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Critical illness myopathy (CIM) is an acquired, devastating, multifactorial muscle-wasting disease with incomplete recovery. The impact on hospital costs and permanent loss of quality of life is enormous. Incomplete recovery might imply that the function of muscle stem cells (MuSC) is impaired. We tested whether epigenetic alterations could be in part responsible. We characterized human muscle stem cells (MuSC) isolated from early CIM and analyzed epigenetic alterations (CIM n = 15, controls n = 21) by RNA-Seq, immunofluorescence, analysis of DNA repair, and ATAC-Seq. CIM-MuSC were transplanted into immunodeficient NOG mice to assess their regenerative potential. CIM-MuSC exhibited significant growth deficits, reduced ability to differentiate into myotubes, and impaired DNA repair. The chromatin structure was damaged, as characterized by alterations in mRNA of histone 1, depletion or dislocation of core proteins of nucleosome remodeling and deacetylase complex, and loosening of multiple nucleosome-spanning sites. Functionally, CIM-MuSC had a defect in building new muscle fibers. Further, MuSC obtained from the electrically stimulated muscle of CIM patients was very similar to control MuSC, indicating the impact of muscle contraction in the onset of CIM. CIM not only affects working skeletal muscle but has a lasting and severe epigenetic impact on MuSC.

The use of RNA sequencing from wastewater samples is a valuable way for estimating infection dynamics and circulating lineages of SARS-CoV-2. This approach is independent from testing individuals and can therefore become the key tool to monitor this and potentially other viruses. However, it is equally important to develop easily accessible and scalable tools which can highlight critical changes in infection rates and dynamics over time across different locations given sequencing data from wastewater. Here, we provide an analysis of lineage dynamics in Berlin and New York City using wastewater sequencing and present PiGx SARS-CoV-2, a highly reproducible computational analysis pipeline with comprehensive reports. This end-to-end pipeline includes all steps from raw data to shareable reports, additional taxonomic analysis, deconvolution and geospatial time series analyses. Using simulated datasets (in silico generated and spiked-in samples) we could demonstrate the accuracy of our pipeline calculating proportions of Variants of Concern (VOC) from environmental as well as pre-mixed samples (spiked-in). By applying our pipeline on a dataset of wastewater samples from Berlin between February 2021 and January 2022, we could reconstruct the emergence of B.1.1.7(alpha) in February/March 2021 and the replacement dynamics from B.1.617.2 (delta) to BA.1 and BA.2 (omicron) during the winter of 2021/2022. Using data from very-short-reads generated in an industrial scale setting, we could see even higher accuracy in our deconvolution. Lastly, using a targeted sequencing dataset from New York City (receptor-binding-domain (RBD) only), we could reproduce the results recovering the proportions of the so-called cryptic lineages shown in the original study. Overall our study provides an in-depth analysis reconstructing virus lineage dynamics from wastewater. While applying our tool on a wide range of different datasets (from different types of wastewater sample locations and sequenced with different methods), we show that PiGx SARS-CoV-2 can be used to identify new mutations and detect any emerging new lineages in a highly automated and scalable way. Our approach can support efforts to establish continuous monitoring and early-warning projects for detecting SARS-CoV-2 or any other pathogen.

The use of RNA sequencing from wastewater samples is a valuable way for estimating infection dynamics and circulating lineages of SARS-CoV-2. This approach is independent from testing individuals and can therefore become the key tool to monitor this and potentially other viruses. However, it is equally important to develop easily accessible and scalable tools which can highlight critical changes in infection rates and dynamics over time across different locations given sequencing data from wastewater. Here, we provide an analysis of lineage dynamics in Berlin and New York City using wastewater sequencing and present PiGx SARS-CoV-2, a highly reproducible computational analysis pipeline with comprehensive reports. This end-to-end pipeline includes all steps from raw data to shareable reports, additional taxonomic analysis, deconvolution and geospatial time series analyses. Using simulated datasets (in silico generated and spiked-in samples) we could demonstrate the accuracy of our pipeline calculating proportions of Variants of Concern (VOC) from environmental as well as pre-mixed samples (spiked-in). By applying our pipeline on a dataset of wastewater samples from Berlin between February 2021 and January 2022, we could reconstruct the emergence of B.1.1.7(alpha) in February/March 2021 and the replacement dynamics from B.1.617.2 (delta) to BA.1 and BA.2 (omicron) during the winter of 2021/2022. Using data from very-short-reads generated in an industrial scale setting, we could see even higher accuracy in our deconvolution. Lastly, using a targeted sequencing dataset from New York City (receptor-binding-domain (RBD) only), we could reproduce the results recovering the proportions of the so-called cryptic lineages shown in the original study. Overall our study provides an in-depth analysis reconstructing virus lineage dynamics from wastewater, and that our tool can be used to identify new mutations and to detect any emerging new lineages with different amplification and sequencing methods. Our approach can support efforts to establish continuous monitoring and early-warning projects for detecting SARS-CoV-2 or any other pathogen.

