Estimates of gene ensemble noise highlight critical pathways and predict disease severity in H1N1, COVID-19 and mortality in sepsis patients

Finding novel biomarkers for human pathologies and predicting clinical outcomes for patients is challenging. This stems from the heterogeneous response of individuals to disease and is reflected in the inter-individual variability of gene expression responses that obscures differential gene expression analysis. Here, we developed an alternative approach that could be applied to dissect the disease-associated molecular changes. We define gene ensemble noise as a measure that represents a variance for a collection of genes encoding for either members of known biological pathways or subunits of annotated protein complexes and calculated within an individual. The gene ensemble noise allows for the holistic identification and interpretation of gene expression disbalance on the level of gene networks and systems. By comparing gene expression data from COVID-19, H1N1, and sepsis patients we identified common disturbances in a number of pathways and protein complexes relevant to the sepsis pathology. Among others, these include the mitochondrial respiratory chain complex I and peroxisomes. This suggests a Warburg effect and oxidative stress as common hallmarks of the immune host-pathogen response. Finally, we showed that gene ensemble noise could successfully be applied for the prediction of clinical outcome namely, the mortality of patients. Thus, we conclude that gene ensemble noise represents a promising approach for the investigation of molecular mechanisms of pathology through a prism of alterations in the coherent expression of gene circuits

Gene expression variability - the other dimension in transcriptome analysis:the other dimension in transcriptome analysis

Transcriptome sequencing is a powerful technique to study molecular changes that underlie the differences in physiological conditions and disease progression. A typical question that is posed in such studies is finding genes with significant changes between sample groups. In this respect expression variability is regarded as a nuisance factor that is primarily of technical origin and complicates the data analysis. However, it is becoming apparent that the biological variation in gene expression might be an important molecular phenotype that can affect physiological parameters. In this review we explore the recent literature on technical and biological variability in gene expression, sources of expression variability, (epi-) genetic hallmarks, and evolutionary constraints in genes with robust and variable gene expression. We provide an overview of recent findings on effects of external cues, such as diet and aging, on expression variability and on other biological phenomena that can be linked to it. We discuss metrics and tools that were developed for quantification of expression variability and highlight the importance of future studies in this direction. To assist the adoption of expression variability analysis, we also provide a detailed description and computer code, which can easily be utilized by other researchers. We also provide a reanalysis of recently published data to highlight the value of the analysis method

Whole genome sequencing of nearly isogenic WMI and WLI inbred rats identifies genes potentially involved in depression and stress reactivity

The WMI and WLI inbred rats were generated from the stress-prone, and not yet fully inbred, Wistar Kyoto (WKY) strain. These were selected using bi-directional selection for immobility in the forced swim test and were then sib-mated for over 38 generations. Despite the low level of genetic diversity among WKY progenitors, the WMI substrain is significantly more vulnerable to stress relative to the counter-selected WLI strain. Here we quantify numbers and classes of genomic sequence variants distinguishing these substrains with the long term goal of uncovering functional and behavioral polymorphism that modulate sensitivity to stress and depression-like phenotypes. DNA from WLI and WMI was sequenced using Illumina xTen, IonTorrent, and 10X Chromium linked-read platforms to obtain a combined coverage of ~ 100X for each strain. We identified 4,296 high quality homozygous SNPs and indels between the WMI and WLI. We detected high impact variants in genes previously implicated in depression (e.g. Gnat2), depression-like behavior (e.g. Prlr, Nlrp1a), other psychiatric disease (e.g. Pou6f2, Kdm5a, Reep3, Wdfy3), and responses to psychological stressors (e.g. Pigr). High coverage sequencing data confirm that the two substrains are nearly coisogenic. Nonetheless, the small number of sequence variants contributes to numerous well characterized differences including depression-like behavior, stress reactivity, and addiction related phenotypes. These selected substrains are an ideal resource for forward and reverse genetic studies using a reduced complexity cross

Sensitivity kernels for receiver function misfits in a full waveform inversion workflow

Receiver functions have been used for decades to study the Earth's major discontinuities by focusing on converted waves. Deconvolution, which is the mathematical backbone of the method, is assumed to remove the source time function and the far-field dependence on structure, making it a useful method to map the nearby Earth structure and its discontinuities. Ray theory, a plane incoming wavefield, and a sufficiently well-known near-receiver background velocity model are conventionally assumed to map the observations to locations in the subsurface. Many researchers are aware of the shortcoming of these assumptions and several remedies have been proposed for mitigating their consequences. Adjoint tomography with a quasi-exact forward operator is now within reach for most researchers, and we believe is the way forward in receiver function studies. A first step is to calculate adjoint sensitivity kernels for a given misfit function. Here, we derive the adjoint source for a receiver function waveform misfit. Using a spectral element forward code, we have calculated sensitivity kernels for P-to-S converted waves using several 2-D models representing an average crust with an underlying mantle. The kernels show profound differences between P- and S-wave speed sensitivity. The sensitivity to P-wave speed is wide-ranging and related to the scattered P-wavefield which interferes with that of the P-to-S converted wave. The S-wave speed sensitivity is more local and mostly associated to potential locations of P-to-S conversion, although more distant sensitivity is also observed. Notably, there is virtually no sensitivity to impedance. We further observe the well-known trade-off between depth of the discontinuity and wave speed, but find that considering a longer waveform that includes more surface reverberations reduces this trade-off significantly

