDUFAF8 Cause Leigh Syndrome with an Isolated Complex I Deficiency.

Leigh syndrome is one of the most common neurological phenotypes observed in pediatric mitochondrial disease presentations. It is characterized by symmetrical lesions found on neuroimaging in the basal ganglia, thalamus, and brainstem and by a loss of motor skills and delayed developmental milestones. Genetic diagnosis of Leigh syndrome is complicated on account of the vast genetic heterogeneity with >75 candidate disease-associated genes having been reported to date. Candidate genes are still emerging, being identified when "omics" tools (genomics, proteomics, and transcriptomics) are applied to manipulated cell lines and cohorts of clinically characterized individuals who lack a genetic diagnosis. NDUFAF8 is one such protein; it has been found to interact with the well-characterized complex I (CI) assembly factor NDUFAF5 in a large-scale protein-protein interaction screen. Diagnostic next-generation sequencing has identified three unrelated pediatric subjects, each with a clinical diagnosis of Leigh syndrome, who harbor bi-allelic pathogenic variants in NDUFAF8. These variants include a recurrent splicing variant that was initially overlooked due to its deep-intronic location. Subject fibroblasts were found to express a complex I deficiency, and lentiviral transduction with wild-type NDUFAF8-cDNA ameliorated both the assembly defect and the biochemical deficiency. Complexome profiling of subject fibroblasts demonstrated a complex I assembly defect, and the stalled assembly intermediates corroborate the role of NDUFAF8 in early complex I assembly. This report serves to expand the genetic heterogeneity associated with Leigh syndrome and to validate the clinical utility of orphan protein characterization. We also highlight the importance of evaluating intronic sequence when a single, definitively pathogenic variant is identified during diagnostic testing.

The "Eye Avoidance" Hypothesis of Autism Face Processing.

Although a growing body of research indicates that children with autism spectrum disorder (ASD) exhibit selective deficits in their ability to recognize facial identities and expressions, the source of their face impairment is, as yet, undetermined. In this paper, we consider three possible accounts of the autism face deficit: (1) the holistic hypothesis, (2) the local perceptual bias hypothesis and (3) the eye avoidance hypothesis. A review of the literature indicates that contrary to the holistic hypothesis, there is little evidence to suggest that individuals with autism do perceive faces holistically. The local perceptual bias account also fails to explain the selective advantage that ASD individuals demonstrate for objects and their selective disadvantage for faces. The eye avoidance hypothesis provides a plausible explanation of face recognition deficits where individuals with ASD avoid the eye region because it is perceived as socially threatening. Direct eye contact elicits a increased physiological response as indicated by heightened skin conductance and amygdala activity. For individuals with autism, avoiding the eyes is an adaptive strategy, however, this approach interferes with the ability to process facial cues of identity, expressions and intentions, exacerbating the social challenges for persons with ASD.

Carbofluoresceins and carborhodamines as scaffolds for high-contrast fluorogenic probes.

Fluorogenic molecules are important tools for advanced biochemical and biological experiments. The extant collection of fluorogenic probes is incomplete, however, leaving regions of the electromagnetic spectrum unutilized. Here, we synthesize green-excited fluorescent and fluorogenic analogues of the classic fluorescein and rhodamine 110 fluorophores by replacement of the xanthene oxygen with a quaternary carbon. These anthracenyl "carbofluorescein" and "carborhodamine 110" fluorophores exhibit excellent fluorescent properties and can be masked with enzyme- and photolabile groups to prepare high-contrast fluorogenic molecules useful for live cell imaging experiments and super-resolution microscopy. Our divergent approach to these red-shifted dye scaffolds will enable the preparation of numerous novel fluorogenic probes with high biological utility.

Gene selection and classification for cancer microarray data based on machine learning and similarity measures.

