Piotr Dittwald

Small noncoding differentially methylated copy-number variants, including lncRNA genes, cause a lethal lung developmental disorder

An unanticipated and tremendous amount of the noncoding sequence of the human genome is transcribed. Long noncoding RNAs (lncRNAs) constitute a significant fraction of non-protein-coding transcripts; however, their functions remain enigmatic. We demonstrate that deletions of a small noncoding differentially methylated region at 16q24.1, including lncRNA genes, cause a lethal lung developmental disorder, alveolar capillary dysplasia with misalignment of pulmonary veins (ACD/MPV), with parent-of-origin effects. We identify overlapping deletions 250 kb upstream of FOXF1 in nine patients with ACD/MPV that arose de novo specifically on the maternally inherited chromosome and delete lung-specific lncRNA genes. These deletions define a distant cis-regulatory region that harbors, besides lncRNA genes, also a differentially methylated CpG island, binds GLI2 depending on the methylation status of this CpG island, and physically interacts with and up-regulates the FOXF1 promoter. We suggest that lung-transcribed 16q24.1 lncRNAs may contribute to long-range regulation of FOXF1 by GLI2 and other transcription factors. Perturbation of lncRNA-mediated chromatin interactions may, in general, be responsible for position effect phenomena and potentially cause many disorders of human development.

Recurrent HERV‐H‐Mediated 3q13.2–q13.31 Deletions Cause a Syndrome of Hypotonia and Motor, Language, and Cognitive Delays

We describe the molecular and clinical characterization of nine individuals with recurrent, 3.4‐Mb, de novo deletions of 3q13.2–q13.31 detected by chromosomal microarray analysis. All individuals have hypotonia and language and motor delays; they variably express mild to moderate cognitive delays (8/9), abnormal behavior (7/9), and autism spectrum disorders (3/9). Common facial features include downslanting palpebral fissures with epicanthal folds, a slightly bulbous nose, and relative macrocephaly. Twenty‐eight genes map to the deleted region, including four strong candidate genes, DRD3, ZBTB20, GAP43, and BOC, with important roles in neural and/or muscular development. Analysis of the breakpoint regions based on array data revealed directly oriented human endogenous retrovirus (HERV‐H) elements of ∼5 kb in size and of >95% DNA sequence identity flanking the deletion. Subsequent DNA sequencing revealed different deletion breakpoints and suggested nonallelic homologous recombination (NAHR) between HERV‐H elements as a mechanism of deletion formation, analogous to HERV‐I‐flanked and NAHR‐mediated AZFa deletions. We propose that similar HERV elements may also mediate other recurrent deletion and duplication events on a genome‐wide scale. Observation of rare recurrent chromosomal events such as these deletions helps to further the understanding of mechanisms behind naturally occurring variation in the human genome and its contribution to genetic disease.

An Efficient Method to Calculate the Aggregated Isotopic Distribution and Exact Center-Masses

In this article, we present a computation- and memory-efficient method to calculate the probabilities of occurrence and exact center-masses of the aggregated isotopic distribution of a molecule. The method uses fundamental mathematical properties of polynomials given by the Newton-Girard theorem and Viete’s formulae. The calculation is based on the atomic composition of the molecule and the natural abundances of the elemental isotopes in normal terrestrial matter. To evaluate the performance of the proposed method, which we named BRAIN, we compare it with the results obtained from five existing software packages (IsoPro, Mercury, Emass, NeutronCluster, and IsoDalton) for 10 biomolecules. Additionally, we compare the computed mass centers with the results obtained by calculating, and subsequently aggregating, the fine isotopic distribution for two of the exemplary biomolecules. The algorithm will be made available as a Bioconductor package in R, and is also available upon request.

