Influence of autozygosity on common disease risk across the phenotypic spectrum.

Autozygosity is associated with rare Mendelian disorders and clinically relevant quantitative traits. We investigated associations between the fraction of the genome in runs of homozygosity (FROH) and common diseases in Genes & Health (n = 23,978 British South Asians), UK Biobank (n = 397,184), and 23andMe. We show that restricting analysis to offspring of first cousins is an effective way of reducing confounding due to social/environmental correlates of FROH. Within this group in G&H+UK Biobank, we found experiment-wide significant associations between FROH and twelve common diseases. We replicated associations with type 2 diabetes (T2D) and post-traumatic stress disorder via within-sibling analysis in 23andMe (median n = 480,282). We estimated that autozygosity due to consanguinity accounts for 5%-18% of T2D cases among British Pakistanis. Our work highlights the possibility of widespread non-additive genetic effects on common diseases and has important implications for global populations with high rates of consanguinity.

S-leaping: an efficient downsampling method for large high-throughput sequencing data.

MOTIVATION: Sequencing coverage is among key determinants considered in the design of omics studies. To help estimate cost-effective sequencing coverage for specific downstream analysis, downsampling, a technique to sample subsets of reads with a specific size, is routinely used. However, as the size of sequencing becomes larger and larger, downsampling becomes computationally challenging. RESULTS: Here, we developed an approximate downsampling method called s-leaping that was designed to efficiently and accurately process large-size data. We compared the performance of s-leaping with state-of-the-art downsampling methods in a range of practical omics-study downsampling settings and found s-leaping to be up to 39% faster than the second-fastest method, with comparable accuracy to the exact downsampling methods. To apply s-leaping on FASTQ data, we developed a light-weight tool called fadso in C. Using whole-genome sequencing data with 208 million reads, we compared fadso's performance with that of a commonly used FASTQ tool with the same downsampling feature and found fadso to be up to 12% faster with 21% lower memory usage, suggesting fadso to have up to 40% higher throughput in a parallel computing setting. AVAILABILITY AND IMPLEMENTATION: The C source code for s-leaping, as well as the fadso package is freely available at https://github.com/hkuwahara/sleaping.

The potential for egg-guarding care in females of the damselfish, Dascyllus reticulatus, in the absence of uniparental male care.

Male damselfish typically demonstrate uniparental egg-guarding care in nature. Potential plasticity in sexual behavior has recently been reported in various teleost fish. To examine behavioral plasticity in parental care, we conducted aquarium experiments to explore the potential for egg-guarding care in the female damselfish, Dascyllus reticulatus. After initial caretaking, males were removed from the mating nests, and cohabiting females frequently exhibited egg predation on the same day. However, we confirmed that females showed significantly decreased egg-predation frequencies on the following day and showed egg-caring behaviors. All experimental females guarded their eggs until they hatched. Females subsequently spawned eggs as females even after performing parental care behaviors, indicating no progression of sex change into males. Molecular analysis of select pituitary gland hormones indicated that egg-caring females and males showed high expression levels of prolactin, suggesting its involvement in the development of parental care behaviors. The cryptic possession of caretaking ability in females may be a tactical response to the need for temporary replacement of the care roles in cases where caretaking males are removed, for example, through predation, in damselfish species living in sexually cohabiting groups.

Recessive, Deleterious Variants in SMG8 Expand the Role of Nonsense-Mediated Decay in Developmental Disorders in Humans.

We have previously described a heart-, eye-, and brain-malformation syndrome caused by homozygous loss-of-function variants in SMG9, which encodes a critical component of the nonsense-mediated decay (NMD) machinery. Here, we describe four consanguineous families with four different likely deleterious homozygous variants in SMG8, encoding a binding partner of SMG9. The observed phenotype greatly resembles that linked to SMG9 and comprises severe global developmental delay, microcephaly, facial dysmorphism, and variable congenital heart and eye malformations. RNA-seq analysis revealed a general increase in mRNA expression levels with significant overrepresentation of core NMD substrates. We also identified increased phosphorylation of UPF1, a key SMG1-dependent step in NMD, which most likely represents the loss of SMG8--mediated inhibition of SMG1 kinase activity. Our data show that SMG8 and SMG9 deficiency results in overlapping developmental disorders that most likely converge mechanistically on impaired NMD.

Deep learning identifies and quantifies recombination hotspot determinants.