Acute coronary syndromes (ACS) remain a major cause of worldwide mortality. ACS diagnosis is done by a combination of factors, such as electrocardiogram and plasma biomarkers. These biomarkers, however, lack the power to accurately stratify patients into different risk groups. Instead, we used changes in the circulating cell-free DNA (ccfDNA) methylation profiles to estimate the extent of heart injury and the severity of ACS. Our approach relies on the fact that dying cells in acutely damaged tissue release DNA into the blood, causing an increase in the ccfDNA. In addition, each cell type has a distinct DNA methylation profile. We leverage cell type/state specificity of DNA methylation to deconvolute the cell types of origin for ccfDNA and also find DNA methylation-based biomarkers that stratify patient cohorts. The cohorts consisted of healthy subjects, and patients from three ACS conditions: ST-segment elevation myocardial infarction (STEMI), non-ST-segment elevation myocardial infarction (NSTEMI) and unstable angina (UA). We have used two cohorts of patients - discovery, and validation, both consisting of the same conditions . We have sequenced the ccfDNA from the discovery cohort using Whole Bisulfite Genome Sequencing (WBGS), to obtain an unbiased overview of plasma DNA methylation profiles. We have found a total of 1,614 differential methylated regions (DMRs) in the three ACS groups. Many of the regions are associated with genes involved in cardiovascular conditions and inflammation. Using linear models we were able to narrow down to 254 DMRs significantly associated with ACS severity. The reduced list of DMRs enabled a more accurate stratification of ACS patients. The predictive power of the DMRs was validated in the confirmation cohort using targeted methylation sequencing of the validation cohort. Measuring methylation on ccfDNA showed promise as a method for estimating the level of heart injury during an acute coronary event, and accurate patient risk stratification. The method is however not limited to acute events, and can be extended to other heart related diseases. It can be used for estimating the status of the disease in patients with chronic states, such as heart failure and coronary artery disease.

Epigenetic mechanisms to control chromatin accessibility and structure is important for gene expression in eukaryotic cells. Chromatin regulation ensures proper development and cell fate specification but is also essential later in life. Modifications of histone proteins as an integral component of chromatin can promote either gene expression or repression, respectively. Proteins containing specific domains such as the chromodomain recognize mono-, di- or tri-methylated lysine residues on histone H3. The chromodomain protein MRG-1 in Caenorhabditis elegans is the ortholog of mammalian MRG15, which belongs to the MORF4 Related Gene (MRG) family in humans. In C. elegans MRG-1 predominantly binds methylated histone H3 lysine residues at position 36 (H3K36me3). MRG-1 is important during germline maturation and for safeguarding the germ cell identity. However, it lacks enzymatic activity and depends on protein-protein interaction to cooperate with other factors to regulate chromatin. To elucidate the variety of MRG-1 interaction partners we performed in-depth protein-protein interaction analysis using immunoprecipitations coupled with mass-spectrometry. Besides previously described and novel interactions with other proteins, we also detected a strong association with the Small Ubiquitin-like Modifier (SUMO). Since SUMO is known to be attached to proteins in order to modulate the target proteins activity we assessed whether MRG-1 is post-translationally modified by SUMOylation. Notably, we provide evidence that MRG-1 is indeed SUMOylated and that this post-translational modification influences the chromatin-binding profile of MRG-1 in the C. elegans genome. Our presented study hints towards an important role of SUMOylation in the context of epigenetic regulation via the chromodomain protein MRG-1, which may be a conserved phenomenon also in mammalian species.

Human immunodeficiency virus (HIV) replication is strongly dependent upon a programmed ribosomal frameshift. Here we investigate the relationships between the thermodynamic stability of the HIV type 1 (HIV-1) RNA frameshift site stem-loop, frameshift efficiency, and infectivity, using pseudotyped HIV-1 and HEK293T cells. The data reveal a strong correlation between frameshift efficiency and local, but not overall, RNA thermodynamic stability. Mutations that modestly increase the local stability of the frameshift site RNA stem-loop structure increase frameshift efficiency 2-fold to 3-fold in cells. Thus, frameshift efficiency is determined by the strength of the thermodynamic barrier encountered by the ribosome. These data agree with previous in vitro measurements, suggesting that there are no virus- or host-specific factors that modulate frameshifting. The data also indicate that there are no sequence-specific requirements for the frameshift site stem-loop. A linear correlation between Gag-polymerase (Gag-Pol) levels in cells and levels in virions supports the idea of a stochastic virion assembly mechanism. We further demonstrate that the surrounding genomic RNA secondary structure influences frameshift efficiency and that a mutation that commonly arises in response to protease inhibitor therapy creates a functional but inefficient secondary slippery site. Finally, HIV-1 mutants with enhanced frameshift efficiencies are significantly less infectious, suggesting that compounds that increase frameshift efficiency by as little as 2-fold may be effective at suppressing HIV-1 replication. IMPORTANCE HIV, like many retroviruses, utilizes a −1 programmed ribosomal frameshift to generate viral enzymes in the form of a Gag-Pol polyprotein precursor. Thus, frameshifting is essential for viral replication. Here, we utilized a panel of mutant HIV strains to demonstrate that in cells, frameshifting efficiency is correlated with the stability of the local thermodynamic barrier to ribosomal translocation. Increasing the stability of the frameshift site RNA increases the frameshift efficiency 2-fold to 3-fold. Mutant viruses with increased frameshift efficiencies have significantly reduced infectivity. These data suggest that this effect might be exploited in the development of novel antiviral strategies.