Madhavi Ganapathiraju

Global genetic analysis in mice unveils central role for cilia in congenital heart disease

Congenital heart disease (CHD) is the most prevalent birth defect, affecting nearly 1% of live births; the incidence of CHD is up to tenfold higher in human fetuses. A genetic contribution is strongly suggested by the association of CHD with chromosome abnormalities and high recurrence risk. Here we report findings from a recessive forward genetic screen in fetal mice, showing that cilia and cilia-transduced cell signalling have important roles in the pathogenesis of CHD. The cilium is an evolutionarily conserved organelle projecting from the cell surface with essential roles in diverse cellular processes. Using echocardiography, we ultrasound scanned 87,355 chemically mutagenized C57BL/6J fetal mice and recovered 218 CHD mouse models. Whole-exome sequencing identified 91 recessive CHD mutations in 61 genes. This included 34 cilia-related genes, 16 genes involved in cilia-transduced cell signalling, and 10 genes regulating vesicular trafficking, a pathway important for ciliogenesis and cell signalling. Surprisingly, many CHD genes encoded interacting proteins, suggesting that an interactome protein network may provide a larger genomic context for CHD pathogenesis. These findings provide novel insights into the potential Mendelian genetic contribution to CHD in the fetal population, a segment of the human population not well studied. We note that the pathways identified show overlap with CHD candidate genes recovered in CHD patients, suggesting that they may have relevance to the more complex genetics of CHD overall. These CHD mouse models and >8,000 incidental mutations have been sperm archived, creating a rich public resource for human disease modelling.

Active learning for human protein-protein interaction prediction

BackgroundBiological processes in cells are carried out by means of protein-protein interactions. Determining whether a pair of proteins interacts by wet-lab experiments is resource-intensive; only about 38,000 interactions, out of a few hundred thousand expected interactions, are known today. Active machine learning can guide the selection of pairs of proteins for future experimental characterization in order to accelerate accurate prediction of the human protein interactome.ResultsRandom forest (RF) has previously been shown to be effective for predicting protein-protein interactions. Here, four different active learning algorithms have been devised for selection of protein pairs to be used to train the RF. With labels of as few as 500 protein-pairs selected using any of the four active learning methods described here, the classifier achieved a higher F-score (harmonic mean of Precision and Recall) than with 3000 randomly chosen protein-pairs. F-score of predicted interactions is shown to increase by about 15% with active learning in comparison to that with random selection of data.ConclusionActive learning algorithms enable learning more accurate classifiers with much lesser labelled data and prove to be useful in applications where manual annotation of data is formidable. Active learning techniques demonstrated here can also be applied to other proteomics applications such as protein structure prediction and classification.

Characterization of protein secondary structure

What do proteins look like? Proteins are composed of fundamental building blocks of chemical molecules called amino acids. When a protein is synthesized by the cells, initially it is just a string of amino acids. This string arranges itself in a process called protein folding into a complex three-dimensional structure capable of exerting the function of the specific protein. We briefly review the fundamental building blocks of proteins, their primary and secondary structure.

Retinitis pigmentosa associated with rhodopsin mutations: Correlation between phenotypic variability and molecular effects.

Similar retinitis pigmentosa (RP) phenotypes can result from mutations affecting different rhodopsin regions, and distinct amino acid substitutions can cause different RP severity and progression rates. Specifically, both the R135L and R135W mutations (cytoplasmic end of H3) result in diffuse, severe disease (class A), but R135W causes more severe and more rapidly progressive RP than R135L. The P180A and G188R mutations (second intradiscal loop) exhibit a mild phenotype with regional variability (class B1) and diffuse disease of moderate severity (class B2), respectively. Computational and in vitro studies of these mutants provide molecular insights into this phenotypic variability.

Comparison of stability predictions and simulated unfolding of rhodopsin structures.

