Quantitative prediction of the effect of genetic variation using hidden Markov models

In silico features of ADAMTS13 contributing to plasmatic ADAMTS13 levels in neonates with congenital heart disease

INTRODUCTION

Risk factors contributing to heightened thrombosis in pediatric congenital heart disease (CHD) patients are not fully understood. Among the neonatal CHD population, those presenting with single ventricular physiology are at the highest risk for perioperative thrombosis. The von Willebrand factor and ADAMTS13 interactions have emerged as causative risk factors for pediatric stroke and could contribute to heightened thrombosis in CHD neonates.

METHODS

This study investigates a cohort of children with single ventricle physiology and undergoing cardiac surgery, during which some patients developed thrombosis. In this cohort, we analyzed the relationship of several molecular features of ADAMTS13 with the plasma and activity levels in patients at risk of thrombosis. Additionally, in light of the natural antithrombotic activity of ADAMTS13, we have sequenced the ADAMTS13 gene for each patient and evaluated the role of genetic variants in determining the plasma ADAMTS13 levels using a series of in silico tools including Hidden Markov Models, EVmutation, and Rosetta.

RESULTS

Lower ADAMTS13 levels were found in patients that developed thrombosis. A novel in silico analysis to assess haplotype effect of co-occurring variants identified alterations in relative surface area and solvation energy as important contributors. Our analysis suggested that beneficial or deleterious effect of a variant can be reasonably predicted by comprehensive analysis of in silico assessment and in vitro and/or in vivo data.

CONCLUSION

Findings from this study add to our understanding the role of genetic features of ADAMTS13 in patients at high risk of thrombosis related to an imbalanced relation between VWF and ADAMTS13.

Loss of Janus Associated Kinase 1 Alters Urothelial Cell Function and Facilitates the Development of Bladder Cancer

Inherited Primary Immunodeficiency (PID) disorders are associated with increased risk of malignancy that may relate to impaired antitumor immune responses or a direct role for PID germline mutations in tumorigenesis. We recently identified germline loss of function mutations in Janus Associated Kinase 1 (JAK1) causing primary immunodeficiency characterized by infections and associated with early onset, fatal high-grade bladder carcinoma. Somatic mutations in JAK1, required for immune cell signaling in response to interferon gamma (IFNγ), have been associated with several non-hematopoietic and hematopoietic cancer cell types but pathogenic mechanisms remain largely unexplored. Here we demonstrate that JAK1 is required for the intrinsic IFNγ response of urothelial cells impacting immunogenicity and cell survival. Specifically, JAK1-deficient urothelial cells showed reduced surface expression of major histocompatibility complex class II (MHC II), intercellular adhesion molecule-1 (ICAM-1) and programmed death-ligand-1 (PD-L1) after IFNγ stimulation and were resistant to IFNγ-induced apoptosis and lymphocyte-mediated killing. In addition, we identify a previously unknown role for IFNγ signaling in modulating urothelial differentiation. Together, our findings support a role for urothelial cell JAK1 in immune surveillance and development of bladder cancer. Our results have implications for patients with rare JAK1 PID and, more broadly, inform development of biomarker and targeted therapies for urothelial carcinoma.

Evolution and Diversification of FRUITFULL Genes in Solanaceae

Ecologically and economically important fleshy edible fruits have evolved from dry fruit numerous times during angiosperm diversification. However, the molecular mechanisms that underlie these shifts are unknown. In the Solanaceae there has been a major shift to fleshy fruits in the subfamily Solanoideae. Evidence suggests that an ortholog of FRUITFULL (FUL), a transcription factor that regulates cell proliferation and limits the dehiscence zone in the silique of Arabidopsis, plays a similar role in dry-fruited Solanaceae. However, studies have shown that FUL orthologs have taken on new functions in fleshy fruit development, including regulating elements of tomato ripening such as pigment accumulation. FUL belongs to the core eudicot euFUL clade of the angiosperm AP1/FUL gene lineage. The euFUL genes fall into two paralogous clades, euFULI and euFULII. While most core eudicots have one gene in each clade, Solanaceae have two: FUL1 and FUL2 in the former, and MBP10 and MBP20 in the latter. We characterized the evolution of the euFUL genes to identify changes that might be correlated with the origin of fleshy fruit in Solanaceae. Our analyses revealed that the Solanaceae FUL1 and FUL2 clades probably originated through an early whole genome multiplication event. By contrast, the data suggest that the MBP10 and MBP20 clades are the result of a later tandem duplication event. MBP10 is expressed at weak to moderate levels, and its atypical short first intron lacks putative transcription factor binding sites, indicating possible pseudogenization. Consistent with this, our analyses show that MBP10 is evolving at a faster rate compared to MBP20. Our analyses found that Solanaceae euFUL gene duplications, evolutionary rates, and changes in protein residues and expression patterns are not correlated with the shift in fruit type. This suggests deeper analyses are needed to identify the mechanism underlying the change in FUL ortholog function.

The role of small in-frame insertions/deletions in inherited eye disorders and how structural modelling can help estimate their pathogenicity

Background

Although the majority of small in-frame insertions/deletions (indels) has no/little affect on protein function, a small subset of these changes has been causally associated with genetic disorders. Notably, the molecular mechanisms and frequency by which they give rise to disease phenotypes remain largely unknown. The aim of this study is to provide insights into the role of in-frame indels (≤21 nucleotides) in two genetically heterogeneous eye disorders.

