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Abstract Background Visualization approaches transform high-dimensional data from single cell RNA sequencing (scRNA-seq) experiments into two-dimensional plots that are used for analysis of cell relationships, and as a means of reporting biological insights. Yet, many standard approaches generate visuals that suffer from overplotting, lack of quantitative information, and distort global and local properties of biological patterns relative to the original high-dimensional space. Results We present scBubbletree, a new, scalable method for visualization of scRNA-seq data. The method identifies clusters of cells of similar transcriptomes and visualizes such clusters as “bubbles” at the tips of dendrograms (bubble trees), corresponding to quantitative summaries of cluster properties and relationships. scBubbletree stacks bubble trees with further cluster-associated information in a visually easily accessible way, thus facilitating quantitative assessment and biological interpretation of scRNA-seq data. We demonstrate this with large scRNA-seq data sets, including one with over 1.2 million cells. Conclusions To facilitate coherent quantification and visualization of scRNA-seq data we developed the R-package scBubbletree, which is freely available as part of the Bioconductor repository at: https://bioconductor.org/packages/scBubbletree/

Abstract Functional cure for chronic hepatitis B (CHB) remains challenging due to the lack of direct intervention methods for hepatic inflammation. Multi‐omics research offers a promising approach to understand hepatic inflammation mechanisms in CHB. A Bayesian linear model linked gene expression with clinical parameters, and population‐specific expression analysis (PSEA) refined bulk gene expression into specific cell types across different clinical phases. These models were integrated into our analysis of key factors like inflammatory cells, immune activation, T cell exhaustion, chemokines, receptors, and interferon‐stimulated genes (ISGs). Validation through multi‐immune staining in liver specimens from CHB patients bolstered our findings. In CHB patients, increased gene expression related to immune cell activation and migration was noted. Marker genes of macrophages, T cells, immune‐negative regulators, chemokines, and ISGs showed a positive correlation with serum alanine aminotransferase (ALT) levels but not hepatitis B virus DNA levels. The PSEA model confirmed T cells as the source of exhausted regulators, while macrophages primarily contributed to chemokine expression. Upregulated ISGs (ISG20, IFI16, TAP2, GBP1, PSMB9) in the hepatitis phase were associated with T cell and macrophage infiltration and positively correlated with ALT levels. Conversely, another set of ISGs (IFI44, ISG15, IFI44L, IFI6, MX1) mainly expressed by hepatocytes and B cells showed no correlation with ALT levels. Our study presents a multi‐omics analysis integrating bulk transcriptomic, single‐cell sequencing data, and clinical data from CHB patients to decipher the cause of intrahepatic inflammation in CHB. The findings confirm that macrophages secrete chemokines like CCL20, recruiting exhausted T cells into liver tissue; concurrently, hepatocyte innate immunity is suppressed, hindering the antiviral effects of ISGs.

ABSTRACTDuring viral infections, type I interferons (IFN) are induced and play a key role in counteracting initial viral spread. Twelve different human IFNα subtypes exist that bind the same receptor; however, they elicit unique host responses and display distinct potencies of antiviral activities. Our previous studies on human immunodeficiency virus (HIV) and hepatitis B virus (HBV) demonstrated that the clinically used IFNα2 is not the most effective one among the IFNα subtypes. By sequence modeling, we identified a region in helix B with mainly conserved residues at the outside facing IFNAR1, but variable residues at the inside facing the core of IFNα, potentially representing a putative tunable anchor to tune pleiotropic IFN responses. Using site-directed mutagenesis, various mutations were introduced into the IFNα2b backbone targeting sites which are important for binding to IFNAR1 and IFNAR2, the putative tunable anchor, or outside these three regions. Selected mutations were based on sequence differences to high antiviral subtypes IFNα6 and IFNα14. Treatment assays against HBV and HIV identified several critical residues for the antiviral activity of IFNα mainly in the IFNAR1 binding region. Combined mutations of the IFNα2 IFNAR1/2 binding sites or the IFNAR1 binding region plus the putative tunable anchor by those of IFNα14 further augmented activation of different downstream signaling cascades providing a molecular correlate for the enhanced antiviral activity. We describe here important functional residues within IFNα subtype molecules, which enabled us to design novel and innovative drugs that may have the potential to be used in clinical trials against a variety of different viral infections.IMPORTANCEThe potency of interferon (IFN)α to restrict viruses was already discovered in 1957. However, until today, only IFNα2 out of the 12 distinct human IFNα subtypes has been therapeutically used against chronic viral infections. There is convincing evidence that other IFNα subtypes are far more efficient than IFNα2 against many viruses. In order to identify critical antiviral residues within the IFNα subtype sequence, we designed hybrid molecules based on the IFNα2 backbone with individual sequence motifs from the more potent subtypes IFNα6 and IFNα14. In different antiviral assays with HIV or HBV, residues binding to IFNAR1 as well as combinations of residues in the IFNAR1 binding region, the putative tunable anchor, and residues outside these regions were identified to be crucial for the antiviral activity of IFNα. Thus, we designed artificial IFNα molecules, based on the clinically approved IFNα2 backbone, but with highly improved antiviral activity against several viruses.

To understand the biological relevance and mode of action of artificial protein ligands, crystal structures with their protein targets are essential. Here, we describe and investigate all known crystal structures that contain a so-called “molecular tweezer” or one of its derivatives with an attached natural ligand on the respective target protein. The aromatic ring system of these compounds is able to include lysine and arginine side chains, supported by one or two phosphate groups that are attached to the half-moon-shaped molecule. Due to their marked preference for basic amino acids and the fully reversible binding mode, molecular tweezers are able to counteract pathologic protein aggregation and are currently being developed as disease-modifying therapies against neurodegenerative diseases such as Alzheimer’s and Parkinson’s disease. We analyzed the corresponding crystal structures with 14-3-3 proteins in complex with mono- and diphosphate tweezers. Furthermore, we solved crystal structures of two different tweezer variants in complex with the enzyme Δ1-Pyrroline-5-carboxyl-dehydrogenase (P5CDH) and found that the tweezers are bound to a lysine and methionine side chain, respectively. The different binding modes and their implications for affinity and specificity are discussed, as well as the general problems in crystallizing protein complexes with artificial ligands.

