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Introduction: Esophageal atresia with or without tracheoesophageal fistula (EA/TEF) occurs approximately 1 in 3.500 live births representing the most common malformation of the upper digestive tract. Only half a century ago, EA/TEF was fatal among affected newborns suggesting that the steady birth prevalence might in parts be due to mutational de novo events in genes involved in foregut development.
Methods: To identify mutational de novo events in EA/TEF patients, we surveyed the exome of 30 case-parent trios. Identified and confirmed de novo variants were prioritized using in silico prediction tools. To investigate the embryonic role of genes harboring prioritized de novo variants we performed targeted analysis of mouse transcriptome data of esophageal tissue obtained at the embryonic day (E) E8.5, E12.5, and postnatal.
Results: In total we prioritized 14 novel de novo variants in 14 different genes (APOL2, EEF1D, CHD7, FANCB, GGT6, KIAA0556, NFX1, NPR2, PIGC, SLC5A2, TANC2, TRPS1, UBA3, and ZFHX3) and eight rare de novo variants in eight additional genes (CELSR1, CLP1, GPR133, HPS3, MTA3, PLEC, STAB1, and PPIP5K2). Through personal communication during the project, we identified an additional EA/TEF case-parent trio with a rare de novo variant in ZFHX3. In silico prediction analysis of the identified variants and comparative analysis of mouse transcriptome data of esophageal tissue obtained at E8.5, E12.5, and postnatal prioritized CHD7, TRPS1, and ZFHX3 as EA/TEF candidate genes. Re-sequencing of ZFHX3 in additional 192 EA/TEF patients did not identify further putative EA/TEF-associated variants.
Conclusion: Our study suggests that rare mutational de novo events in genes involved in foregut development contribute to the development of EA/TEF.
Haloferax volcanii is a well-established model species for haloarchaea. Small scale RNomics and bioinformatics predictions were used to identify small non-coding RNAs (sRNAs), and deletion mutants revealed that sRNAs have important regulatory functions. A recent dRNA-Seq study was used to characterize the primary transcriptome. Unexpectedly, it was revealed that, under optimal conditions, H. volcanii contains more non-coding sRNAs than protein-encoding mRNAs. However, the dRNA-Seq approach did not contain any length information. Therefore, a mixed RNA-Seq approach was used to determine transcript length and to identify additional transcripts, which are not present under optimal conditions. In total, 50 million paired end reads of 150 nt length were obtained. 1861 protein-coding RNAs (cdRNAs) were detected, which encoded 3092 proteins. This nearly doubled the coverage of cdRNAs, compared to the previous dRNA-Seq study. About 2/3 of the cdRNAs were monocistronic, and 1/3 covered more than one gene. In addition, 1635 non-coding sRNAs were identified. The highest fraction of non-coding RNAs were cis antisense RNAs (asRNAs). Analysis of the length distribution revealed that sRNAs have a median length of about 150 nt. Based on the RNA-Seq and dRNA-Seq results, genes were chosen to exemplify characteristics of the H. volcanii transcriptome by Northern blot analyses, e.g. 1) the transcript patterns of gene clusters can be straightforward, but also very complex, 2) many transcripts differ in expression level under the four analyzed conditions, 3) some genes are transcribed into RNA isoforms of different length, which can be differentially regulated, 4) transcripts with very long 5’-UTRs and with very long 3’-UTRs exist, and 5) about 30% of all cdRNAs have overlapping 3’-ends, which indicates, together with the asRNAs, that H. volcanii makes ample use of sense-antisense interactions. Taken together, this RNA-Seq study, together with a previous dRNA-Seq study, enabled an unprecedented view on the H. volcanii transcriptome.
Biological ageing is a degenerative and irreversible process, ultimately leading to death of the organism. The process is complex and under the control of genetic, environmental and stochastic traits. Although many theories have been established during the last decades, none of these are able to fully describe the complex mechanisms, which lead to ageing. Generally, biological processes and environmental factors lead to molecular damage and an accumulation of impaired cellular components. In contrast, counteracting surveillance systems are effective, including repair, remodelling and degradation of damaged or impaired components, respectively. Nevertheless, at some point these systems are no longer effective, either because the increasing amount of molecular damages can not longer be removed efficiently or because the repairing and removing mechanisms themselves become affected by impairing effects. The organism finally declines and dies. To investigate and to understand these counteracting mechanisms and the complex interplay of decline and maintenance, holistic and systems biological investigations are required. Hence, the processes which lead to ageing in the fungal model organism Podospora anserina, had been analysed using different advanced bioinformatics methods. In contrast to many other ageing models, P. anserina exhibits a short lifespan, a less biochemical complexity and it provides a good accessibility for genetic manipulations.
To achieve a general overview on the different biochemical processes, which are affected during ageing in P. anserina, an initial comprehensive investigation was applied, which aimed to reveal genes significantly regulated and expressed in an age-dependent manner. This investigation was based on an age-dependent transcriptome analysis. Sophisticated and comprehensive analyses revealed different age-related pathways and indicated that especially autophagy may play a crucial role during ageing. For example, it was found that the expression of autophagy-associated genes increases in the course of ageing.
Subsequently, to investigate and to characterise the autophagy pathway, its associated single components and their interactions, Path2PPI, a new bioinformatics approach, was developed. Path2PPI enables the prediction of protein-protein interaction networks of particular pathways by means of a homology comparison approach and was applied to construct the protein-protein interaction network of autophagy in P. anserina.
The predicted network was extended by experimental data, comprising the transcriptome data as well as newly generated protein-protein interaction data achieved from a yeast two-hybrid analysis. Using different mathematical and statistical methods the topological properties of the constructed network had been compared with those of randomly generated networks to approve its biological significance. In addition, based on this topological and functional analysis, the most important proteins were determined and functional modules were identified, which correspond to the different sub-pathways of autophagy. Due to the integrated transcriptome data the autophagy network could be linked to the ageing process. For example, different proteins had been identified, which genes are continuously up- or down-regulated during ageing and it was shown for the first time that autophagy-associated genes are significantly often co-expressed during ageing.
The presented biological network provides a systems biological view on autophagy and enables further studies, which aim to analyse the relationship of autophagy and ageing. Furthermore, it allows the investigation of potential methods for intervention into the ageing process and to extend the healthy lifespan of P. anserina as well as of other eukaryotic organisms, in particular humans.