Fungi produce an impressive array of secondary metabolites (SMs) including mycotoxins, antibiotics and pharmaceuticals. The genes responsible for their biosynthesis, export, and transcriptional regulation are often found in contiguous gene clusters. To facilitate annotation of these clusters in sequenced fungal genomes, we developed the web-based software SMURF (www.jcvi.org/smurf/) to systematically predict clustered SM genes based on their genomic context and domain content. We applied SMURF to catalog putative clusters in 27 publicly available fungal genomes. Comparison with genetically characterized clusters from six fungal species showed that SMURF accurately recovered all clusters and detected additional potential clusters. Subsequent comparative analysis revealed the striking biosynthetic capacity and variability of the fungal SM pathways and the correlation between unicellularity and the absence of SMs. Further genetics studies are needed to experimentally confirm these clusters.

The heritable component to attempted and completed suicide is partly related to psychiatric disorders and also partly independent of them. While attempted suicide linkage regions have been identified on 2p11–12 and 6q25–26, there are likely many more such loci, the discovery of which will require a much higher resolution approach, such as the genome-wide association study (GWAS). With this in mind, we conducted an attempted suicide GWAS that compared the single nucleotide polymorphism (SNP) genotypes of 1,201 bipolar (BP) subjects with a history of suicide attempts to the genotypes of 1,497 BP subjects without a history of suicide attempts. 2,507 SNPs with evidence for association at p<0.001 were identified. These associated SNPs were subsequently tested for association in a large and independent BP sample set. None of these SNPs were significantly associated in the replication sample after correcting for multiple testing, but the combined analysis of the two sample sets produced an association signal on 2p25 (rs300774) at the threshold of genome-wide significance (p= 5.07 × 10−8). The associated SNPs on 2p25 fall in a large linkage disequilibrium block containing the ACP1 gene, a gene whose expression is significantly elevated in BP subjects who have completed suicide. Furthermore, the ACP1 protein is a tyrosine phosphatase that influences Wnt signaling, a pathway regulated by lithium, making ACP1 a functional candidate for involvement in the phenotype. Larger GWAS sample sets will be required to confirm the signal on 2p25 and to identify additional genetic risk factors increasing susceptibility for attempted suicide.

INTRODUCTION The clinical course of coronavirus 2019 (COVID-19) is heterogeneous, ranging from mild to severe multiorgan failure and death. In this study, we analyzed cell-free DNA (cfDNA) as a biomarker of injury to define the sources of tissue injury that contribute to such different trajectories. METHODS We conducted a multicenter prospective cohort study to enroll patients with COVID-19 and collect plasma samples. Plasma cfDNA was subject to bisulfite sequencing. A library of tissue-specific DNA methylation signatures was used to analyze sequence reads to quantitate cfDNA from different tissue types. We then determined the correlation of tissue-specific cfDNA measures to COVID-19 outcomes. Similar analyses were performed for healthy controls and a comparator group of patients with respiratory syncytial virus and influenza. RESULTS We found markedly elevated levels and divergent tissue sources of cfDNA in COVID-19 patients compared with patients who had influenza and/or respiratory syncytial virus and with healthy controls. The major sources of cfDNA in COVID-19 were hematopoietic cells, vascular endothelium, hepatocytes, adipocytes, kidney, heart, and lung. cfDNA levels positively correlated with COVID-19 disease severity, C-reactive protein, and D-dimer. cfDNA profile at admission identified patients who subsequently required intensive care or died during hospitalization. Furthermore, the increased cfDNA in COVID-19 patients generated excessive mitochondrial ROS (mtROS) in renal tubular cells in a concentration-dependent manner. This mtROS production was inhibited by a TLR9-specific antagonist. CONCLUSION cfDNA maps tissue injury that predicts COVID-19 outcomes and may mechanistically propagate COVID-19–induced tissue injury. FUNDING Intramural Targeted Anti–COVID-19 grant, NIH.

Summary Background Epigenetic studies that utilize peripheral tissues to identify molecular substrates of neuropsychiatric disorders rely on the assumption that disease-relevant, cellular alterations that occur in the brain are mirrored and detectable in peripheral tissues such as blood. We sought to test this assumption by using a mouse model of Cushing’s disease and asking whether epigenetic changes induced by glucocorticoids can be correlated between these tissue types. Methods Mice were treated with different doses of glucocorticoids in their drinking water for four weeks to assess gene expression and DNA methylation (DNAm) changes in the stress response gene Fkbp5. Results Significant linear relationships were observed between DNAm and four-week mean plasma corticosterone levels for both blood (R2 = 0.68, P = 7.1×10−10) and brain (R2 = 0.33, P = 0.001). Further, degree of methylation change in blood correlated significantly with both methylation (R2 = 0.49, P = 2.7×10−5) and expression (R2 = 0.43, P = 3.5×10−5) changes in hippocampus, with the notable observation that methylation changes occurred at different intronic regions between blood and brain tissues. Conclusion Although our findings are limited to several intronic CpGs in a single gene, our results demonstrate that DNA from blood can be used to assess dynamic, glucocorticoid-induced changes occurring in the brain. However, for such correlation analyses to be effective, tissue-specific locations of these epigenetic changes may need to be considered when investigating brain-relevant changes in peripheral tissues.