The Hedgehog morphogen creates gradients during development, but diffusion alone cannot explain its spatiotemporal dynamics. Hedgehog transport requires binding heparan sulfate sugar chains, and the authors now show that Hedgehogs can spread by interacting with sequential heparan molecules.

The 14-3-3 protein family, one of the first discovered phosphoserine/phosphothreonine binding proteins, has attracted interest not only because of its important role in the cell regulatory processes but also due to its enormous number of interactions with other proteins. Here, we use a computational approach to predict the binding sites of the designed hybrid compound featuring aggregation-induced emission luminophores as a potential supramolecular ligand for 14-3-3ζ in the presence and absence of C-Raf peptides. Our results suggest that the area above and below the central pore of the dimeric 14-3-3ζ protein is the most probable binding site for the ligand. Moreover, we predict that the position of the ligand is sensitive to the presence of phosphorylated C-Raf peptides. With a series of experiments, we confirmed the computational prediction of two C2 related, dominating binding sites on 14-3-3ζ that may bind to two of the supramolecular ligand molecules.

IntroductionThe Hepatitis Delta Virus (HDV) is a defective, single-stranded RNA virusoid encoding for a single protein, the Hepatitis Delta Antigen (HDAg), which requires the hepatitis B virus (HBV) envelope protein (HBsAg) for its transmission. Currently, hepatitis D is the most aggressive form of viral hepatitis and treatment options are limited. Worldwide 12 million people are chronically infected with HDV being at high risk for progression to cirrhosis and development of liver cancer.ObjectivesAlthough it is well established that Mongolia is the country with the highest prevalence of HDV infections, the information on the molecular epidemiology and factors contributing to HDV sequence diversity are largely unclear. The aim of the study was to characterize the sequence diversity of HDV in rural areas from Mongolia and to determine the extent of HLA class I-associated selection pressure.Patients and methodsFrom the HepMongolia cohort from rural areas in Mongolia, 451 HBsAg-positive individuals were selected and anti-HDV, HDV-RNA and the sequence of the large HDAg was determined. For all individuals the HLA class I locus was genotyped. Residues under selection pressure in the presence of individual HLA class I types were identified with the recently published analysis tool HAMdetector.ResultsOf 431 HBsAg positive patients, 281 were anti-HDV positive (65%), and HDV-RNA could be detected in 207 of 281 (74%) of patients. The complete large HDAg was successfully sequenced from 131 samples. Phylogenetic analysis revealed that all Mongolian HDV isolates belong to genotype 1, however, they separate into several different clusters without clear regional association. In turn, from phylogeny there is strong evidence for recent local transmission events. Importantly, we found multiple residues with strong support for HLA class I-associated selection pressure consistent with a functional CD8+ T cell response directed against HDV.ConclusionHDV isolates from Mongolia are highly diverse. The molecular epidemiology suggests circulation of multiple subtypes and provides evidence for ongoing recent transmissions.

IntroductionThreat processing, enabled by threat circuits, is supported by a remarkably conserved neural architecture across mammals. Threatening stimuli relevant for most species include the threat of being attacked by a predator or an aggressive conspecific and the threat of pain. Extensive studies in rodents have associated the threats of pain, predator attack and aggressive conspecific attack with distinct neural circuits in subregions of the amygdala, the hypothalamus and the periaqueductal gray. Bearing in mind the considerable conservation of both the anatomy of these regions and defensive behaviors across mammalian species, we hypothesized that distinct brain activity corresponding to the threats of pain, predator attack and aggressive conspecific attack would also exist in human subcortical brain regions.MethodsForty healthy female subjects underwent fMRI scanning during aversive classical conditioning. In close analogy to rodent studies, threat stimuli consisted of painful electric shocks, a short video clip of an attacking bear and a short video clip of an attacking man. Threat processing was conceptualized as the expectation of the aversive stimulus during the presentation of the conditioned stimulus.ResultsOur results demonstrate differential brain activations in the left and right amygdala as well as in the left hypothalamus for the threats of pain, predator attack and aggressive conspecific attack, for the first time showing distinct threat-related brain activity within the human subcortical brain. Specifically, the threat of pain showed an increase of activity in the left and right amygdala and the left hypothalamus compared to the threat of conspecific attack (pain > conspecific), and increased activity in the left amygdala compared to the threat of predator attack (pain > predator). Threat of conspecific attack revealed heightened activity in the right amygdala, both in comparison to threat of pain (conspecific > pain) and threat of predator attack (conspecific > predator). Finally, for the condition threat of predator attack we found increased activity in the bilateral amygdala and the hypothalamus when compared to threat of conspecific attack (predator > conspecific). No significant clusters were found for the contrast predator attack > pain.ConclusionResults suggest that threat type-specific circuits identified in rodents might be conserved in the human brain.

Background and aimsThere is growing interest in T cell-based immune therapies for a functional cure of chronic HBV infection including check-point inhibition, T cell-targeted vaccines or TCR-grafted effector cells. All these approaches depend on recognition of HLA class I-presented viral peptides. The HBV core region 18-27 is an immunodominant target of CD8+ T cells and represents the prime target for T cell-based therapies. Here, a high-resolution analysis of the core18-27 specific CD8+ T cell and the selected escape pathways was performed.MethodsHLA class I typing and viral sequence analyses were performed for 464 patients with chronic HBV infection. HBV-specific CD8+ T-cell responses against the prototype and epitope variants were characterized by flow cytometry.ResultsConsistent with promiscuous presentation of the core18-27 epitope, antigen-specific T cells were detected in patients carrying HLA-A*02:01, HLA-B*35:01, HLA-B*35:03 or HLA-B*51:01. Sequence analysis confirmed reproducible selection pressure on the core18-27 epitope in the context of these alleles. Interestingly, the selected immune escape pathways depend on the presenting HLA-class I-molecule. Although cross-reactive T cells were observed, some epitope variants achieved functional escape by impaired TCR-interaction or disturbed antigen processing. Of note, selection of epitope variants was exclusively observed in HBeAg negative HBV infection and here, detection of variants associated with significantly greater magnitude of the CD8 T cell response compared to absence of variants.ConclusionThe core18-27 epitope is highly variable and under heavy selection pressure in the context of different HLA class I-molecules. Some epitope variants showed evidence for impaired antigen processing and reduced presentation. Viruses carrying such escape substitutions will be less susceptible to CD8+ T cell responses and should be considered for T cell-based therapy strategies.