BACKGROUND: Microarray data have a high dimension of variables and a small sample size. In microarray data analyses, two important issues are how to choose genes, which provide reliable and good prediction for disease status, and how to determine the final gene set that is best for classification. Associations among genetic markers mean one can exploit information redundancy to potentially reduce classification cost in terms of time and money. RESULTS: To deal with redundant information and improve classification, we propose a gene selection method, Recursive Feature Addition, which combines supervised learning and statistical similarity measures. To determine the final optimal gene set for prediction and classification, we propose an algorithm, Lagging Prediction Peephole Optimization. By using six benchmark microarray gene expression data sets, we compared Recursive Feature Addition with recently developed gene selection methods: Support Vector Machine Recursive Feature Elimination, Leave-One-Out Calculation Sequential Forward Selection and several others. CONCLUSIONS: On average, with the use of popular learning machines including Nearest Mean Scaled Classifier, Support Vector Machine, Naive Bayes Classifier and Random Forest, Recursive Feature Addition outperformed other methods. Our studies also showed that Lagging Prediction Peephole Optimization is superior to random strategy; Recursive Feature Addition with Lagging Prediction Peephole Optimization obtained better testing accuracies than the gene selection method varSelRF.

Feature selection and classification of MAQC-II breast cancer and multiple myeloma microarray gene expression data.

Microarray data has a high dimension of variables but available datasets usually have only a small number of samples, thereby making the study of such datasets interesting and challenging. In the task of analyzing microarray data for the purpose of, e.g., predicting gene-disease association, feature selection is very important because it provides a way to handle the high dimensionality by exploiting information redundancy induced by associations among genetic markers. Judicious feature selection in microarray data analysis can result in significant reduction of cost while maintaining or improving the classification or prediction accuracy of learning machines that are employed to sort out the datasets. In this paper, we propose a gene selection method called Recursive Feature Addition (RFA), which combines supervised learning and statistical similarity measures. We compare our method with the following gene selection methods: Support Vector Machine Recursive Feature Elimination (SVMRFE), Leave-One-Out Calculation Sequential Forward Selection (LOOCSFS), Gradient based Leave-one-out Gene Selection (GLGS). To evaluate the performance of these gene selection methods, we employ several popular learning classifiers on the MicroArray Quality Control phase II on predictive modeling (MAQC-II) breast cancer dataset and the MAQC-II multiple myeloma dataset. Experimental results show that gene selection is strictly paired with learning classifier. Overall, our approach outperforms other compared methods. The biological functional analysis based on the MAQC-II breast cancer dataset convinced us to apply our method for phenotype prediction. Additionally, learning classifiers also play important roles in the classification of microarray data and our experimental results indicate that the Nearest Mean Scale Classifier (NMSC) is a good choice due to its prediction reliability and its stability across the three performance measurements: Testing accuracy, MCC values, and AUC errors.

High-throughput next-generation sequencing technologies foster new cutting-edge computing techniques in bioinformatics.

The advent of high-throughput next generation sequencing technologies have fostered enormous potential applications of supercomputing techniques in genome sequencing, epi-genetics, metagenomics, personalized medicine, discovery of non-coding RNAs and protein-binding sites. To this end, the 2008 International Conference on Bioinformatics and Computational Biology (Biocomp) - 2008 World Congress on Computer Science, Computer Engineering and Applied Computing (Worldcomp) was designed to promote synergistic inter/multidisciplinary research and education in response to the current research trends and advances. The conference attracted more than two thousand scientists, medical doctors, engineers, professors and students gathered at Las Vegas, Nevada, USA during July 14-17 and received great success. Supported by International Society of Intelligent Biological Medicine (ISIBM), International Journal of Computational Biology and Drug Design (IJCBDD), International Journal of Functional Informatics and Personalized Medicine (IJFIPM) and the leading research laboratories from Harvard, M.I.T., Purdue, UIUC, UCLA, Georgia Tech, UT Austin, U. of Minnesota, U. of Iowa etc, the conference received thousands of research papers. Each submitted paper was reviewed by at least three reviewers and accepted papers were required to satisfy reviewers' comments. Finally, the review board and the committee decided to select only 19 high-quality research papers for inclusion in this supplement to BMC Genomics based on the peer reviews only. The conference committee was very grateful for the Plenary Keynote Lectures given by: Dr. Brian D. Athey (University of Michigan Medical School), Dr. Vladimir N. Uversky (Indiana University School of Medicine), Dr. David A. Patterson (Member of United States National Academy of Sciences and National Academy of Engineering, University of California at Berkeley) and Anousheh Ansari (Prodea Systems, Space Ambassador). The theme of the conference to promote synergistic research and education has been achieved successfully.

Comparison of feature selection and classification for MALDI-MS data.