Inverted Low-Copy Repeats and Genome Instability—A Genome-Wide Analysis

Inverse paralogous low‐copy repeats (IP‐LCRs) can cause genome instability by nonallelic homologous recombination (NAHR)‐mediated balanced inversions. When disrupting a dosage‐sensitive gene(s), balanced inversions can lead to abnormal phenotypes. We delineated the genome‐wide distribution of IP‐LCRs >1 kB in size with >95% sequence identity and mapped the genes, potentially intersected by an inversion, that overlap at least one of the IP‐LCRs. Remarkably, our results show that 12.0% of the human genome is potentially susceptible to such inversions and 942 genes, 99 of which are on the X chromosome, are predicted to be disrupted secondary to such an inversion! In addition, IP‐LCRs larger than 800 bp with at least 98% sequence identity (duplication/triplication facilitating IP‐LCRs, DTIP‐LCRs) were recently implicated in the formation of complex genomic rearrangements with a duplication‐inverted triplication–duplication (DUP‐TRP/INV‐DUP) structure by a replication‐based mechanism involving a template switch between such inverted repeats. We identified 1,551 DTIP‐LCRs that could facilitate DUP‐TRP/INV‐DUP formation. Remarkably, 1,445 disease‐associated genes are at risk of undergoing copy‐number gain as they map to genomic intervals susceptible to the formation of DUP‐TRP/INV‐DUP complex rearrangements. We implicate inverted LCRs as a human genome architectural feature that could potentially be responsible for genomic instability associated with many human disease traits.

Human endogenous retroviral elements promote genome instability via non-allelic homologous recombination

BackgroundRecurrent rearrangements of the human genome resulting in disease or variation are mainly mediated by non-allelic homologous recombination (NAHR) between low-copy repeats. However, other genomic structures, including AT-rich palindromes and retroviruses, have also been reported to underlie recurrent structural rearrangements. Notably, recurrent deletions of Yq12 conveying azoospermia, as well as non-pathogenic reciprocal duplications, are mediated by human endogenous retroviral elements (HERVs). We hypothesized that HERV elements throughout the genome can serve as substrates for genomic instability and result in human copy-number variation (CNV).ResultsWe developed parameters to identify HERV elements similar to those that mediate Yq12 rearrangements as well as recurrent deletions of 3q13.2q13.31. We used these parameters to identify HERV pairs genome-wide that may cause instability. Our analysis highlighted 170 pairs, flanking 12.1% of the genome. We cross-referenced these predicted susceptibility regions with CNVs from our clinical databases for potentially HERV-mediated rearrangements and identified 78 CNVs. We subsequently molecularly confirmed recurrent deletion and duplication rearrangements at four loci in ten individuals, including reciprocal rearrangements at two loci. Breakpoint sequencing revealed clustering in regions of high sequence identity enriched in PRDM9-mediated recombination hotspot motifs.ConclusionsThe presence of deletions and reciprocal duplications suggests NAHR as the causative mechanism of HERV-mediated CNV, even though the length and the sequence homology of the HERV elements are less than currently thought to be required for NAHR. We propose that in addition to HERVs, other repetitive elements, such as long interspersed elements, may also be responsible for the formation of recurrent CNVs via NAHR.

BRAIN: A Universal Tool for High-Throughput Calculations of the Isotopic Distribution for Mass Spectrometry

This Letter presents the R-package implementation of the recently introduced polynomial method for calculating the aggregated isotopic distribution called BRAIN (Baffling Recursive Algorithm for Isotopic distributioN calculations). The algorithm is simple, easy to understand, highly accurate, fast, and memory-efficient. The method is based on the application of the Newton-Girard theorem and Viètes formulae to the polynomial coding of different aggregated isotopic variants. As a result, an elegant recursive equation is obtained for computing the occurrence probabilities of consecutive aggregated isotopic peaks. Additionally, the algorithm also allows calculating the center-masses of the aggregated isotopic variants. We propose an implementation which is suitable for high-throughput processing and easily customizable for application in different areas of mass spectral data analyses. A case study demonstrates how the R-package can be applied in the context of protein research, but the software can be also used for calculating the isotopic distribution in the context of lipidomics, metabolomics, glycoscience, or even space exploration. More materials, i.e., reference manual, vignette, and the package itself are available at Bioconductor online (http://www.bioconductor.org/packages/release/bioc/html/BRAIN.html).