Summary: Background: Immune activation is a significant contributor to HIV pathogenesis and disease progression. In virally-suppressed individuals on ART, low-level immune activation has been linked to several non-infectious comorbid diseases. However, studies have not been systematically performed in sub-Saharan Africa and thus the impact of demographics, ART and regional endemic co-infections on immune activation is not known. We therefore comprehensively evaluated in a large multinational African cohort markers for immune activation and its distribution in various settings. Methods: 2747 specimens from 2240 people living with HIV (PLWH) and 477 without HIV from the observational African Cohort Study (AFRICOS) were analyzed for 13 immune parameters. Samples were collected along with medical history, sociodemographic and comorbidity data at 12 HIV clinics across 5 programs in Uganda, Kenya, Tanzania and Nigeria. Data were analyzed with univariate and multivariate methods such as random forests and principal component analysis. Findings: Immune activation was markedly different between PLWH with detectable viral loads, and individuals without HIV across sites. Among viremic PLWH, we found that all immune parameters were significantly correlated with viral load except for IFN-α. The overall inflammatory profile was distinct between men and women living with HIV, in individuals off ART and with HIV viremia. We observed stronger differences in the immune activation profile with increasing viremia. Using machine learning methods, we found that geographic differences contributed to unique inflammatory profiles. We also found that among PLWH, age and the presence of infectious and/or noninfectious comorbidities showed distinct inflammatory patterns, and biomarkers may be used to predict the presence of some comorbidities. Interpretation: Our findings show that chronic immune activation in HIV-1 infection is influenced by HIV viral load, sex, age, region and ART use. These predictors, as well as associations among some biomarkers and coinfections, influence biomarkers associated with noncommunicable diseases. Funding: This work was supported by the President's Emergency Plan for AIDS Relief via a cooperative agreement between the Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., and the U.S. Department of Defense [W81XWH-11-2-0174, W81XWH-18-2-0040]. The investigators have adhered to the policies for protection of human subjects as prescribed in AR 70–25. This article was prepared while Michael A. Eller was employed at Henry M. Jackson Foundation for the Advancement of Military Medicine for the U.S. Military HIV Research Program. The views expressed are those of the authors and should not be construed to represent the positions of the US Army or the Department of Defense. The opinions expressed in this article are the author's own, and do not reflect the view of the National Institutes of Health, the U.S. Department of Health and Human Services, or the U.S. government.

Abstract Background Non-pharmaceutical measures to control the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) should be carefully tuned as they can impose a heavy social and economic burden. To quantify and possibly tune the efficacy of these anti-SARS-CoV-2 measures, we have devised indicators based on the abundant historic and current prevalence data from other respiratory viruses. Methods We obtained incidence data of 17 respiratory viruses from hospitalized patients and outpatients collected by 37 clinics and laboratories between 2010-2020 in Germany. With a probabilistic model for Bayes inference we quantified prevalence changes of the different viruses between months in the pre-pandemic period 2010-2019 and the corresponding months in 2020, the year of the pandemic with noninvasive measures of various degrees of stringency. Results We discovered remarkable reductions δ in rhinovirus (RV) prevalence by about 25% (95% highest density interval (HDI) [−0.35,−0.15]) in the months after the measures against SARS-CoV-2 were introduced in Germany. In the months after the measures began to ease, RV prevalence increased to low pre-pandemic levels, e.g. in August 2020 δ=−0.14 (95% HDI [−0.28,0.12]). Conclusions RV prevalence is negatively correlated with the stringency of anti-SARS-CoV-2 measures with only a short time delay. This result suggests that RV prevalence could possibly be an indicator for the efficiency for these measures. As RV is ubiquitous at higher prevalence than SARS-CoV-2 or other emerging respiratory viruses, it could reflect the efficacy of noninvasive measures better than such emerging viruses themselves with their unevenly spreading clusters.

Survivin’s dual function as apoptosis inhibitor and regulator of cell proliferation is mediated via its interaction with the export receptor CRM1. Here authors report a strategy addressing its dimer interface overlapping with the nuclear export signal, the binding site of CRM1, with advanced supramolecular tweezers for lysine and arginine.

Abstract Host immune control plays a pivotal role in resolving primary hepatitis-B-virus (HBV) infections. The complex interaction between HBV and host immune cells, however, remains unclear. In this study, the transcriptional profiling of specimens from animals infected with woodchuck hepatitis virus (WHV) indicated TLR2 mRNA accumulation as most strongly impacted during WHV infection resolution as compared to other mRNAs. Analysis of blood transcriptional modules demonstrated that monocytes and B-cells were the predominantly activated cell types in animals that showed resolution of infection, which was similar to the response of TLR2-stimulated PBMCs. Further investigation of TLR2-stimulated B-cells pointed at interactions between activated TLR signaling, Akt-mTOR, and glucose metabolic pathways. Moreover, analysis of B-cells from Tlr2−/−, Trif−/−, Myd88−/−, and Trif/Myd88−/− mice challenged with HBV particles indicated B-cell function and glucose metabolism alterations is TLR2-MyD88-mTOR axis dependent. Overall, our study implicates B-cell TLR2 activation in HBV infection resolution.

