Eitan Rubin

Mitochondrial DNA heteroplasmy in diabetes and normal adults: role of acquired and inherited mutational patterns in twins

Heteroplasmy, the mixture of mitochondrial genomes (mtDNA), varies among individuals and cells. Heteroplasmy levels alter the penetrance of pathological mtDNA mutations, and the susceptibility to age-related diseases such as Parkinsons disease. Although mitochondrial dysfunction occurs in age-related type 2 diabetes mellitus (T2DM), the involvement of heteroplasmy in diabetes is unclear. We hypothesized that the heteroplasmic mutational (HM) pattern may change in T2DM. To test this, we used next-generation sequencing, i.e. massive parallel sequencing (MPS), along with PCR-cloning-Sanger sequencing to analyze HM in blood and skeletal muscle DNA samples from monozygotic (MZ) twins either concordant or discordant for T2DM. Great variability was identified in the repertoires and amounts of HMs among individuals, with a tendency towards more mutations in skeletal muscle than in blood. Whereas many HMs were unique, many were either shared among twin pairs or among tissues of the same individual, regardless of their prevalence. This suggested a heritable influence on even low abundance HMs. We found no clear differences between T2DM and controls. However, we found ~5-fold increase of HMs in non-coding sequences implying the influence of negative selection (P < 0.001). This negative selection was evident both in moderate to highly abundant heteroplasmy (>5% of the molecules per sample) and in low abundance heteroplasmy (<5% of the molecules). Although our study found no evidence supporting the involvement of HMs in the etiology of T2DM, the twin study found clear evidence of a heritable influence on the accumulation of HMs as well as the signatures of selection in heteroplasmic mutations.

Serum apolipoproteins C-I and C-III are reduced in stomach cancer patients: results from MALDI-based peptidome and immuno-based clinical assays.

Finding new peptide biomarkers for stomach cancer in human sera that can be implemented into a clinically practicable prediction method for monitoring of stomach cancer. We studied the serum peptidome from two different biorepositories. We first employed a C8-reverse phase liquid chromatography approach for sample purification, followed by mass-spectrometry analysis. These were applied onto serum samples from cancer-free controls and stomach cancer patients at various clinical stages. We then created a bioinformatics analysis pipeline and identified peptide signature discriminating stomach adenocarcinoma patients from cancer-free controls. Matrix Assisted Laser Desorption/Ionization–Time of Flight (MALDI-TOF) results from 103 samples revealed 9 signature peptides; with prediction accuracy of 89% in the training set and 88% in the validation set. Three of the discriminating peptides discovered were fragments of Apolipoproteins C-I and C-III (apoC-I and C-III); we further quantified their serum levels, as well as CA19-9 and CRP, employing quantitative commercial-clinical assays in 142 samples. ApoC-I and apoC-III quantitative results correlated with the MS results. We then employed apoB-100-normalized apoC-I and apoC-III, CA19-9 and CRP levels to generate rules set for stomach cancer prediction. For training, we used sera from one repository, and for validation, we used sera from the second repository. Prediction accuracies of 88.4% and 74.4% were obtained in the training and validation sets, respectively. Serum levels of apoC-I and apoC-III combined with other clinical parameters can serve as a basis for the formulation of a diagnostic score for stomach cancer patients.

mtDNA mutation pattern in tumors and human evolution are shaped by similar selective constraints

Multiple human mutational landscapes of normal and cancer conditions are currently available. However, while the unique mutational patterns of tumors have been extensively studied, little attention has been paid to similarities between malignant and normal conditions. Here we compared the pattern of mutations in the mitochondrial genomes (mtDNAs) of cancer (98 sequences) and natural populations (2400 sequences). De novo mtDNA mutations in cancer preferentially colocalized with ancient variants in human phylogeny. A significant portion of the cancer mutations was organized in recurrent combinations (COMs), reaching a length of seven mutations, which also colocalized with ancient variants. Thus, by analyzing similarities rather than differences in patterns of mtDNA mutations in tumor and human evolution, we discovered evidence for similar selective constraints, suggesting a functional potential for these mutations.