Background Previous studies suggest that the relationship between genetic risk and depression may be moderated by stressful life events (SLEs). The goal of this study was to assess whether SLEs moderate the association between polygenic risk of Major Depressive Disorder (MDD) and depressive symptoms in older adults. Methods We used logistic and negative binomial regressions to assess the associations between polygenic risk, SLEs and depressive symptoms in a sample of 8,761 participants from the Health and Retirement Study (HRS). Polygenic scores were derived from the Psychiatric Genomics Consortium (PGC) genome-wide association study (GWAS) of MDD. SLEs were operationalized as a dichotomous variable indicating whether participants had experienced at least 1 stressful event during the previous two years. Depressive symptoms were measured using an 8-item CES-D subscale and operationalized as both a dichotomous and a count variable. Results The odds of reporting ≥ 4 depressive symptoms were over twice as high among individuals who experienced at least one SLE (OR = 2.19, 95% CI = [1.86, 2.58]). Polygenic scores were significantly associated with depressive symptoms (β = .21, p = <.0001), although the variance explained was modest (Pseudo r2 = .0095). None of the interaction terms for polygenic scores and SLEs were statistically significant. Conclusions Polygenic risk and SLEs are robust, independent predictors of depressive symptoms in older adults. Consistent with an additive model, we found no evidence that SLEs moderated the association between common variant polygenic risk and depressive symptoms.

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BackgroundNumerous genome-wide gene expression studies of bipolar disorder (BP) have been carried out. These studies are heterogeneous, underpowered and use overlapping samples. We conducted a systematic review of these studies to synthesize the current findings.MethodsWe identified all genome-wide gene expression studies on BP in humans. We then carried out a quantitative mega-analysis of studies done with post-mortem brain tissue. We obtained raw data from each study and used standardized procedures to process and analyze the data. We then combined the data and conducted three separate mega-analyses on samples from 1) any region of the brain (9 studies); 2) the prefrontal cortex (PFC) (6 studies); and 3) the hippocampus (2 studies). To minimize heterogeneity across studies, we focused primarily on the most numerous, recent and comprehensive studies.ResultsA total of 30 genome-wide gene expression studies of BP done with blood or brain tissue were identified. We included 10 studies with data on 211 microarrays on 57 unique BP cases and 229 microarrays on 60 unique controls in the quantitative mega-analysis. A total of 382 genes were identified as significantly differentially expressed by the three analyses. Eleven genes survived correction for multiple testing with a q-value < 0.05 in the PFC. Among these were FKBP5 and WFS1, which have been previously implicated in mood disorders. Pathway analyses suggested a role for metallothionein proteins, MAP Kinase phosphotases, and neuropeptides.ConclusionWe provided an up-to-date summary of results from gene expression studies of the brain in BP. Our analyses focused on the highest quality data available and provided results by brain region so that similarities and differences can be examined relative to disease status. The results are available for closer inspection on-line at Metamoodics [http://metamoodics.igm.jhmi.edu/], where investigators can look up any genes of interest and view the current results in their genomic context and in relation to leading findings from other genomic experiments in bipolar disorder.

Clinical applications of precision oncology require accurate tests that can distinguish true cancer specific mutations from errors introduced at each step of next-generation sequencing (NGS). To date, no bulk sequencing study has addressed the effects of cross-site reproducibility, nor the biological, technical and computational factors that influence variant identification. Here we report a systematic interrogation of somatic mutations in paired tumor-normal cell lines to identify factors affecting detection reproducibility and accuracy at six different centers. Using whole genome sequencing (WGS) and whole-exome sequencing (WES), we evaluated the reproducibility of different sample types with varying input amount and tumor purity, and multiple library construction protocols, followed by processing with nine bioinformatics pipelines. We found that read coverage and callers affected both WGS and WES reproducibility, but WES performance was influenced by insert fragment size, genomic copy content and the global imbalance score (GIV; G > T/C > A). Finally, taking into account library preparation protocol, tumor content, read coverage and bioinformatics processes concomitantly, we recommend actionable practices to improve the reproducibility and accuracy of NGS experiments for cancer mutation detection.