Modeling Human Cardiac Hypertrophy in Stem Cell-Derived Cardiomyocytes

Cardiac hypertrophy accompanies many forms of cardiovascular diseases. The mechanisms behind the development and regulation of cardiac hypertrophy in the human setting are poorly understood, which can be partially attributed to the lack of a human cardiomyocyte-based preclinical test system recapitulating features of diseased myocardium. The objective of our study is to determine whether human embryonic stem cell-derived cardiomyocytes (hESC-CMs) subjected to mechanical stretch can be used as an adequate in vitro model for studying molecular mechanisms of cardiac hypertrophy. We show that hESC-CMs subjected to cyclic stretch, which mimics mechanical overload, exhibit essential features of a hypertrophic state on structural, functional, and gene expression levels. The presented hESC-CM stretch approach provides insight into molecular mechanisms behind mechanotransduction and cardiac hypertrophy and lays groundwork for the development of pharmacological approaches as well as for discovering potential circulating biomarkers of cardiac dysfunction. In this article, Berezikov, van der Meer, and colleagues used stem cell-derived cardiomyocytes to model human cardiac hypertrophy. Their approach provides novel insights into molecular mechanisms behind mechanotransduction and cardiac hypertrophy and lays groundwork for the development of new pharmacological approaches as well as for discovering new potential circulating biomarkers of cardiac dysfunction

Reduced expression of C/EBPβ-LIP extends health- and lifespan in mice

Ageing is associated with physical decline and the development of age-related diseases such as metabolic disorders and cancer. Few conditions are known that attenuate the adverse effects of ageing, including calorie restriction (CR) and reduced signalling through the mechanistic target of rapamycin complex 1 (mTORC1) pathway. Synthesis of the metabolic transcription factor C/EBPβ-LIP is stimulated by mTORC1, which critically depends on a short upstream open reading frame (uORF) in the Cebpb-mRNA. Here we describe that reduced C/EBPβ-LIP expression due to genetic ablation of the uORF delays the development of age-associated phenotypes in mice. Moreover, female C/EBPβΔuORF mice display an extended lifespan. Since LIP levels increase upon aging in wild type mice, our data reveal an important role for C/EBPβ in the aging process and suggest that restriction of LIP expression sustains health and fitness. Thus, therapeutic strategies targeting C/EBPβ-LIP may offer new possibilities to treat age-related diseases and to prolong healthspan

Identification of an RNA Polymerase III Regulator Linked to Disease-Associated Protein Aggregation.

Protein aggregation is associated with age-related neurodegenerative disorders, such as Alzheimer's and polyglutamine diseases. As a causal relationship between protein aggregation and neurodegeneration remains elusive, understanding the cellular mechanisms regulating protein aggregation will help develop future treatments. To identify such mechanisms, we conducted a forward genetic screen in a C. elegans model of polyglutamine aggregation and identified the protein MOAG-2/LIR-3 as a driver of protein aggregation. In the absence of polyglutamine, MOAG-2/LIR-3 regulates the RNA polymerase III-associated transcription of small non-coding RNAs. This regulation is lost in the presence of polyglutamine, which mislocalizes MOAG-2/LIR-3 from the nucleus to the cytosol. We then show biochemically that MOAG-2/LIR-3 can also catalyze the aggregation of polyglutamine-expanded huntingtin. These results suggest that polyglutamine can induce an aggregation-promoting activity of MOAG-2/LIR-3 in the cytosol. The concept that certain aggregation-prone proteins can convert other endogenous proteins into drivers of aggregation and toxicity adds to the understanding of how cellular homeostasis can be deteriorated in protein misfolding diseases

Viral load is strongly associated with length of stay in adults hospitalised with viral acute respiratory illness

Background: respiratory viruses are detectable in a large proportion of adults hospitalised with acute respiratory illness. For influenza and other viruses there is evidence that viral load and persistence are associated with certain clinical outcomes but it is not known if there is an association between viral load and hospital length of stay. Methods: 306 adults hospitalised with viral acute respiratory illness were studied. Associations between viral load and length of stay were examined. Multiple linear regression analysis was performed to control for age, comorbidity, influenza vaccine status, duration of illness prior to hospitalisation, bacterial co-infection, clinical group and virus subtype.Results: high viral load was associated with a longer duration of hospitalisation for all patients (p &lt;0.0001). This remained significant across all virus types and clinical groups and when adjusted for age, comorbidity, duration of illness prior to hospitalisation, bacterial co-infection and other factors. Conclusions: high viral loads are associated with prolonged hospital length of stay in adults with viral acute respiratory illness. This further supports existing evidence demonstrating that viral acute respiratory illness is a viral load driven process and suggests that viral load could be used in clinical practise to predict prolonged hospitalisation and prioritise antivirals. International Standard Randomised Controlled Trial Number (ISRCTN): 21521552<br/