Abstract Background RNA-Seq, the high-throughput sequencing (HT-Seq) of mRNAs, has become an essential tool for characterizing gene expression differences between different cell types and conditions. Gene expression is regulated by several mechanisms, including epigenetically by post-translational histone modifications which can be assessed by ChIP-Seq (Chromatin Immuno-Precipitation Sequencing). As more and more biological samples are analyzed by the combination of ChIP-Seq and RNA-Seq, the integrated analysis of the corresponding data sets becomes, theoretically, a unique option to study gene regulation. However, technically such analyses are still in their infancy. Results Here we introduce intePareto, a computational tool for the integrative analysis of RNA-Seq and ChIP-Seq data. With intePareto we match RNA-Seq and ChIP-Seq data at the level of genes, perform differential expression analysis between biological conditions, and prioritize genes with consistent changes in RNA-Seq and ChIP-Seq data using Pareto optimization. Conclusion intePareto facilitates comprehensive understanding of high dimensional transcriptomic and epigenomic data. Its superiority to a naive differential gene expression analysis with RNA-Seq and available integrative approach is demonstrated by analyzing a public dataset.

The gut microbiota contributes to maintaining human health and regulating immune responses. Severe COVID-19 illness is associated with a dysregulated pro-inflammatory immune response. The effect of SARS-CoV-2 on altering the gut microbiome and the relevance of the gut microbiome on COVID-19 severity needs to be clarified. In this prospective study, we analyzed the gut microbiome of 212 patients of a tertiary care hospital (117 patients infected with SARS-CoV-2 and 95 SARS-CoV-2 negative patients) using 16S rRNA gene sequencing of the V3-V4 region. Inflammatory markers and immune cells were quantified from blood. The gut microbiome in SARS-CoV-2 infected patients was characterized by a lower bacterial richness and distinct differences in the gut microbiome composition, including an enrichment of the phyla Proteobacteria and Bacteroidetes and a decrease of Actinobacteria compared to SARS-CoV-2 negative patients. The relative abundance of several genera including Bifidobacterium, Streptococcus and Collinsella was lower in SARS-CoV-2 positive patients while the abundance of Bacteroides and Enterobacteriaceae was increased. Higher pro-inflammatory blood markers and a lower CD8+ T cell number characterized patients with severe COVID-19 illness. The gut microbiome of patients with severe/critical COVID-19 exhibited a lower abundance of butyrate-producing genera Faecalibacterium and Roseburia and a reduction in the connectivity of a distinct network of anti-inflammatory genera that was observed in patients with mild COVID-19 illness and in SARS-CoV-2 negative patients. Dysbiosis of the gut microbiome associated with a pro-inflammatory signature may contribute to the hyperinflammatory immune response characterizing severe COVID-19 illness.

Summary: Cancer bioenergetics fuel processes necessary to maintain viability and growth under stress conditions. We hypothesized that cancer metabolism supports the repair of radiation-induced DNA double-stranded breaks (DSBs). We combined the systematic collection of metabolic and radiobiological data from a panel of irradiated cancer cell lines with mathematical modeling and identified a common metabolic response with impact on the DSB repair kinetics, including a mitochondrial shutdown followed by compensatory glycolysis and resumption of mitochondrial function. Combining ionizing radiation (IR) with inhibitors of the compensatory glycolysis or mitochondrial respiratory chain slowed mitochondrial recovery and DNA repair kinetics, offering an opportunity for therapeutic intervention. Mathematical modeling allowed us to generate new hypotheses on general and individual mechanisms of the radiation response with relevance to DNA repair and on metabolic vulnerabilities induced by cancer radiotherapy. These discoveries will guide future mechanistic studies for the discovery of metabolic targets for overcoming intrinsic or therapy-induced radioresistance.

Abstract Background The East Asian mosquito species Aedes koreicus was recorded out of its native range for the first time in Belgium in 2008. Since then, several other European populations or single individuals have been observed throughout Europe with reports from Italy, Switzerland, European Russia, Slovenia, Germany and Hungary. The Italian population seems to be the only one that is expanding rapidly, so the Swiss population very likely derives from it. Results In a surveillance program for invasive mosquito species, a single larva of Ae. koreicus was found in a cemetery vase in 2016 in the city of Wiesbaden, Germany. In the following year the finding was confirmed and an established population could be proven over an area of about 50 km2. The morphological identification of the first larva was confirmed by sequencing of a region within the nad4 sequence. A study of adult females showed that the morphological characteristics of this population are not identical to the populations from Belgium and Italy. The eggs and larvae were found together with Aedes j. japonicus in the same breeding sites and ovitraps, as well as with other indigenous mosquito species such as Culex pipiens/Culex torrentium, Aedes geniculatus and Anopheles plumbeus. Conclusions Since the newly discovered population in Germany shows different morphological characteristics to the populations in Belgium and Italy, it seems to originate from an independent introduction. It remains unknown how the introduction took place. A further spread similar to the one in northern Italy can be assumed for the future due to similar climatic conditions.

BackgroundThe pathogenesis of COVID-19 emerges as complex, with multiple factors leading to injury of different organs. Some of the studies on aspects of SARS-CoV-2 cell entry and innate immunity have produced seemingly contradictory claims. In this situation, a comprehensive comparative analysis of a large number of related datasets from several studies could bring more clarity, which is imperative for therapy development.MethodsWe therefore performed a comprehensive comparative study, analyzing RNA-Seq data of infections with SARS-CoV-2, SARS-CoV and MERS-CoV, including data from different types of cells as well as COVID-19 patients. Using these data, we investigated viral entry routes and innate immune responses.Results and ConclusionFirst, our analyses support the existence of cell entry mechanisms for SARS and SARS-CoV-2 other than the ACE2 route with evidence of inefficient infection of cells without expression of ACE2; expression of TMPRSS2/TPMRSS4 is unnecessary for efficient SARS-CoV-2 infection with evidence of efficient infection of A549 cells transduced with a vector expressing human ACE2. Second, we find that innate immune responses in terms of interferons and interferon simulated genes are strong in relevant cells, for example Calu3 cells, but vary markedly with cell type, virus dose, and virus type.