MOTIVATION: Recombination is one of the essential genetic processes for sexually reproducing organisms, which can happen more frequently in some regions, called recombination hotspots. Although several factors, such as PRDM9 binding motifs, are known to be related to the hotspots, their contributions to the recombination hotspots have not been quantified, and other determinants are yet to be elucidated. Here, we propose a computational method, RHSNet, based on deep learning and signal processing, to identify and quantify the hotspot determinants in a purely data-driven manner, utilizing datasets from various studies, populations, sexes and species. RESULTS: RHSNet can significantly outperform other sequence-based methods on multiple datasets across different species, sexes and studies. In addition to being able to identify hotspot regions and the well-known determinants accurately, more importantly, RHSNet can quantify the determinants that contribute significantly to the recombination hotspot formation in the relation between PRDM9 binding motif, histone modification and GC content. Further cross-sex, cross-population and cross-species studies suggest that the proposed method has the generalization power and potential to identify and quantify the evolutionary determinant motifs. AVAILABILITY AND IMPLEMENTATION: https://github.com/frankchen121212/RHSNet. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Promoter analysis and prediction in the human genome using sequence-based deep learning models.

MOTIVATION: Computational identification of promoters is notoriously difficult as human genes often have unique promoter sequences that provide regulation of transcription and interaction with transcription initiation complex. While there are many attempts to develop computational promoter identification methods, we have no reliable tool to analyze long genomic sequences. RESULTS: In this work, we further develop our deep learning approach that was relatively successful to discriminate short promoter and non-promoter sequences. Instead of focusing on the classification accuracy, in this work we predict the exact positions of the transcription start site inside the genomic sequences testing every possible location. We studied human promoters to find effective regions for discrimination and built corresponding deep learning models. These models use adaptively constructed negative set, which iteratively improves the model's discriminative ability. Our method significantly outperforms the previously developed promoter prediction programs by considerably reducing the number of false-positive predictions. We have achieved error-per-1000-bp rate of 0.02 and have 0.31 errors per correct prediction, which is significantly better than the results of other human promoter predictors. AVAILABILITY AND IMPLEMENTATION: The developed method is available as a web server at http://www.cbrc.kaust.edu.sa/PromID/.

Systematic selection of chemical fingerprint features improves the Gibbs energy prediction of biochemical reactions.

MOTIVATION: Accurate and wide-ranging prediction of thermodynamic parameters for biochemical reactions can facilitate deeper insights into the workings and the design of metabolic systems. RESULTS: Here, we introduce a machine learning method with chemical fingerprint-based features for the prediction of the Gibbs free energy of biochemical reactions. From a large pool of 2D fingerprint-based features, this method systematically selects a small number of relevant ones and uses them to construct a regularized linear model. Since a manual selection of 2D structure-based features can be a tedious and time-consuming task, requiring expert knowledge about the structure-activity relationship of chemical compounds, the systematic feature selection step in our method offers a convenient means to identify relevant 2D fingerprint-based features. By comparing our method with state-of-the-art linear regression-based methods for the standard Gibbs free energy prediction, we demonstrated that its prediction accuracy and prediction coverage are most favorable. Our results show direct evidence that a number of 2D fingerprints collectively provide useful information about the Gibbs free energy of biochemical reactions and that our systematic feature selection procedure provides a convenient way to identify them. AVAILABILITY AND IMPLEMENTATION: Our software is freely available for download at http://sfb.kaust.edu.sa/Pages/Software.aspx. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

B-type Natriuretic Peptide Regulation in Patients with Severe Aortic Stenosis Following Transaortic Valvular Implantation.

Peri-procedural elevated B-type natriuretic peptide (BNP) is also associated with worse outcomes following transcatheter aortic valve implantation (TAVI). However, the mechanism of BNP regulation in patients with severe aortic stenosis (AS) remains unknown. Consecutive patients with severe AS who were referred for TAVI were enrolled in our prospective registry. BNP levels were correlated with other clinical variables. Ninety-six patients (84.7 ± 5.0 years old, 34% males) were investigated in this study. Plasma BNP averaged 353 ± 179 pg/mL. Log10 BNP had no significant correlation with severity of AS including aortic valve area and maximum flow velocity across the aortic valve (P > 0.05 for all), whereas a higher left ventricular end-diastolic dimension (LVDd) index was a significant factor associating with BNP >100 pg/mL with an odds ratio of 1.34 (95% confidence interval 1.06-1.52, P = 0.004) adjusted for several other echocardiographic parameters, with a cutoff of 30.8 mm/m2 (equivalent to LVDd 44 mm). In conclusion, among the patients with severe AS who undergo TAVI, even slight eccentric hypertrophy can cause a considerable increase in BNP level.

Paracorporeal Ventricular Assist Device Implantation in a Patient with Transposition of the Great Arteries After Senning Operation.