Developing a better mechanistic understanding of membrane protein folding is urgently needed because of the discovery of an increasing number of human diseases, where membrane protein instability and misfolding is involved. Towards this goal, we investigated folding and stability of 7‐transmembrane (TM) helical bundles by computational methods. We compared the results of three different algorithms for predicting changes in stability of proteins against an experimental mutation dataset obtained for bacteriorhodopsin (BR) and mammalian rhodopsin and find that 61.6% and 70.6% of the mutation results can potentially be explained by known local contributors to the stability of the folded state of BR and mammalian rhodopsin, respectively. To obtain further information on the predicted folding pathway of 7‐TM proteins, we conducted simulated thermal unfolding experiments of all available rhodopsin structures with resolution better than 3 Å using the Floppy Inclusions and Rigid Substructure Topography (FIRST) method (Jacobs, D. J., A. J. Rader, L. A. Kuhn and M. F. Thorpe [2001] Proteins44, 150) described previously for a single mammalian rhodopsin structure (Rader et al. [2004] PNAS101, 7246). In statistical comparison we found that structures of mammalian rhodopsin have a stability core that is characterized by long‐range interactions involving amino acids close in space but distant in sequence comprising positions from both extracellular loop and TM regions. In contrast, BR‐simulated unfolding does not reveal such a core but is dominated by interactions within individual and groups of TM helices, consistent with the two‐stage hypothesis of membrane protein folding. Similar results were obtained for halo‐ and sensory rhodopsins as for BRs. However, the average folding core energies of sensory rhodopsins were in between those observed for mammalian rhodopsins and BRs hinting at a possible evolution of these structures toward a rhodopsin‐like behavior. These results support the conclusion that although the two‐stage model can explain the mechanisms of folding and stability of BR, it fails to account for the folding and stability of mammalian rhodopsin, even though the two proteins are structurally related.

The complex genetics of hypoplastic left heart syndrome

Congenital heart disease (CHD) affects up to 1% of live births. Although a genetic etiology is indicated by an increased recurrence risk, sporadic occurrence suggests that CHD genetics is complex. Here, we show that hypoplastic left heart syndrome (HLHS), a severe CHD, is multigenic and genetically heterogeneous. Using mouse forward genetics, we report what is, to our knowledge, the first isolation of HLHS mutant mice and identification of genes causing HLHS. Mutations from seven HLHS mouse lines showed multigenic enrichment in ten human chromosome regions linked to HLHS. Mutations in Sap130 and Pcdha9, genes not previously associated with CHD, were validated by CRISPR–Cas9 genome editing in mice as being digenic causes of HLHS. We also identified one subject with HLHS with SAP130 and PCDHA13 mutations. Mouse and zebrafish modeling showed that Sap130 mediates left ventricular hypoplasia, whereas Pcdha9 increases penetrance of aortic valve abnormalities, both signature HLHS defects. These findings show that HLHS can arise genetically in a combinatorial fashion, thus providing a new paradigm for the complex genetics of CHD.

Analysis of LMNB1 Duplications in Autosomal Dominant Leukodystrophy Provides Insights into Duplication Mechanisms and Allele-Specific Expression

Autosomal dominant leukodystrophy (ADLD) is an adult onset demyelinating disorder that is caused by duplications of the lamin B1 (LMNB1) gene. However, as only a few cases have been analyzed in detail, the mechanisms underlying LMNB1 duplications are unclear. We report the detailed molecular analysis of the largest collection of ADLD families studied, to date. We have identified the minimal duplicated region necessary for the disease, defined all the duplication junctions at the nucleotide level and identified the first inverted LMNB1 duplication. We have demonstrated that the duplications are not recurrent; patients with identical duplications share the same haplotype, likely inherited from a common founder and that the duplications originated from intrachromosomal events. The duplication junction sequences indicated that nonhomologous end joining or replication‐based mechanisms such fork stalling and template switching or microhomology‐mediated break induced repair are likely to be involved. LMNB1 expression was increased in patients’ fibroblasts both at mRNA and protein levels and the three LMNB1 alleles in ADLD patients show equal expression, suggesting that regulatory regions are maintained within the rearranged segment. These results have allowed us to elucidate duplication mechanisms and provide insights into allele‐specific LMNB1 expression levels.