INTRODUCTION: In the classification of Mass Spectrometry (MS) proteomics data, peak detection, feature selection, and learning classifiers are critical to classification accuracy. To better understand which methods are more accurate when classifying data, some publicly available peak detection algorithms for Matrix assisted Laser Desorption Ionization Mass Spectrometry (MALDI-MS) data were recently compared; however, the issue of different feature selection methods and different classification models as they relate to classification performance has not been addressed. With the application of intelligent computing, much progress has been made in the development of feature selection methods and learning classifiers for the analysis of high-throughput biological data. The main objective of this paper is to compare the methods of feature selection and different learning classifiers when applied to MALDI-MS data and to provide a subsequent reference for the analysis of MS proteomics data. RESULTS: We compared a well-known method of feature selection, Support Vector Machine Recursive Feature Elimination (SVMRFE), and a recently developed method, Gradient based Leave-one-out Gene Selection (GLGS) that effectively performs microarray data analysis. We also compared several learning classifiers including K-Nearest Neighbor Classifier (KNNC), Naïve Bayes Classifier (NBC), Nearest Mean Scaled Classifier (NMSC), uncorrelated normal based quadratic Bayes Classifier recorded as UDC, Support Vector Machines, and a distance metric learning for Large Margin Nearest Neighbor classifier (LMNN) based on Mahanalobis distance. To compare, we conducted a comprehensive experimental study using three types of MALDI-MS data. CONCLUSION: Regarding feature selection, SVMRFE outperformed GLGS in classification. As for the learning classifiers, when classification models derived from the best training were compared, SVMs performed the best with respect to the expected testing accuracy. However, the distance metric learning LMNN outperformed SVMs and other classifiers on evaluating the best testing. In such cases, the optimum classification model based on LMNN is worth investigating for future study.

Association of common genetic variation in the insulin/IGF1 signaling pathway with human longevity.

The insulin/IGF1 signaling pathways affect lifespan in several model organisms, including worms, flies and mice. To investigate whether common genetic variation in this pathway influences lifespan in humans, we genotyped 291 common variants in 30 genes encoding proteins in the insulin/IGF1 signaling pathway in a cohort of elderly Caucasian women selected from the Study of Osteoporotic Fractures (SOF). The cohort included 293 long-lived cases (lifespan > or = 92 years (y), mean +/- standard deviation (SD) = 95.3 +/- 2.2y) and 603 average-lifespan controls (lifespan < or = 79y, mean = 75.7 +/- 2.6y). Variants were selected for genotyping using a haplotype-tagging approach. We found a modest excess of variants nominally associated with longevity. Nominally significant variants were then replicated in two additional Caucasian cohorts including both males and females: the Cardiovascular Health Study and Ashkenazi Jewish Centenarians. An intronic single nucleotide polymorphism in AKT1, rs3803304, was significantly associated with lifespan in a meta-analysis across the three cohorts (OR = 0.78 95%CI = 0.68-0.89, adjusted P = 0.043); two intronic single nucleotide polymorphisms in FOXO3A demonstrated a significant lifespan association among women only (rs1935949, OR = 1.35, 95%CI = 1.15-1.57, adjusted P = 0.0093). These results demonstrate that common variants in several genes in the insulin/IGF1 pathway are associated with human lifespan.

Supervised learning-based tagSNP selection for genome-wide disease classifications.

BACKGROUND: Comprehensive evaluation of common genetic variations through association of single nucleotide polymorphisms (SNPs) with complex human diseases on the genome-wide scale is an active area in human genome research. One of the fundamental questions in a SNP-disease association study is to find an optimal subset of SNPs with predicting power for disease status. To find that subset while reducing study burden in terms of time and costs, one can potentially reconcile information redundancy from associations between SNP markers. RESULTS: We have developed a feature selection method named Supervised Recursive Feature Addition (SRFA). This method combines supervised learning and statistical measures for the chosen candidate features/SNPs to reconcile the redundancy information and, in doing so, improve the classification performance in association studies. Additionally, we have proposed a Support Vector based Recursive Feature Addition (SVRFA) scheme in SNP-disease association analysis. CONCLUSIONS: We have proposed using SRFA with different statistical learning classifiers and SVRFA for both SNP selection and disease classification and then applying them to two complex disease data sets. In general, our approaches outperform the well-known feature selection method of Support Vector Machine Recursive Feature Elimination and logic regression-based SNP selection for disease classification in genetic association studies. Our study further indicates that both genetic and environmental variables should be taken into account when doing disease predictions and classifications for the most complex human diseases that have gene-environment interactions.