We experienced a 33-year-old patient with D-looped transposition of the great arteries (D-TGA) and a history of Senning operation who was referred to our institute with cardiogenic shock and subsequently underwent urgent paracorporeal ventricular assist device (VAD) implantation, which was a first in Japan, that was eventually converted to a durable VAD. Central venous pressure was maintained relatively high to obtain VAD filling and recover end-organ dysfunction, given the migration of the inflow cannula due to rich trabeculae carneae of the anatomical right ventricle (systemic ventricle in this case).

Regulation and Clinical Implication of Arginine Vasopressin in Patients with Severe Aortic Stenosis Referred to Trans-Catheter Aortic Valve Implantation.

Background and objectives: Plasma arginine vasopressin (P-AVP) is regulated by the non-osmotic pathway in patients with heart failure (HF) and reduced ejection fraction. However, the regulation of P-AVP in patients with severe aortic stenosis (AS) remains unknown. Materials and Methods: Consecutive patients with severe AS who received trans-catheter aortic valve implantation (TAVI) between Apr 2016 and Apr 2019 were enrolled in this prospective study. Clinical data including P-AVP were obtained just before TAVI, and the correlation between P-AVP and other variables was investigated. Results: In total, 159 patients with severe AS (85.3 ± 4.6 years, male 26%) were enrolled. P-AVP was 1.45 ± 1.13 ng/mL and cardiac index was relatively preserved (2.76 ± 0.54 L/min/m2). There was no significant correlation between cardiac index and P-AVP (p > 0.05), whereas plasma osmolality had a moderate positive correlation with P-AVP (r = 0.35, p < 0.01), predominantly due to blood urea nitrogen (r = 0.27, p < 0.01). Patients with diuretics had significantly higher P-AVP than those without diuretics (1.65 ± 1.43 vs. 1.22 ± 0.57 pg/mL, p < 0.01). Two-year survivals free from HF readmission were statistically comparable irrespective of the level of pre-procedural P-AVP (p = 0.44). Conclusion: In patients with severe high-gradient AS who received TAVI, the P-AVP level was dominantly regulated by plasma osmolality instead of arterial underfilling. The clinical implication of elevated P-AVP in the TAVI candidates is the next concern.

A case of cardiogenic shock due to acute coronary syndrome successfully recovered by percutaneous and paracorporeal left ventricular assist device.

We recently experienced a 70-year-old woman with left main trunk-acute coronary syndrome who was initially supported by Impella 5.0 which converted to paracorporeal left ventricular assist device (LVAD) implantation as a bridge to recovery. Optimized guideline-directed medical therapy with cardiac rehabilitation resulted in successful explantation of LVAD and she discharged on foot.

Clinical Advantages of Using Low Tube Voltage in Third-Generation 192-Slice Dual-Source Computed Tomographic Angiography Before Transcatheter Aortic Valve Implantation.

Low-voltage computed tomographic angiography (CTA) is a highly effective technique to reduce contrast media volume. We sought to examine the suitability of low tube voltage CTA with a reduced contrast media volume protocol using third-generation 192-slice dual-source CT in patients undergoing transcatheter aortic valve implantation (TAVI). CTA was performed to aid TAVI planning for 40 consecutive patients with severe aortic stenosis. For the first 10 patients (120/100 kV group), we used a conventional tube voltage combined CTA protocol (an ECG-gated helical scan; 120 kV, non-gated helical scan; 100 kV). For the subsequent 30 patients (70-kV group), we adopted a low tube voltage CTA protocol. We evaluated vascular attenuation, image noise, contrast-to-noise ratio (CNR), and renal function. The mean contrast media (CM) volume was 77.7 ± 17.7 mL in the 120/100-kV group and 30.9 ± 6.3 mL in the 70-kV group (P < 0.001). In the images of the aortic valve complex, the mean attenuation was not significant difference for both groups. In the images of the aorto-femoral arteries, mean attenuation was > 250 Hounsfield Units and CNR was > 10 in all vascular segments for both groups. There was no significant difference in the change of renal function in the 70-kV group, but renal function in the 120/100-kV group decreased within 1-3 months after CTA. Low tube voltage CTA using third-generation dual-source CT is suitable to assess procedural planning for TAVI. This approach maintains image quality and reduces the required CM volume.

Gracob: a novel graph-based constant-column biclustering method for mining growth phenotype data.