MitoBamAnnotator: A web-based tool for detecting and annotating heteroplasmy in human mitochondrial DNA sequences.

The use of Next-Generation Sequencing of mitochondrial DNA is becoming widespread in biological and clinical research. This, in turn, creates a need for a convenient tool that detects and analyzes heteroplasmy. Here we present MitoBamAnnotator, a user friendly web-based tool that allows maximum flexibility and control in heteroplasmy research. MitoBamAnnotator provides the user with a comprehensively annotated overview of mitochondrial genetic variation, allowing for an in-depth analysis with no prior knowledge in programming.

The Realistic Modelling of Biological Systems: A Workshop Synopsis

There is a growing awareness for the need to understand the basic design principles of living systems. In May of 2005, a diverse group of researchers from the fields of biomedicine, physics, mathematics, engineering, and computer science were brought together in Mizpe Hayamim, Israel to contemplate the current and future trends in computational modeling of biology. In the following work we provide an overview of the discussions that took place and describe some of the research projects that were presented. We also discuss how these seemingly disparate efforts may be integrated and directed at the development of meaningful computational models of biological systems. The wide range of techniques presented in Mizpe Hayamim served to demonstrate not only the breadth of scale found in biology but also the diversity in criteria for the development and application of numerical models in the field. One of the key issues remains the reconciliation of different model types and their effective use as a single composite representation. By attempting to formulate a unifying theme that transcends traditional boundaries between disciplines, it is hoped that this workshop provided a first rallying point that will promote a new level of interaction and synergy in the field.

Survival in acute myeloid leukemia is associated with NKp44 splice variants.

NKp44 is a receptor encoded by the NCR2 gene, which is expressed by cytokine-activated natural killer (NK) cells that are involved in anti-AML immunity. NKp44 has three splice variants corresponding to NKp44ITIM+ (NKp44-1) and NKp44ITIM− (NKp44-2, and NKp44-3) isoforms. RNAseq data of AML patients revealed similar survival of NKp46+NKp44+ and NKp46+NKp44− patients. However, if grouped according to the NKp44 splice variant profile, NKp44-1 expression was significantly associated with poor survival of AML patients. Moreover, activation of PBMC from healthy controls showed co-dominant expression of NKp44-1 and NKp44-3, while primary NK clones show more diverse NKp44 splice variant profiles. Cultured primary NK cells resulted in NKp44-1 dominance and impaired function associated with PCNA over-expression by target cells. This impaired functional phenotype could be rescued by blocking of NKp44 receptor. Human NK cell lines revealed co-dominant expression of NKp44-1 and NKp44-3 and showed a functional phenotype that was not inhibited by PCNA over-expression. Furthermore, transfection-based overexpression of NKp44-1, but not NKp44-2/NKp44-3, reversed the endogenous resistance of NK-92 cells to PCNA-mediated inhibition, and resulted in poor formation of stable lytic immune synapses. This research contributes to the understanding of AML prognosis by shedding new light on the functional implications of differential splicing of NKp44.

CHILD: a new tool for detecting low-abundance insertions and deletions in standard sequence traces

Several methods have been proposed for detecting insertion/deletions (indels) from chromatograms generated by Sanger sequencing. However, most such methods are unsuitable when the mutated and normal variants occur at unequal ratios, such as is expected to be the case in cancer, with organellar DNA or with alternatively spliced RNAs. In addition, the current methods do not provide robust estimates of the statistical confidence of their results, and the sensitivity of this approach has not been rigorously evaluated. Here, we present CHILD, a tool specifically designed for indel detection in mixtures where one variant is rare. CHILD makes use of standard sequence alignment statistics to evaluate the significance of the results. The sensitivity of CHILD was tested by sequencing controlled mixtures of deleted and undeleted plasmids at various ratios. Our results indicate that CHILD can identify deleted molecules present as just 5% of the mixture. Notably, the results were plasmid/primer-specific; for some primers and/or plasmids, the deleted molecule was only detected when it comprised 10% or more of the mixture. The false positive rate was estimated to be lower than 0.4%. CHILD was implemented as a user-oriented web site, providing a sensitive and experimentally validated method for the detection of rare indel-carrying molecules in common Sanger sequence reads.