Classifiers are often tested on relatively small data sets, which should lead to uncertain performance metrics. Nevertheless, these metrics are usually taken at face value. We present an approach to quantify the uncertainty of classification performance metrics, based on a probability model of the confusion matrix. Application of our approach to classifiers from the scientific literature and a classification competition shows that uncertainties can be surprisingly large and limit performance evaluation. In fact, some published classifiers may be misleading. The application of our approach is simple and requires only the confusion matrix. It is agnostic of the underlying classifier. Our method can also be used for the estimation of sample sizes that achieve a desired precision of a performance metric.

Early and strong production of IFN-I by dendritic cells is important to control vesicular stomatitis virus (VSV), however mechanisms which explain this cell-type specific innate immune activation remain to be defined. Here, using a genome wide association study (GWAS), we identified Integrin alpha-E (Itgae, CD103) as a new regulator of antiviral IFN-I production in a mouse model of vesicular stomatitis virus (VSV) infection. CD103 was specifically expressed by splenic conventional dendritic cells (cDCs) and limited IFN-I production in these cells during VSV infection. Mechanistically, CD103 suppressed AKT phosphorylation and mTOR activation in DCs. Deficiency in CD103 accelerated early IFN-I in cDCs and prevented death in VSV infected animals. In conclusion, CD103 participates in regulation of cDC specific IFN-I induction and thereby influences immune activation after VSV infection.

Background: Cases of excessive neutrophil counts in the blood in severe coronavirus disease (COVID-19) patients have drawn significant attention. Neutrophil infiltration was also noted on the pathological findings from autopsies. It is urgent to clarify the pathogenesis of neutrophils leading to severe pneumonia in COVID-19.Methods: A retrospective analysis was performed on 55 COVID-19 patients classified as mild (n = 22), moderate (n = 25), and severe (n = 8) according to the Guidelines released by the National Health Commission of China. Trends relating leukocyte counts and lungs examined by chest CT scan were quantified by Bayesian inference. Transcriptional signatures of host immune cells of four COVID19 patients were analyzed by RNA sequencing of lung specimens and BALF.Results: Neutrophilia occurred in 6 of 8 severe patients at 7–19 days after symptom onset, coinciding with lesion progression. Increasing neutrophil counts paralleled lesion CT values (slope: 0.8 and 0.3–1.2), reflecting neutrophilia-induced lung injury in severe patients. Transcriptome analysis revealed that neutrophil activation was correlated with 17 neutrophil extracellular trap (NET)-associated genes in COVID-19 patients, which was related to innate immunity and interacted with T/NK/B cells, as supported by a protein–protein interaction network analysis.Conclusion: Excessive neutrophils and associated NETs could explain the pathogenesis of lung injury in COVID-19 pneumonia.

Microorganisms have evolved specific cell surface molecules that enable discrimination between cells from the same and from a different kind. Here, we investigate the role of Flo11-type cell surface adhesins from social yeasts in kin discrimination. We measure the adhesion forces mediated by Flo11A-type domains using single-cell force spectroscopy, quantify Flo11A-based cell aggregation in populations and determine the Flo11A-dependent segregation of competing yeast strains in biofilms. We find that Flo11A domains from diverse yeast species confer remarkably strong adhesion forces by establishing homotypic interactions between single cells, leading to efficient cell aggregation and biofilm formation in homogenous populations. Heterotypic interactions between Flo11A domains from different yeast species or Saccharomyces cerevisiae strains confer weak adhesive forces and lead to efficient strain segregation in heterogenous populations, indicating that in social yeasts Flo11A-mediated cell adhesion is a major mechanism for kin discrimination at species and sub-species levels. These findings, together with our structure and mutation analysis of selected Flo11A domains, provide a rationale of how cell surface receptors have evolved in microorganisms to mediate kin discrimination.

Atonal homolog 8 (Atoh8) is a transcription factor of the basic helix-loop-helix (bHLH) protein family, which is expressed in the cartilaginous elements of endochondral bones. To analyze its function during chondrogenesis we deleted Atoh8 in mice using a chondrocyte- (Atoh8flox/flox;Col2a1-Cre) and a germline- (Atoh8flox/flox;Prx1-Crefemale) specific Cre allele. In both strains, Atoh8 deletion leads to a reduced skeletal size of the axial and appendicular bones, but the stages of phenotypic manifestations differ. While we observed obviously shortened bones in Atoh8flox/flox;Col2a1-Cre mice only postnatally, the bones of Atoh8flox/flox;Prx1-Crefemale mice are characterized by a reduced bone length already at prenatal stages. Detailed histological and molecular investigations revealed reduced zones of proliferating and hypertrophic chondrocytes. In addition, Atoh8 deletion identified Atoh8 as a positive regulator of chondrocyte proliferation. As increased Atoh8 expression is found in the region of prehypertrophic chondrocytes where the expression of Ihh, a main regulator of chondrocyte proliferation and differentiation, is induced, we investigated a potential interaction of Atoh8 function and Ihh signaling. By activating Ihh signaling with Purmorphamine we demonstrate that Atoh8 regulates chondrocyte proliferation in parallel or downstream of Ihh signaling while it acts on the onset of hypertrophy upstream of Ihh likely by modulating Ihh expression levels.

Self-adaptive vibration energy harvesters convert the kinetic energy from vibration sources into electrical energy and continuously adapt their resonance frequency to the vibration frequency. Only when the two frequencies match can the system harvest energy efficiently. The harvesting of vibration sources with a time-variant frequency therefore requires self-adaptive vibration harvesting systems without human intervention. This work presents a self-adaptive energy harvesting system that works completely self-sufficiently. Using magnetic forces, the axial load on a bending beam is changed and thus the resonance frequency is set. The system achieves a relative tuning range of 23% at a center frequency of 36.4 Hz. Within this range, the resonance frequency of the harvester can be set continuously and precisely. With a novel optimized method for frequency measurement and with customized electronics, the system only needs 22 µW to monitor the external vibration frequency and is therefore also suitable for environments with low vibration amplitudes. The system was verified on a vibrational test bench and can easily be tailored to a specific vibration source.