On the Fine Isotopic Distribution and Limits to Resolution in Mass Spectrometry.

AbstractMass spectrometry enables the study of increasingly larger biomolecules with increasingly higher resolution, which is able to distinguish between fine isotopic variants having the same additional nucleon count, but slightly different masses. Therefore, the analysis of the fine isotopic distribution becomes an interesting research topic with important practical applications. In this paper, we propose the comprehensive methodology for studying the basic characteristics of the fine isotopic distribution. Our approach uses a broad spectrum of methods ranging from generating functions—that allow us to estimate the variance and the information theory entropy of the distribution—to the theory of thermal energy fluctuations. Having characterized the variance, spread, shape, and size of the fine isotopic distribution, we are able to indicate limitations to high resolution mass spectrometry. Moreover, the analysis of “thermorelativistic” effects (i.e., mass uncertainty attributable to relativistic effects coupled with the statistical mechanical uncertainty of the energy of an isolated ion), in turn, gives us an estimate of impassable limits of isotopic resolution (understood as the ability to distinguish fine structure peaks), which can be moved further only by cooling the ions. The presented approach highlights the potential of theoretical analysis of the fine isotopic distribution, which allows modeling the data more accurately, aiming to support the successful experimental measurements. Graphical Abstractᅟ

BRAIN 2.0: time and memory complexity improvements in the algorithm for calculating the isotope distribution.

AbstractRecently, an elegant iterative algorithm called BRAIN (Baffling Recursive Algorithm for Isotopic distributioN calculations) was presented. The algorithm is based on the classic polynomial method for calculating aggregated isotope distributions, and it introduces algebraic identities using Newton-Girard and Viète’s formulae to solve the problem of polynomial expansion. Due to the iterative nature of the BRAIN method, it is a requirement that the calculations start from the lightest isotope variant. As such, the complexity of BRAIN scales quadratically with the mass of the putative molecule, since it depends on the number of aggregated peaks that need to be calculated. In this manuscript, we suggest two improvements of the algorithm to decrease both time and memory complexity in obtaining the aggregated isotope distribution. We also illustrate a concept to represent the element isotope distribution in a generic manner. This representation allows for omitting the root calculation of the element polynomial required in the original BRAIN method. A generic formulation for the roots is of special interest for higher order element polynomials such that root finding algorithms and its inaccuracies can be avoided. Graphical abstractᅟ

Comment on "Computation of isotopic peak center-mass distribution by fourier transform".

Jorge Fernandez-de-Cossio Diaz and Jorge Fernandez-de-Cossio recently published1 an algorithm to calculate the aggregated isotope distribution and center-masses based on the molecular formula and elemental isotope distribution that could serve their ISOTOPICA software.2–4 The presented approach is rooted in the polynomial methods as proposed by Brownawell and Fillippo5 and Yergey et al.6 Furthermore, Rockwood and Haimi suggested to utilize the polynomial methods to calculate accurate masses of isotopic peaks.7 Recently, Claesen et al. generalized the method of Rockwood and Haimi as a polynomial generating function to calculate exact center-masses as applied in the BRAIN method.8–11 Fernandez-de-Cossio Diaz and Fernandez-de-Cossio solve the polynomial specified in eqs 18–23 by Claesen et al.8 for the center-masses using the fast Fourier transform approach (FFT) instead of employing an iterative scheme as the case in BRAIN. The operation of the FFT-approach to solve the polynomial generating function opened the possibility for additional improvements in their procedure. The FFT-approach,1 which implements the center-mass calculation will be referred to as FTMC in the remainder of this comment.