NKp44 and NKp30 splice variant profiles in decidua and tumor tissues: a comparative viewpoint

NKp44 and NKp30 splice variant profiles have been shown to promote diverse cellular functions. Moreover, microenvironment factors such as TGF-β, IL-15 and IL-18 are able to influence both NKp44 and NKp30 splice variant profiles, leading to cytokine-associated profiles. Placenta and cancerous tissues have many similarities; both are immunologically privileged sites and both share immune tolerance mechanisms to support tissue development. Therefore, we studied the profiles of NKp44 and NKp30 splice variants in these states by comparing (i) decidua from pregnancy disorder and healthy gestation and (ii) matched normal and cancer tissue. Decidua samples had high incidence of both NKp44 and NKp30. In cancerous state it was different; while NKp30 expression was evident in most cancerous and matched normal tissues, NKp44 incidence was lower and was mostly associated with the cancerous tissues. A NKp44-1dominant inhibitory profile predominated in healthy pregnancy gestation. Interestingly, the NKp44-2/3 activation profile becomes the leading profile in spontaneous abortions, whereas balanced NKp44 profiles were observed in preeclampsia. In contrast, a clear preference for the NKp30a/b profile was evident in the 1st trimester decidua, yet no significant differences were observed for NKp30 profiles between healthy gestation and spontaneous abortions/preeclampsia. Both cancerous and matched normal tissues manifested balanced NKp30c inhibitory and NKp30a/b activation profiles with a NKp44-1dominant profile. However, a shift in NKp30 profiles between matched normal and cancer tissue was observed in half of the cases. To summarize, NKp44 and NKp30 splice variants profiles are tissue/condition specific and demonstrate similarity between placenta and cancerous tissues.

PCM-SABRE: a platform for benchmarking and comparing outcome prediction methods in precision cancer medicine

BackgroundNumerous publications attempt to predict cancer survival outcome from gene expression data using machine-learning methods. A direct comparison of these works is challenging for the following reasons: (1) inconsistent measures used to evaluate the performance of different models, and (2) incomplete specification of critical stages in the process of knowledge discovery. There is a need for a platform that would allow researchers to replicate previous works and to test the impact of changes in the knowledge discovery process on the accuracy of the induced models.ResultsWe developed the PCM-SABRE platform, which supports the entire knowledge discovery process for cancer outcome analysis. PCM-SABRE was developed using KNIME. By using PCM-SABRE to reproduce the results of previously published works on breast cancer survival, we define a baseline for evaluating future attempts to predict cancer outcome with machine learning. We used PCM-SABRE to replicate previous work that describe predictive models of breast cancer recurrence, and tested the performance of all possible combinations of feature selection methods and data mining algorithms that was used in either of the works. We reconstructed the work of Chou et al. observing similar trends – superior performance of Probabilistic Neural Network (PNN) and logistic regression (LR) algorithms and inconclusive impact of feature pre-selection with the decision tree algorithm on subsequent analysis.ConclusionsPCM-SABRE is a software tool that provides an intuitive environment for rapid development of predictive models in cancer precision medicine.

The age-phenome database.

Data linking specific ages or age ranges with disease are abundant in biomedical literature. However, these data are organized such that searching for age-phenotype relationships is difficult. Recently, we described the Age-Phenome Knowledge-base (APK), a computational platform for storage and retrieval of information concerning age-related phenotypic patterns. Here, we report that data derived from over 1.5 million human-related PubMed abstracts have been added to APK. Using a text-mining pipeline, 35,683 entries which describe relationships between age and phenotype (such as disease) have been introduced into the database. Comparing the results to those obtained by a human reader reveals that the overall accuracy of these entries is estimated to exceed 80%. The usefulness of these data for obtaining new insight regarding age-disease relationships is demonstrated using clustering analysis, which is shown to capture obvious, as well as potentially interesting relationships between diseases. In addition, a new tool for browsing and searching the APK database is presented. We thus present a unique resource and a new framework for studying age-disease relationships and other phenotypic processes.