We introduce an Interaction- and Trade-off-based Eco-Evolutionary Model (ITEEM), in which species are competing in a well-mixed system, and their evolution in interaction trait space is subject to a life-history trade-off between replication rate and competitive ability. We demonstrate that the shape of the trade-off has a fundamental impact on eco-evolutionary dynamics, as it imposes four phases of diversity, including a sharp phase transition. Despite its minimalism, ITEEM produces a remarkable range of patterns of eco-evolutionary dynamics that are observed in experimental and natural systems. Most notably we find self-organization towards structured communities with high and sustained diversity, in which competing species form interaction cycles similar to rock-paper-scissors games.

Cell fate determination during development often requires morphogen transport from producing to distant responding cells. Hedgehog (Hh) morphogens present a challenge to this concept, as all Hhs are synthesized as terminally lipidated molecules that form insoluble clusters at the surface of producing cells. While several proposed Hh transport modes tie directly into these unusual properties, the crucial step of Hh relay from producing cells to receptors on remote responding cells remains unresolved. Using wing development in Drosophila melanogaster as a model, we show that Hh relay and direct patterning of the 3–4 intervein region strictly depend on proteolytic removal of lipidated N-terminal membrane anchors. Site-directed modification of the N-terminal Hh processing site selectively eliminated the entire 3–4 intervein region, and additional targeted removal of N-palmitate restored its formation. Hence, palmitoylated membrane anchors restrict morphogen spread until site-specific processing switches membrane-bound Hh into bioactive forms with specific patterning functions.

Background: Type I interferon (IFN-I) predisposes to bacterial superinfections, an important problem during viral infection or treatment with interferon-alpha (IFN-α). IFN-I-induced neutropenia is one reason for the impaired bacterial control; however there is evidence that more frequent bacterial infections during IFN-α-treatment occur independently of neutropenia. Methods: We analyzed in a mouse model, whether Pseudomonas aeruginosa control is influenced by co-infection with the lymphocytic choriomeningitis virus (LCMV). Bacterial titers, numbers of neutrophils and the gene-expression of liver-lysozyme-2 were determined during a 24 hours systemic infection with P. aeruginosa in wild-type and Ifnar-/- mice under the influence of LCMV or poly(I:C). Results: Virus-induced IFN-I impaired the control of Pseudomonas aeruginosa. This was associated with neutropenia and loss of lysozyme-2-expression in the liver, which had captured P. aeruginosa. A lower release of IFN-I by poly(I:C)-injection also impaired the bacterial control in the liver and reduced the expression of liver-lysozyme-2. Low concentration of IFN-I after infection with a virulent strain of P. aeruginosa alone impaired the bacterial control and reduced lysozyme-2-expression in the liver as well. Conclusion: We found that during systemic infection with P. aeruginosa Kupffer cells quickly controlled the bacteria in cooperation with neutrophils. Upon LCMV-infection this cooperation was disturbed.

The community, the assemblage of organisms co-existing in a given space and time, has the potential to become one of the unifying concepts of biology, especially with the advent of high-throughput sequencing experiments that reveal genetic diversity exhaustively. In this spirit we show that a tool from community ecology, the Rank Abundance Distribution (RAD), can be turned by the new MaxRank normalization method into a generic, expressive descriptor for quantitative comparison of communities in many areas of biology. To illustrate the versatility of the method, we analyze RADs from various generalized communities, i.e. assemblages of genetically diverse cells or organisms, including human B cells, gut microbiomes under antibiotic treatment and of different ages and countries of origin, and other human and environmental microbial communities. We show that normalized RADs enable novel quantitative approaches that help to understand structures and dynamics of complex generalized communities.

Specific selection pressures often lead to specifically mutated genomes. The open source software SeqFeatR has been developed to identify associations between mutation patterns in biological sequences and specific selection pressures ("features"). For instance, SeqFeatR has been used to discover in viral protein sequences new T cell epitopes for hosts of given HLA types. SeqFeatR supports frequentist and Bayesian methods for the discovery of statistical sequence-feature associations. Moreover, it offers novel ways to visualize results of the statistical analyses and to relate them to further properties. In this article we demonstrate various functions of SeqFeatR with real data. The most frequently used set of functions is also provided by a web server. SeqFeatR is implemented as R package and freely available from the R archive CRAN (http://cran.r-project.org/web/packages/SeqFeatR/index.html). The package includes a tutorial vignette. The software is distributed under the GNU General Public License (version 3 or later). The web server URL is https://seqfeatr.zmb.uni-due.de.

Stefan Franke, Julia Herfurth, Daniel HoffmannDepartment of Bioinformatics/Centre for Medical Biotechnology, University of Duisburg-Essen, Essen, GermanyAbstract: Ca2+-ions have a range of affinities to different proteins, depending on the various functions of these proteins. This makes the determination of Ca2+-protein affinities an interesting subject for functional studies. We have investigated the performance of two methods – Fold-X and AutoDock vina – in the prediction of Ca2+-protein affinities. Both methods, although based on different energy functions, showed virtually the same correlation with experimental affinities. Guided by insight from experiment, we further derived a simple linear model based on thesolvent accessible surface of Ca2+ that had practically the same performance in terms of absolute errors as the more complex docking methods.Keywords: metal ions, binding, free energy, crystal structure, solvent accessible surface

Many macromolecules, namely proteins, show functional substructures or epitopes defined by characteristic spatial arrangements of groups of specific atoms or residues. The identification of such substructures in a set of macromolecular 3D-structures solves an important problem in molecular biology as it allows the assignment of functions to molecular moieties and thus opens the possibility of a mechanistic understanding of molecular function. We have devised an algorithm that models a functional epitope formed by a group of atoms or residues as set of points in cartesian space with associated functional properties. The algorithm searches for similar epitopes in a database of structures by an efficient multistage comparison of distance sets in the epitope and in the structures from the database. The search results in a list of optimal matches and corresponding optimal superpositions of query epitope and matching epitopes from the database. The algorithm is discussed against the background of related approaches, and it is successfully tested in three application scenarios: global match of two homologous proteins, search for an epitope on a homologous protein, and finding matching epitopes in a protein database.