MOTIVATION: Growth phenotype profiling of genome-wide gene-deletion strains over stress conditions can offer a clear picture that the essentiality of genes depends on environmental conditions. Systematically identifying groups of genes from such high-throughput data that share similar patterns of conditional essentiality and dispensability under various environmental conditions can elucidate how genetic interactions of the growth phenotype are regulated in response to the environment. RESULTS: We first demonstrate that detecting such 'co-fit' gene groups can be cast as a less well-studied problem in biclustering, i.e. constant-column biclustering. Despite significant advances in biclustering techniques, very few were designed for mining in growth phenotype data. Here, we propose Gracob, a novel, efficient graph-based method that casts and solves the constant-column biclustering problem as a maximal clique finding problem in a multipartite graph. We compared Gracob with a large collection of widely used biclustering methods that cover different types of algorithms designed to detect different types of biclusters. Gracob showed superior performance on finding co-fit genes over all the existing methods on both a variety of synthetic data sets with a wide range of settings, and three real growth phenotype datasets for E. coli, proteobacteria and yeast. AVAILABILITY AND IMPLEMENTATION: Our program is freely available for download at http://sfb.kaust.edu.sa/Pages/Software.aspx. CONTACT: xin.gao@kaust.edu.sa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Sequence2Vec: a novel embedding approach for modeling transcription factor binding affinity landscape.

MOTIVATION: An accurate characterization of transcription factor (TF)-DNA affinity landscape is crucial to a quantitative understanding of the molecular mechanisms underpinning endogenous gene regulation. While recent advances in biotechnology have brought the opportunity for building binding affinity prediction methods, the accurate characterization of TF-DNA binding affinity landscape still remains a challenging problem. RESULTS: Here we propose a novel sequence embedding approach for modeling the transcription factor binding affinity landscape. Our method represents DNA binding sequences as a hidden Markov model which captures both position specific information and long-range dependency in the sequence. A cornerstone of our method is a novel message passing-like embedding algorithm, called Sequence2Vec, which maps these hidden Markov models into a common nonlinear feature space and uses these embedded features to build a predictive model. Our method is a novel combination of the strength of probabilistic graphical models, feature space embedding and deep learning. We conducted comprehensive experiments on over 90 large-scale TF-DNA datasets which were measured by different high-throughput experimental technologies. Sequence2Vec outperforms alternative machine learning methods as well as the state-of-the-art binding affinity prediction methods. AVAILABILITY AND IMPLEMENTATION: Our program is freely available at https://github.com/ramzan1990/sequence2vec. CONTACT: xin.gao@kaust.edu.sa or lsong@cc.gatech.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Transcatheter Aortic Valve Implantation Improves Cardiac Sympathetic Nerve Activity on 123I-Metaiodobenzylguanidine Myocardial Scintigraphy in Severe Aortic Valve Stenosis.

BACKGROUND: There is a consensus that overactivation of the cardiac sympathetic nervous system (CSN) proportionately increases the severity of heart failure and is accompanied by worse prognosis. Because it is unknown whether patients with aortic valve stenosis (AS) have similar CSN activation, we investigated the effect of transcatheter aortic valve implantation (TAVI).Methods and Results:We enrolled 31 consecutive patients with AS treated by TAVI. 123I-metaiodobenzylguanidine (MIBG) myocardial scintigraphy was performed at baseline and at 2 weeks after TAVI. At baseline, the early heart-mediastinum ratio (H/M) was within normal limits (3.0±0.5), but the delayed H/M was low (2.6±0.6) and the washout rate (WR) was high (34±13%). WR negatively correlated with aortic valve area (r=-0.389, P<0.01) and cardiac output (r=-0.595, P<0.01) and positively correlated with norepinephrine (r=0.519, P<0.01) and log NT-proBNP level (r=0.613, P<0.01). After TAVI, there were significant decreases in the norepinephrine level (366±179 ng/mL vs. 276±125 ng/mL, P<0.01) and WR (34±13 vs. 26±11%, P<0.01). CONCLUSIONS: The WR of MIBG was a useful marker of CSN activity and severity of AS. Immediate improvement of CSN activity after TAVI implied that AS hemodynamics per se enhanced CSN.

MRE: a web tool to suggest foreign enzymes for the biosynthesis pathway design with competing endogenous reactions in mind.

To rationally design a productive heterologous biosynthesis system, it is essential to consider the suitability of foreign reactions for the specific endogenous metabolic infrastructure of a host. We developed a novel web server, called MRE, which, for a given pair of starting and desired compounds in a given chassis organism, ranks biosynthesis routes from the perspective of the integration of new reactions into the endogenous metabolic system. For each promising heterologous biosynthesis pathway, MRE suggests actual enzymes for foreign metabolic reactions and generates information on competing endogenous reactions for the consumption of metabolites. These unique, chassis-centered features distinguish MRE from existing pathway design tools and allow synthetic biologists to evaluate the design of their biosynthesis systems from a different angle. By using biosynthesis of a range of high-value natural products as a case study, we show that MRE is an effective tool to guide the design and optimization of heterologous biosynthesis pathways. The URL of MRE is http://www.cbrc.kaust.edu.sa/mre/.

