Shared genetic risk between eating disorder- and substance-use-related phenotypes:Evidence from genome-wide association studies

First published: 16 February 202

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Background Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.Peer reviewe

Transcriptome analysis of the two unrelated fungal β-lactam producers Acremonium chrysogenum and Penicillium chrysogenum: Velvet-regulated genes are major targets during conventional strain improvement programs

Abstract Background Cephalosporins and penicillins are the most frequently used β-lactam antibiotics for the treatment of human infections worldwide. The main industrial producers of these antibiotics are Acremonium chrysogenum and Penicillium chrysogenum, two taxonomically unrelated fungi. Both were subjects of long-term strain development programs to reach economically relevant antibiotic titers. It is so far unknown, whether equivalent changes in gene expression lead to elevated antibiotic titers in production strains. Results Using the sequence of PcbC, a key enzyme of β-lactam antibiotic biosynthesis, from eighteen different pro- and eukaryotic microorganisms, we have constructed a phylogenetic tree to demonstrate the distant relationship of both fungal producers. To address the question whether both fungi have undergone similar genetic adaptions, we have performed a comparative gene expression analysis of wild-type and production strains. We found that strain improvement is associated with the remodeling of the transcriptional landscape in both fungi. In P. chrysogenum, 748 genes showed differential expression, while 1572 genes from A. chrysogenum are differentially expressed in the industrial strain. Common in both fungi is the upregulation of genes belonging to primary and secondary metabolism, notably those involved in precursor supply for β-lactam production. Other genes not essential for β-lactam production are downregulated with a preference for those responsible for transport processes or biosynthesis of other secondary metabolites. Transcriptional regulation was shown to be an important parameter during strain improvement in different organisms. We therefore investigated deletion strains of the major transcriptional regulator velvet from both production strains. We identified 567 P. chrysogenum and 412 A. chrysogenum Velvet target genes. In both deletion strains, approximately 50% of all secondary metabolite cluster genes are differentially regulated, including β-lactam biosynthesis genes. Most importantly, 35-57% of Velvet target genes are among those that showed differential expression in both improved industrial strains. Conclusions The major finding of our comparative transcriptome analysis is that strain improvement programs in two unrelated fungal β-lactam antibiotic producers alter the expression of target genes of Velvet, a global regulator of secondary metabolism. From these results, we conclude that regulatory alterations are crucial contributing factors for improved β-lactam antibiotic titers during strain improvement in both fungi

Characterization of Dicer-dependent small RNAs.

<p>(A) Strain-specific and overrepresented unique reads in ∆<i>ku70</i>FRT2 compared to ∆<i>dcl2</i>∆<i>dcl1</i> and <i>vice versa</i>. (B) Nucleotide preference and size distribution of Dicer-dependent small RNAs. (C) Pie graphs of the relative abundance of Dicer-dependent sRNAs and (D) Dicer-dependent sRNA-producing loci in accordance to their strand bias.</p

Accumulation of sRNAs along representative coding-sequences.

<p>(A) Normalized read count (TPTM: transcripts per ten million) of ∆<i>ku70</i>FRT2 (grey graph) and ∆<i>dcl2</i>∆<i>dcl1</i> (black graph) of two representative Dicer-dependent coding regions, the Copia13-like transposable element Pc17g00440 and the putative DNA-binding protein Pc12g14660. (B) Dicer-independent sRNA accumulation for the coding region of the putative cell-wall protein Pc20g06530 and for a histidine tRNA-gene cluster. To ensure a faultless representation of Dicer-independent reads the graphs for ∆<i>dcl2</i>∆<i>dcl1</i> were slightly moved to the right.</p

Statistical summary of small RNA sequencing data and distribution of small RNAs with perfect match to the genome sequence of <i>P</i>. <i>chrysogenum</i>.

<p>Statistical summary of small RNA sequencing data and distribution of small RNAs with perfect match to the genome sequence of <i>P</i>. <i>chrysogenum</i>.</p

Chromosomal distribution of small RNAs and sRNA-producing loci.

<p>Pie graphs for total reads (A) and unique reads (B) are showing the relative abundance of sRNAs located in tRNAs, rRNAs, intergenic, exonic and intronic regions in ∆<i>ku70</i>FRT2. Alignments of sRNA-producing loci of ∆<i>ku70</i>FRT2 (C) and ∆<i>dcl2</i>∆<i>dcl1</i> (D) show that the number of sRNAs that map to both DNA strands of one feature have strongly increased and that the fraction of sRNA loci that align to exonic regions in sense orientation has decreased substantially in ∆<i>dcl2</i>∆<i>dcl1</i> compared to ∆<i>ku70</i>FRT2.</p

Predicted milRNAs and the appearance of their reads within the three datasets.

<p>Predicted milRNAs and the appearance of their reads within the three datasets.</p

Validation and expression analysis of (A) milR-1 and (B) milR-21.

<p>Total RNA from strain P2niaD18, the recipient ∆<i>ku70</i>FRT2 as well as Dicer single and double mutant strains were used for polyacrylamide gel electrophoresis and northern blot analysis. Mature milRNAs (milRs) and milRNA precursors (pre-milRs) were detected with revers complement ³²P-labled DNA probes. Below, loading controls of the total RNA, stained with ethidium bromide (EtBr), and the predicted secondary structures of milRNA precursors are given. On the secondary structures, milRNA sequences are highlighted in red and arrows indicate the expected Dicer cleavage sites.</p

Length distribution of <i>P</i>. <i>chrysogenum</i> small RNA population.

<p>Length distribution of mapping sRNA reads for the datasets of total (A) and unique (B) reads obtained from three different samples. Frequency of the 5'-nucleotide of the unique reads of ∆<i>ku70</i>FRT2 (C) and ∆<i>dcl2</i>∆<i>dcl1</i> (D) in dependency of their read length.</p