ObjectivesWe analysed the impact of different parameters on genotypic tropism testing related to clinical outcome prediction in 108 patients on maraviroc (MVC) treatment.Methods87 RNA and 60 DNA samples were used. The viral tropism was predicted using the geno2pheno[coreceptor] and T-CUP tools with FPR cut-offs ranging from 1%-20%. Additionally, 27 RNA and 28 DNA samples were analysed in triplicate, 43 samples with the ESTA assay and 45 with next-generation sequencing. The influence of the genotypic susceptibility score (GSS) and 16 MVC-resistance mutations on clinical outcome was also studied.ResultsConcordance between single-amplification testing compared to ESTA and to NGS was in the order of 80%. Concordance with NGS was higher at lower FPR cut-offs. Detection of baseline R5 viruses in RNA and DNA samples by all methods significantly correlated with treatment success, even with FPR cut-offs of 3.75%-7.5%. Triple amplification did not improve the prediction value but reduced the number of patients eligible for MVC. No influence of the GSS or MVC-resistance mutations but adherence to treatment, on the clinical outcome was detected.ConclusionsProviral DNA is valid to select candidates for MVC treatment. FPR cut-offs of 5%-7.5% and single amplification from RNA or DNA would assure a safe administration of MVC without excluding many patients who could benefit from this drug. In addition, the new prediction system T-CUP produced reliable results.

Multivariate biomarkers that can predict the effectiveness of targeted therapy in individual patients are highly desired. Previous biomarker discovery studies have largely focused on the identification of single biomarker signatures, aimed at maximizing prediction accuracy. Here, we present a different approach that identifies multiple biomarkers by simultaneously optimizing their predictive power, number of features, and proximity to the drug target in a protein-protein interaction network. To this end, we incorporated NSGA-II, a fast and elitist multi-objective optimization algorithm that is based on the principle of Pareto optimality, into the biomarker discovery workflow. The method was applied to quantitative phosphoproteome data of 19 non-small cell lung cancer (NSCLC) cell lines from a previous biomarker study. The algorithm successfully identified a total of 77 candidate biomarker signatures predicting response to treatment with dasatinib. Through filtering and similarity clustering, this set was trimmed to four final biomarker signatures, which then were validated on an independent set of breast cancer cell lines. All four candidates reached the same good prediction accuracy (83%) as the originally published biomarker. Although the newly discovered signatures were diverse in their composition and in their size, the central protein of the originally published signature - integrin β4 (ITGB4) - was also present in all four Pareto signatures, confirming its pivotal role in predicting dasatinib response in NSCLC cell lines. In summary, the method presented here allows for a robust and simultaneous identification of multiple multivariate biomarkers that are optimized for prediction performance, size, and relevance.

High throughput sequencing (HTSeq) of small ribosomal subunit amplicons has the potential for a comprehensive characterization of microbial community compositions, down to rare species. However, the error-prone nature of the multi-step experimental process requires that the resulting raw sequences are subjected to quality control procedures. These procedures often involve an abundance cutoff for rare sequences or clustering of sequences, both of which limit genetic resolution. Here we propose a simple experimental protocol that retains the high genetic resolution granted by HTSeq methods while effectively removing many low abundance sequences that are likely due to PCR and sequencing errors. According to this protocol, we split samples and submit both halves to independent PCR and sequencing runs. The resulting sequence data is graphically and quantitatively characterized by the discordance between the two experimental branches, allowing for a quick identification of problematic samples. Further, we discard sequences that are not found in both branches ("AmpliconDuo filter"). We show that the majority of sequences removed in this way, mostly low abundance but also some higher abundance sequences, show features expected from random modifications of true sequences as introduced by PCR and sequencing errors. On the other hand, the filter retains many low abundance sequences observed in both branches and thus provides a more reliable census of the rare biosphere. We find that the AmpliconDuo filter increases biological resolution as it increases apparent community similarity between biologically similar communities, while it does not affect apparent community similarities between biologically dissimilar communities. The filter does not distort overall apparent community compositions. Finally, we quantitatively explain the effect of the AmpliconDuo filter by a simple mathematical model.

Sonic Hedgehog (Shh) is a representative of the evolutionary closely related class of Hedgehog proteins that have essential signaling functions in animal development. The N-terminal domain (ShhN) is also assigned to the group of LAS proteins (LAS = Lysostaphin type enzymes, D-Ala-D-Ala metalloproteases, Sonic Hedgehog), of which all members harbor a structurally well-defined Zn2+ center; however, it is remarkable that ShhN so far is the only LAS member without proven peptidase activity. Another unique feature of ShhN in the LAS group is a double-Ca2+ center close to the zinc. We have studied the effect of these calcium ions on ShhN structure, dynamics, and interactions. We find that the presence of calcium has a marked impact on ShhN properties, with the two calcium ions having different effects. The more strongly bound calcium ion significantly stabilizes the overall structure. Surprisingly, the binding of the second calcium ion switches the putative catalytic center from a state similar to LAS enzymes to a state that probably is catalytically inactive. We describe in detail the mechanics of the switch, including the effect on substrate co-ordinating residues and on the putative catalytic water molecule. The properties of the putative substrate binding site suggest that ShhN could degrade other ShhN molecules, e.g. by cleavage at highly conserved glycines in ShhN. To test experimentally the stability of ShhN against autodegradation, we compare two ShhN mutants in vitro: (1) a ShhN mutant unable to bind calcium but with putative catalytic center intact, and thus, according to our hypothesis, a constitutively active peptidase, and (2) a mutant carrying additionally mutation E177A, i.e., with the putative catalytically active residue knocked out. The in vitro results are consistent with ShhN being a cannibalistic zinc-peptidase. These experiments also reveal that the peptidase activity depends on pH.