Parameter estimation methods for gene circuit modeling from time-series mRNA data: a comparative study.

Parameter estimation is a challenging computational problem in the reverse engineering of biological systems. Because advances in biotechnology have facilitated wide availability of time-series gene expression data, systematic parameter estimation of gene circuit models from such time-series mRNA data has become an important method for quantitatively dissecting the regulation of gene expression. By focusing on the modeling of gene circuits, we examine here the performance of three types of state-of-the-art parameter estimation methods: population-based methods, online methods and model-decomposition-based methods. Our results show that certain population-based methods are able to generate high-quality parameter solutions. The performance of these methods, however, is heavily dependent on the size of the parameter search space, and their computational requirements substantially increase as the size of the search space increases. In comparison, online methods and model decomposition-based methods are computationally faster alternatives and are less dependent on the size of the search space. Among other things, our results show that a hybrid approach that augments computationally fast methods with local search as a subsequent refinement procedure can substantially increase the quality of their parameter estimates to the level on par with the best solution obtained from the population-based methods while maintaining high computational speed. These suggest that such hybrid methods can be a promising alternative to the more commonly used population-based methods for parameter estimation of gene circuit models when limited prior knowledge about the underlying regulatory mechanisms makes the size of the parameter search space vastly large.

A standard-enabled workflow for synthetic biology.

A synthetic biology workflow is composed of data repositories that provide information about genetic parts, sequence-level design tools to compose these parts into circuits, visualization tools to depict these designs, genetic design tools to select parts to create systems, and modeling and simulation tools to evaluate alternative design choices. Data standards enable the ready exchange of information within such a workflow, allowing repositories and tools to be connected from a diversity of sources. The present paper describes one such workflow that utilizes, among others, the Synthetic Biology Open Language (SBOL) to describe genetic designs, the Systems Biology Markup Language to model these designs, and SBOL Visual to visualize these designs. We describe how a standard-enabled workflow can be used to produce types of design information, including multiple repositories and software tools exchanging information using a variety of data standards. Recently, the ACS Synthetic Biology journal has recommended the use of SBOL in their publications.

Molecular characterization of aspartylglucosaminidase, a lysosomal hydrolase upregulated during strobilation in the moon jellyfish, Aurelia aurita.

The life cycle of the moon jellyfish, Aurelia aurita, alternates between a benthic asexual polyp stage and a planktonic sexual medusa (jellyfish) stage. Transition from polyp to medusa is called strobilation. To investigate the molecular mechanisms of strobilation, we screened for genes that are upregulated during strobilation using the differential display method and we identified aspartylglucosaminidase (AGA), which encodes a lysosomal hydrolase. Similar to AGAs from other species, Aurelia AGA possessed an N-terminal signal peptide and potential N-glycosylation sites. The genomic region of Aurelia AGA was approximately 9.8 kb in length and contained 12 exons and 11 introns. Quantitative RT-PCR analysis revealed that AGA expression increased during strobilation, and was then decreased in medusae. To inhibit AGA function, we administered the lysosomal acidification inhibitors, chloroquine or bafilomycin A1, to animals during strobilation. Both inhibitors disturbed medusa morphogenesis at the oral end, suggesting involvement of lysosomal hydrolases in strobilation.

A framework for scalable parameter estimation of gene circuit models using structural information.

MOTIVATION: Systematic and scalable parameter estimation is a key to construct complex gene regulatory models and to ultimately facilitate an integrative systems biology approach to quantitatively understand the molecular mechanisms underpinning gene regulation. RESULTS: Here, we report a novel framework for efficient and scalable parameter estimation that focuses specifically on modeling of gene circuits. Exploiting the structure commonly found in gene circuit models, this framework decomposes a system of coupled rate equations into individual ones and efficiently integrates them separately to reconstruct the mean time evolution of the gene products. The accuracy of the parameter estimates is refined by iteratively increasing the accuracy of numerical integration using the model structure. As a case study, we applied our framework to four gene circuit models with complex dynamics based on three synthetic datasets and one time series microarray data set. We compared our framework to three state-of-the-art parameter estimation methods and found that our approach consistently generated higher quality parameter solutions efficiently. Although many general-purpose parameter estimation methods have been applied for modeling of gene circuits, our results suggest that the use of more tailored approaches to use domain-specific information may be a key to reverse engineering of complex biological systems. AVAILABILITY: http://sfb.kaust.edu.sa/Pages/Software.aspx. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.