A time-resolved proteomic and prognostic map of COVID-19

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease

Climate change challenges, plant science solutions

Climate change is a defining challenge of the 21st century, and this decade is a critical time for action to mitigate the worst effects on human populations and ecosystems. Plant science can play an important role in developing crops with enhanced resilience to harsh conditions (e.g. heat, drought, salt stress, flooding, disease outbreaks) and engineering efficient carbon-capturing and carbon-sequestering plants. Here, we present examples of research being conducted in these areas and discuss challenges and open questions as a call to action for the plant science community

Genetic loci and prioritization of genes for kidney function decline derived from a meta-analysis of 62 longitudinal genome-wide association studies

Estimated glomerular filtration rate (eGFR) reflects kidney function. Progressive eGFR-decline can lead to kidney failure, necessitating dialysis or transplantation. Hundreds of loci from genome-wide association studies (GWAS) for eGFR help explain population cross section variability. Since the contribution of these or other loci to eGFR-decline remains largely unknown, we derived GWAS for annual eGFR-decline and meta-analyzed 62 longitudinal studies with eGFR assessed twice over time in all 343,339 individuals and in high-risk groups. We also explored different covariate adjustment. Twelve genome-wide significant independent variants for eGFR-decline unadjusted or adjusted for eGFR-baseline (11 novel, one known for this phenotype), including nine variants robustly associated across models were identified. All loci for eGFR-decline were known for cross-sectional eGFR and thus distinguished a subgroup of eGFR loci. Seven of the nine variants showed variant-by-age interaction on eGFR cross section (further about 350,000 individuals), which linked genetic associations for eGFR-decline with age-dependency of genetic cross-section associations. Clinically important were two to four-fold greater genetic effects on eGFR-decline in high-risk subgroups. Five variants associated also with chronic kidney disease progression mapped to genes with functional in-silico evidence (UMOD, SPATA7, GALNTL5, TPPP). An unfavorable versus favorable nine-variant genetic profile showed increased risk odds ratios of 1.35 for kidney failure (95% confidence intervals 1.03-1.77) and 1.27 for acute kidney injury (95% confidence intervals 1.08-1.50) in over 2000 cases each, with matched controls). Thus, we provide a large data resource, genetic loci, and prioritized genes for kidney function decline, which help inform drug development pipelines revealing important insights into the age-dependency of kidney function genetics

Differential Expression Levels of Integrin α6 Enable the Selective Identification and Isolation of Atrial and Ventricular Cardiomyocytes

<div><p>Rationale</p><p>Central questions such as cardiomyocyte subtype emergence during cardiogenesis or the availability of cardiomyocyte subtypes for cell replacement therapy require selective identification and purification of atrial and ventricular cardiomyocytes. However, current methodologies do not allow for a transgene-free selective isolation of atrial or ventricular cardiomyocytes due to the lack of subtype specific cell surface markers.</p><p>Methods and Results</p><p>In order to develop cell surface marker-based isolation procedures for cardiomyocyte subtypes, we performed an antibody-based screening on embryonic mouse hearts. Our data indicate that atrial and ventricular cardiomyocytes are characterized by differential expression of integrin α6 (ITGA6) throughout development and in the adult heart. We discovered that the expression level of this surface marker correlates with the intracellular subtype-specific expression of MLC-2a and MLC-2v on the single cell level and thereby enables the discrimination of cardiomyocyte subtypes by flow cytometry. Based on the differential expression of ITGA6 in atria and ventricles during cardiogenesis, we developed purification protocols for atrial and ventricular cardiomyocytes from mouse hearts. Atrial and ventricular identities of sorted cells were confirmed by expression profiling and patch clamp analysis.</p><p>Conclusion</p><p>Here, we introduce a non-genetic, antibody-based approach to specifically isolate highly pure and viable atrial and ventricular cardiomyocytes from mouse hearts of various developmental stages. This will facilitate in-depth characterization of the individual cellular subsets and support translational research applications.</p></div

Gene expression analysis of sorted cells confirms selective enrichment of atrial and ventricular cardiomyocytes.

<p><b>(A)</b> Normalized signal intensities of CM-specific marker genes: general CM-specific <i>Tnnt2</i> and <i>Nkx2-5</i>, ventricle-specific <i>Hey2</i> and <i>Irx4</i>, atrium-specific <i>Nr2f2</i> and <i>Fgf12</i>. Data are expressed as mean ± SD, n = 4. Statistical analysis: ANOVA, Benjamini-Hochberg correction for multiple testing p ≤ 0.05, Tukey post-hoc test *** p ≤ 0.001, ns = not significant. <b>(B)</b> Heat-map shows median-centered log2-transformed signal intensities of selected genes. The color code indicates expression relative to the gene-wise median of all samples. Abbreviation: EL = E15.5 ERBB-2⁺/ITGA6^low, EH = E15.5 ERBB-2⁺/ITGA6^high, PL = P2 ITGA6^low, PH = P2 ITGA6^high.</p

Functional subtype characterization of sorted cells confirms selective enrichment of atrial and ventricular cardiomyocytes.

<p><b>(A)</b> Top graph, typical ventricular-like action potential (AP) of a cell from the EL group. Bottom graph, typical atrial-like AP from a CMs of the EH group. <b>(B)</b> Distribution of the cells in the two sorted groups. <b>(C)</b> Statistical analysis of AP parameters: left, action potential duration at 90% of repolarization (ADP90); mid, maximum rate of rise of the AP (max dV/dt); right, maximum diastolic polarization (MDP). Data are expressed as mean ± SEM. *** p ≤ 0.001 EL vs. EH. <b>(D)</b> Representative voltage ramps recordings from an E15.5 ERBB-2⁺/ITGA6^low CM (left) and an E15.5 ERBB-2⁺/ITGA6^high CM (right) show functional expression of inward and outward current components. Abbreviation: EL = E15.5 ERBB-2⁺/ITGA6^low, EH = E15.5 ERBB-2⁺/ITGA6^high, PL = P2 ITGA6^low, PH = P2 ITGA6^high.</p

List of fold-change values of selected genes with general or subtype-specific expression in mouse cardiomyocytes.

<p>Positive fold-change values indicate a higher abundance in ITGA6^high as compared to ITGA6^low-sorted cells, negative values demonstrate a higher abundance in ITGA6^low-sorted cells in comparison to ITGA6^high. Differential gene expression was assumed for fold-change values ≥ 3.0 or ≤ -3.0.</p><p>List of fold-change values of selected genes with general or subtype-specific expression in mouse cardiomyocytes.</p

Differential expression of ITGA5 and ITGA6 on atrial and ventricular cardiomyocytes.

<p><b>(A)</b> E13.5 whole hearts and mechanically separated atrial and ventricular tissue were co-labeled for ITGA6 or ITGA5 and α-actinin. Histograms, ITGA6 or ITGA5 expression gated on α-actinin+ cells. <b>(B)</b> E13.5 whole-heart preparations co-stained with antibodies against ITGA6 or ITGA5 and MLC-2a or MLC-2v (labeled with AlexaFluor^® 488 goat anti-mouse IgG). Analysis gates set according to the secondary antibody control. Rectangles indicate ITGA6 low (green) and high (red) expressing myocytes. <b>(C)</b> Co-labeling of E11.5 –P2 mouse hearts for ITGA6 and α-actinin.</p