The role of recessive inheritance in early-onset epileptic encephalopathies: a combined whole-exome sequencing and copy number study.

Early-onset epileptic encephalopathy (EE) and combined developmental and epileptic encephalopathies (DEE) are clinically and genetically heterogeneous severely devastating conditions. Recent studies emphasized de novo variants as major underlying cause suggesting a generally low-recurrence risk. In order to better understand the full genetic landscape of EE and DEE, we performed high-resolution chromosomal microarray analysis in combination with whole-exome sequencing in 63 deeply phenotyped independent patients. After bioinformatic filtering for rare variants, diagnostic yield was improved for recessive disorders by manual data curation as well as molecular modeling of missense variants and untargeted plasma-metabolomics in selected patients. In total, we yielded a diagnosis in ∼42% of cases with causative copy number variants in 6 patients (∼10%) and causative sequence variants in 16 established disease genes in 20 patients (∼32%), including compound heterozygosity for causative sequence and copy number variants in one patient. In total, 38% of diagnosed cases were caused by recessive genes, of which two cases escaped automatic calling due to one allele occurring de novo. Notably, we found the recessive gene SPATA5 causative in as much as 3% of our cohort, indicating that it may have been underdiagnosed in previous studies. We further support candidacy for neurodevelopmental disorders of four previously described genes (PIK3AP1, GTF3C3, UFC1, and WRAP53), three of which also followed a recessive inheritance pattern. Our results therefore confirm the importance of de novo causative gene variants in EE/DEE, but additionally illustrate the major role of mostly compound heterozygous or hemizygous recessive inheritance and consequently high-recurrence risk.

Significant natural product biosynthetic potential of actinorhizal symbionts of the genus frankia, as revealed by comparative genomic and proteomic analyses.

Bacteria of the genus Frankia are mycelium-forming actinomycetes that are found as nitrogen-fixing facultative symbionts of actinorhizal plants. Although soil-dwelling actinomycetes are well-known producers of bioactive compounds, the genus Frankia has largely gone uninvestigated for this potential. Bioinformatic analysis of the genome sequences of Frankia strains ACN14a, CcI3, and EAN1pec revealed an unexpected number of secondary metabolic biosynthesis gene clusters. Our analysis led to the identification of at least 65 biosynthetic gene clusters, the vast majority of which appear to be unique and for which products have not been observed or characterized. More than 25 secondary metabolite structures or structure fragments were predicted, and these are expected to include cyclic peptides, siderophores, pigments, signaling molecules, and specialized lipids. Outside the hopanoid gene locus, no cluster could be convincingly demonstrated to be responsible for the few secondary metabolites previously isolated from other Frankia strains. Few clusters were shared among the three species, demonstrating species-specific biosynthetic diversity. Proteomic analysis of Frankia sp. strains CcI3 and EAN1pec showed that significant and diverse secondary metabolic activity was expressed in laboratory cultures. In addition, several prominent signals in the mass range of peptide natural products were observed in Frankia sp. CcI3 by intact-cell matrix-assisted laser desorption-ionization mass spectrometry (MALDI-MS). This work supports the value of bioinformatic investigation in natural products biosynthesis using genomic information and presents a clear roadmap for natural products discovery in the Frankia genus.

Dereplication and de novo sequencing of nonribosomal peptides.

Nonribosomal peptides (NRPs) are of great pharmacological importance, but there is currently no technology for high-throughput NRP 'dereplication' and sequencing. We used multistage mass spectrometry followed by spectral alignment algorithms for sequencing of cyclic NRPs. We also developed an algorithm for comparative NRP dereplication that establishes similarities between newly isolated and previously identified similar but nonidentical NRPs, substantially reducing dereplication efforts.

Interpretation of tandem mass spectra obtained from cyclic nonribosomal peptides.

Natural and non-natural cyclic peptides are a crucial component in drug discovery programs because of their considerable pharmaceutical properties. Cyclosporin, microcystins, and nodularins are all notable pharmacologically important cyclic peptides. Because these biologically active peptides are often biosynthesized nonribosomally, they often contain nonstandard amino acids, thus increasing the complexity of the resulting tandem mass spectrometry data. In addition, because of the cyclic nature, the fragmentation patterns of many of these peptides showed much higher complexity when compared to related counterparts. Therefore, at the present time it is still difficult to annotate cyclic peptides MS/MS spectra. In this current work, an annotation program was developed for the annotation and characterization of tandem mass spectra obtained from cyclic peptides. This program, which we call MS-CPA is available as a web tool (http://lol.ucsd.edu/ms-cpa_v1/Input.py). Using this program, we have successfully annotated the sequence of representative cyclic peptides, such as seglitide, tyrothricin, desmethoxymajusculamide C, dudawalamide A, and cyclomarins, in a rapid manner and also were able to provide the first-pass structure evidence of a newly discovered natural product based on predicted sequence. This compound is not available in sufficient quantities for structural elucidation by other means such as NMR. In addition to the development of this cyclic annotation program, it was observed that some cyclic peptides fragmented in unexpected ways resulting in the scrambling of sequences. In summary, MS-CPA not only provides a platform for rapid confirmation and annotation of tandem mass spectrometry data obtained with cyclic peptides but also enables quantitative analysis of the ion intensities. This program facilitates cyclic peptide analysis, sequencing, and also acts as a useful tool to investigate the uncommon fragmentation phenomena of cyclic peptides and aids the characterization of newly discovered cyclic peptides encountered in drug discovery programs.