With the introduction of omics-technologies such as transcriptomics and proteomics, numerous methods for the reliable identification of significantly regulated features (genes, proteins, etc.) have been developed. Experimental practice requires these tests to successfully deal with conditions such as small numbers of replicates, missing values, non-normally distributed expression levels, and non-identical distributions of features. With the MeanRank test we aimed at developing a test that performs robustly under these conditions, while favorably scaling with the number of replicates. The test proposed here is a global one-sample location test, which is based on the mean ranks across replicates, and internally estimates and controls the false discovery rate. Furthermore, missing data is accounted for without the need of imputation. In extensive simulations comparing MeanRank to other frequently used methods, we found that it performs well with small and large numbers of replicates, feature dependent variance between replicates, and variable regulation across features on simulation data and a recent two-color microarray spike-in dataset. The tests were then used to identify significant changes in the phosphoproteomes of cancer cells induced by the kinase inhibitors erlotinib and 3-MB-PP1 in two independently published mass spectrometry-based studies. MeanRank outperformed the other global rank-based methods applied in this study. Compared to the popular Significance Analysis of Microarrays and Linear Models for Microarray methods, MeanRank performed similar or better. Furthermore, MeanRank exhibits more consistent behavior regarding the degree of regulation and is robust against the choice of preprocessing methods. MeanRank does not require any imputation of missing values, is easy to understand, and yields results that are easy to interpret. The software implementing the algorithm is freely available for academic and commercial use.

The epidemiology of HIV-1 in China has unique features that may have led to unique viral strains. We therefore tested the hypothesis that it is possible to find distinctive patterns in HIV-1 genomes sampled in China. Using a rule inference algorithm we could indeed extract from sequences of the third variable loop (V3) of HIV-1 gp120 a set of 14 signature patterns that with 89% accuracy distinguished Chinese from non-Chinese sequences. These patterns were found to be specific to HIV-1 subtype, i.e. sequences complying with pattern 1 were of subtype B, pattern 2 almost exclusively covered sequences of subtype 01_AE, etc. We then analyzed the first of these signature patterns in depth, namely that L and W at two V3 positions are specifically occurring in Chinese sequences of subtype B/B' (3% false positives). This pattern was found to be in agreement with the phylogeny of HIV-1 of subtype B inside and outside of China. We could neither reject nor convincingly confirm that the pattern is stabilized by immune escape. For further interpretation of the signature pattern we used the recently developed measure of Direct Information, and in this way discovered evidence for physical interactions between V2 and V3. We conclude by a discussion of limitations of signature patterns, and the applicability of the approach to other genomic regions and other countries.

IntroductionVarious conditions of liver disease and the downsides of liver biopsy call for a non-invasive option to assess liver fibrosis. A non-invasive score would be especially useful to identify patients with slow advancing fibrotic processes, as in Non-Alcoholic Fatty Liver Disease (NAFLD), which should undergo histological examination for fibrosis.Patients/methodsClassic liver serum parameters, hyaluronic acid (HA) and cell death markers of 126 patients undergoing bariatric surgery for morbid obesity were analyzed by machine learning techniques (logistic regression, k-nearest neighbors, linear support vector machines, rule-based systems, decision trees and random forest (RF)). Specificity, sensitivity and accuracy of the evaluated datasets to predict fibrosis were assessed.ResultsNone of the single parameters (ALT, AST, M30, M60, HA) did differ significantly between patients with a fibrosis score 1 or 2. However, combining these parameters using RFs reached 79% accuracy in fibrosis prediction with a sensitivity of more than 60% and specificity of 77%. Moreover, RFs identified the cell death markers M30 and M65 as more important for the decision than the classic liver parameters.ConclusionOn the basis of serum parameters the generation of a fibrosis scoring system seems feasible, even when only marginally fibrotic tissue is available. Prospective evaluation of novel markers, i.e. cell death parameters, should be performed to identify an optimal set of fibrosis predictors.

Magnetic resonance imaging (MRI) offers a non-radioactive alternative for the non-invasive detection of tumours. Low molecular weight MRI contrast agents currently in clinical use suffer either from a lack of specificity for tumour tissue or from low relaxivity and thus low contrast amplification. In this study, we present the newly designed two domain fusion protein Zarvin, which is able to bind to therapeutic IgG antibodies suitable for targeting, while facilitating contrast enhancement through high affinity binding sites for Gd(3+). We show that the Zarvin fold is stable under serum conditions, specifically targets a cancer cell-line when bound to the Cetuximab IgG, and allows for imaging with high relaxivity, a property that would be advantageous for the detection of small tumours and metastases at 1.5 or 3 T.

Human Immunodeficiency Virus 1 uses for entry into host cells a receptor (CD4) and one of two co-receptors (CCR5 or CXCR4). Recently, a new class of antiretroviral drugs has entered clinical practice that specifically bind to the co-receptor CCR5, and thus inhibit virus entry. Accurate prediction of the co-receptor used by the virus in the patient is important as it allows for personalized selection of effective drugs and prognosis of disease progression. We have investigated whether it is possible to predict co-receptor usage accurately by analyzing the amino acid sequence of the main determinant of co-receptor usage, i.e., the third variable loop V3 of the gp120 protein. We developed a two-level machine learning approach that in the first level considers two different properties important for protein-protein binding derived from structural models of V3 and V3 sequences. The second level combines the two predictions of the first level. The two-level method predicts usage of CXCR4 co-receptor for new V3 sequences within seconds, with an area under the ROC curve of 0.937+/-0.004. Moreover, it is relatively robust against insertions and deletions, which frequently occur in V3. The approach could help clinicians to find optimal personalized treatments, and it offers new insights into the molecular basis of co-receptor usage. For instance, it quantifies the importance for co-receptor usage of a pocket that probably is responsible for binding sulfated tyrosine.