Wiki-Pi: A Web-Server of Annotated Human Protein-Protein Interactions to Aid in Discovery of Protein Function

Protein-protein interactions (PPIs) are the basis of biological functions. Knowledge of the interactions of a protein can help understand its molecular function and its association with different biological processes and pathways. Several publicly available databases provide comprehensive information about individual proteins, such as their sequence, structure, and function. There also exist databases that are built exclusively to provide PPIs by curating them from published literature. The information provided in these web resources is protein-centric, and not PPI-centric. The PPIs are typically provided as lists of interactions of a given gene with links to interacting partners; they do not present a comprehensive view of the nature of both the proteins involved in the interactions. A web database that allows search and retrieval based on biomedical characteristics of PPIs is lacking, and is needed. We present Wiki-Pi (read Wiki-π), a web-based interface to a database of human PPIs, which allows users to retrieve interactions by their biomedical attributes such as their association to diseases, pathways, drugs and biological functions. Each retrieved PPI is shown with annotations of both of the participant proteins side-by-side, creating a basis to hypothesize the biological function facilitated by the interaction. Conceptually, it is a search engine for PPIs analogous to PubMed for scientific literature. Its usefulness in generating novel scientific hypotheses is demonstrated through the study of IGSF21, a little-known gene that was recently identified to be associated with diabetic retinopathy. Using Wiki-Pi, we infer that its association to diabetic retinopathy may be mediated through its interactions with the genes HSPB1, KRAS, TMSB4X and DGKD, and that it may be involved in cellular response to external stimuli, cytoskeletal organization and regulation of molecular activity. The website also provides a wiki-like capability allowing users to describe or discuss an interaction. Wiki-Pi is available publicly and freely at http://severus.dbmi.pitt.edu/wiki-pi/.

Active machine learning for transmembrane helix prediction

BackgroundAbout 30% of genes code for membrane proteins, which are involved in a wide variety of crucial biological functions. Despite their importance, experimentally determined structures correspond to only about 1.7% of protein structures deposited in the Protein Data Bank due to the difficulty in crystallizing membrane proteins. Algorithms that can identify proteins whose high-resolution structure can aid in predicting the structure of many previously unresolved proteins are therefore of potentially high value. Active machine learning is a supervised machine learning approach which is suitable for this domain where there are a large number of sequences but only very few have known corresponding structures. In essence, active learning seeks to identify proteins whose structure, if revealed experimentally, is maximally predictive of others.ResultsAn active learning approach is presented for selection of a minimal set of proteins whose structures can aid in the determination of transmembrane helices for the remaining proteins. TMpro, an algorithm for high accuracy TM helix prediction we previously developed, is coupled with active learning. We show that with a well-designed selection procedure, high accuracy can be achieved with only few proteins. TMpro, trained with a single protein achieved an F-score of 94% on benchmark evaluation and 91% on MPtopo dataset, which correspond to the state-of-the-art accuracies on TM helix prediction that are achieved usually by training with over 100 training proteins.ConclusionActive learning is suitable for bioinformatics applications, where manually characterized data are not a comprehensive representation of all possible data, and in fact can be a very sparse subset thereof. It aids in selection of data instances which when characterized experimentally can improve the accuracy of computational characterization of remaining raw data. The results presented here also demonstrate that the feature extraction method of TMpro is well designed, achieving a very good separation between TM and non TM segments.

Computational biology and language

Current scientific research is characterized by increasing specialization, accumulating knowledge at a high speed due to parallel advances in a multitude of sub-disciplines. Recent estimates suggest that human knowledge doubles every two to three years – and with the advances in information and communication technologies, this wide body of scientific knowledge is available to anyone, anywhere, anytime. This may also be referred to as ambient intelligence – an environment characterized by plentiful and available knowledge. The bottleneck in utilizing this knowledge for specific applications is not accessing but assimilating the information and transforming it to suit the needs for a specific application. The increasingly specialized areas of scientific research often have the common goal of converting data into insight allowing the identification of solutions to scientific problems. Due to this common goal, there are strong parallels between different areas of applications that can be exploited and used to cross-fertilize different disciplines. For example, the same fundamental statistical methods are used extensively in speech and language processing, in materials science applications, in visual processing and in biomedicine. Each sub-discipline has found its own specialized methodologies making these statistical methods successful to the given application. The unification of specialized areas is possible because many different problems can share strong analogies, making the theories developed for one problem applicable to other areas of research. It is the goal of this paper to demonstrate the utility of merging two disparate areas of applications to advance scientific research. The merging process requires cross-disciplinary collaboration to allow maximal exploitation of advances in one sub-discipline for that of another. We will demonstrate this general concept with the specific example of merging language technologies and computational biology.