Results

One hundred eighty-one probands with childhood cataracts and 486 probands with retinal dystrophy underwent multigene panel testing in a clinical diagnostic laboratory. In-frame indels were collected and evaluated both clinically and in silico. Variants that could be modeled in the context of protein structure were identified and analysed using integrative structural modeling. Overall, 55 small in-frame indels were detected in 112 of 667 probands (16.8 %); 17 of these changes were novel to this study and 18 variants were reported clinically. A reliable model of the corresponding protein sequence could be generated for 8 variants. Structural modeling indicated a diverse range of molecular mechanisms of disease including disruption of secondary and tertiary protein structure and alteration of protein-DNA binding sites.

Conclusions

In childhood cataract and retinal dystrophy subjects, one small in-frame indel is clinically reported in every ~37 individuals tested. The clinical utility of computational tools evaluating these changes increases when the full complexity of the involved molecular mechanisms is embraced.

Electronic supplementary material

The online version of this article (doi:10.1186/s13023-016-0505-0) contains supplementary material, which is available to authorized users.

HMMvar-func: a new method for predicting the functional outcome of genetic variants

Background

Numerous tools have been developed to predict the fitness effects (i.e., neutral, deleterious, or beneficial) of genetic variants on corresponding proteins. However, prediction in terms of whether a variant causes the variant bearing protein to lose the original function or gain new function is also needed for better understanding of how the variant contributes to disease/cancer. To address this problem, the present work introduces and computationally defines four types of functional outcome of a variant: gain, loss, switch, and conservation of function. The deployment of multiple hidden Markov models is proposed to computationally classify mutations by the four functional impact types.

Results

The functional outcome is predicted for over a hundred thyroid stimulating hormone receptor (TSHR) mutations, as well as cancer related mutations in oncogenes or tumor suppressor genes. The results show that the proposed computational method is effective in fine grained prediction of the functional outcome of a mutation, and can be used to help elucidate the molecular mechanism of disease/cancer causing mutations. The program is freely available at http://bioinformatics.cs.vt.edu/zhanglab/HMMvar/download.php.

Conclusion

This work is the first to computationally define and predict functional impact of mutations, loss, switch, gain, or conservation of function. These fine grained predictions can be especially useful for identifying mutations that cause or are linked to cancer.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0781-z) contains supplementary material, which is available to authorized users.

Computational approaches to study the effects of small genomic variations

Advances in DNA sequencing technologies have led to an avalanche-like increase in the number of gene sequences deposited in public databases over the last decade as well as the detection of an enormous number of previously unseen nucleotide variants therein. Given the size and complex nature of the genome-wide sequence variation data, as well as the rate of data generation, experimental characterization of the disease association of each of these variations or their effects on protein structure/function would be costly, laborious, time-consuming, and essentially impossible. Thus, in silico methods to predict the functional effects of sequence variations are constantly being developed. In this review, we summarize the major computational approaches and tools that are aimed at the prediction of the functional effect of mutations, and describe the state-of-the-art databases that can be used to obtain information about mutation significance. We also discuss future directions in this highly competitive field.

Predicting the combined effect of multiple genetic variants

Background

Many genetic variants have been identified in the human genome. The functional effects of a single variant have been intensively studied. However, the joint effects of multiple variants in the same genes have been largely ignored due to their complexity or lack of data. This paper uses HMMvar, a hidden Markov model based approach, to investigate the combined effect of multiple variants from the 1000 Genomes Project. Two tumor suppressor genes, TP53 and phosphatase and tensin homolog (PTEN), are also studied for the joint effect of compensatory indel variants.

Results

Results show that there are cases where the joint effect of having multiple variants in the same genes is significantly different from that of a single variant. The deleterious effect of a single indel variant can be alleviated by their compensatory indels in TP53 and PTEN. Compound mutations in two genes, β-MHC and MyBP-C, leading to severer cardiovascular disease compared to single mutations, are also validated.

Conclusions

This paper extends the functionality of HMMvar, a tool for assigning a quantitative score to a variant, to measure not only the deleterious effect of a single variant but also the joint effect of multiple variants. HMMvar is the first tool that can predict the functional effects of both single and general multiple variations on proteins. The precomputed scores for multiple variants from the 1000 Genomes Project and the HMMvar package are available at https://bioinformatics.cs.vt.edu/zhanglab/HMMvar/

The pattern of DNA cleavage intensity around indels

Indels (insertions and deletions) are the second most common form of genetic variations in the eukaryotic genomes and are responsible for a multitude of genetic diseases. Despite its significance, detailed molecular mechanisms for indel generation are still unclear. Here we examined 2,656,597 small human and mouse germline indels, 16,742 human somatic indels, 10,599 large human insertions, and 5,822 large chimpanzee insertions and systematically analyzed the patterns of DNA cleavage intensities in the 200 base pair regions surrounding these indels. Our results show that DNA cleavage intensities close to the start and end points of indels are significantly lower than other regions, for both small human germline and somatic indels and also for mouse small indels. Compared to small indels, the patterns of DNA cleavage intensity around large indels are more complex, and there are two low intensity regions near each end of the indels that are approximately 13 bp apart from each other. Detailed analyses of a subset of indels show that there is slight difference in cleavage intensity distribution between insertion indels and deletion indels that could be contributed by their respective enrichment of different repetitive elements. These results will provide new insight into indel generation mechanisms.

The road from next-generation sequencing to personalized medicine

Moving from a traditional medical model of treating pathologies to an individualized predictive and preventive model of personalized medicine promises to reduce the healthcare cost on an overburdened and overwhelmed system. Next-generation sequencing (NGS) has the potential to accelerate the early detection of disorders and the identification of pharmacogenetics markers to customize treatments. This review explains the historical facts that led to the development of NGS along with the strengths and weakness of NGS, with a special emphasis on the analytical aspects used to process NGS data. There are solutions to all the steps necessary for performing NGS in the clinical context where the majority of them are very efficient, but there are some crucial steps in the process that need immediate attention.