Biology subjects

Lange, C.

Publications and source records attributed to Lange, C..

32 records · Page 2Linked to original sources

bioRxiv2020

Mutations in SARS-CoV-2 spike protein and RNA polymerase complex are associated with COVID-19 mortality risk

BackgroundSARS-CoV-2 mortality has been extensively studied in relation to host susceptibility. How sequence variations in the SARS-CoV-2 genome affect pathogenicity is poorly understood. Whole-genome sequencing (WGS) of the virus with death in SARS-CoV-2 patients is one potential method of early identification of highly pathogenic strains to target for containment. MethodsWe analyzed 7,548 single stranded RNA-genomes of SARS-CoV-2 patients in the GISAID database (Elbe and Buckland-Merrett, 2017; Shu and McCauley, 2017) and associated variants with reported patients health status from COVID-19, i.e. deceased versus non-deceased. We probed each locus of the single stranded RNA of the SARS-CoV-2 virus for direct association with host/patient mortality using a logistic regression. ResultsIn total, evaluating 29,891 loci of the viral genome for association with patient/host mortality, two loci, at 12,053bp and 25,088bp, achieved genome-wide significance (p-values of 4.09e-09 and 4.41e-23, respectively). ConclusionsMutations at 25,088bp occur in the S2 subunit of the SARS-CoV-2 spike protein, which plays a key role in viral entry of target host cells. Additionally, mutations at 12,053bp are within the ORF1ab gene, in a region encoding for the protein nsp7, which is necessary to form the RNA polymerase complex responsible for viral replication and transcription. Both mutations altered amino acid coding sequences, potentially imposing structural changes that could enhance viral infectivity and symptom severity, and may be important to consider as targets for therapeutic development. Identification of these highly significant associations, unlikely to occur by chance, may assist with COVID-19 early containment of strains that are potentially highly pathogenic.

genomics↗

bioRxiv2020

A framework to efficiently smooth L1 penalties for linear regression

Penalized linear regression approaches that include an L1 term have become an important tool in statistical data analysis. One prominent example is the least absolute shrinkage and selection operator (Lasso), though the class of L1 penalized regression operators also includes the fused and graphical Lasso, the elastic net, etc. Although the L1 penalty makes their objective function convex, it is not differentiable everywhere, motivating the development of proximal gradient algorithms such as Fista, the current gold standard in the literature. In this work, we take a different approach based on smoothing in a fixed parameter setting (the problem size n and number of parameters p are fixed). The methodological contribution of our article is threefold: (1) We introduce a unified framework to compute closed-form smooth surrogates of a whole class of L1 penalized regression problems using Nesterov smoothing. The surrogates preserve the convexity of the original (unsmoothed) objective functions, are uniformly close to them, and have closed-form derivatives everywhere for efficient minimization via gradient descent; (2) We prove that the estimates obtained with the smooth surrogates can be made arbitrarily close to the ones of the original (unsmoothed) objective functions, and provide explicitly computable a priori error bounds on the accuracy of our estimates; (3) We propose an iterative algorithm to progressively smooth the L1 penalty which increases accuracy and is virtually free of tuning parameters. The proposed methodology is applicable to a large class of L1 penalized regression operators, including all the operators mentioned above. Although the resulting estimates are typically dense, sparseness can be enforced again via thresholding. Using simulation studies, we compare our framework to current gold standards such as Fista, glmnet, gLasso, etc. Our results suggest that our proposed smoothing framework provides predictions of equal or higher accuracy than the gold standards while keeping the aforementioned theoretical guarantees and having roughly the same asymptotic runtime scaling.

bioinformatics↗

bioRxiv2020

Coronavirus surveillance in Congo basin wildlife detects RNA of multiple species circulating in bats and rodents

Coronaviruses play an important role as pathogens of humans and animals, and the emergence of epidemics like SARS, MERS and COVID-19 is closely linked to zoonotic transmission events primarily from wild animals. Bats have been found to be an important source of coronaviruses with some of them having the potential to infect humans, with other animals serving as intermediate or alternate hosts or reservoirs. Host diversity may be an important contributor to viral diversity and thus the potential for zoonotic events. To date, limited research has been done in Africa on this topic, in particular in the Congo Basin despite frequent contact between humans and wildlife in this region. We sampled and, using consensus coronavirus PCR-primers, tested 3,561 wild animals for coronavirus RNA. The focus was on bats (38%), rodents (38%), and primates (23%) that posed an elevated risk for contact with people, and we found coronavirus RNA in 121 animals, of which all but two were bats. Depending on the taxonomic family, bats were significantly more likely to be coronavirus RNA-positive when sampled either in the wet (Pteropodidae and Rhinolophidae) or dry season (Hipposideridae, Miniopteridae, Molossidae, and Vespertilionidae). The detected RNA sequences correspond to 15 Alpha- and 6 Beta-coronaviruses, with some of them being very similar (>95% nucleotide identities) to known coronaviruses and others being more unique and potentially representing novel viruses. In seven of the bats, we detected RNA most closely related to sequences of the human common cold coronaviruses 229E or NL63 (>80% nucleotide identities). The findings highlight the potential for coronavirus spillover, especially in regions with a high diversity of bats and close human contact, and reinforces the need for ongoing surveillance.

microbiology↗

bioRxiv2020

Measuring the iron content of dopaminergic neurons in substantia nigra with MRI relaxometry

In Parkinsons disease, the depletion of iron-rich dopaminergic neurons in substantia nigras nigrosome 1 precedes first motor symptoms by two decades. Monitoring this neuronal depletion at an early disease stage is needed for diagnosis and treatment monitoring. Magnetic resonance imaging (MRI) is particularly suitable for this task due to its sensitivity to tissue iron. However, the mechanisms of MRI contrast in substantia nigra are not well understood, hindering the development of specific biomarkers. We showed that the dominant contribution to the effective transverse MRI relaxation rate [Formula] in nigrosome 1 originates from iron accumulated in the neuromelanin of dopaminergic neurons. We linked [Formula] quantitatively to the product of cell density and local iron concentration in dopaminergic neurons, combining quantitative 3D iron histology, biophysical modeling, and quantitative MRI on post mortem brain tissue. This knowledge opens an avenue for monitoring neuronal iron and density in vivo and may be applied to detect early neurodegeneration in Parkinsons disease.

neuroscience↗

bioRxiv2020

Unsupervised cluster analysis of SARS-CoV-2 genomes indicates that recent (June 2020) cases in Beijing are from a genetic subgroup that consists of mostly European and South(east) Asian samples, of which the latter are the most recent

Research efforts of the ongoing SARS-CoV-2 pandemic have focused on viral genome sequence analysis to understand how the virus spread across the globe. Here, we assess three recently identified SARS-CoV-2 genomes in Beijing from June 2020 and attempt to determine the origin of these genomes, made available in the GISAID database. The database contains fully or partially sequenced SARS-CoV-2 samples from laboratories around the world. Including the three new samples and excluding samples with missing annotations, we analyzed 7, 643 SARS-CoV-2 genomes. Using principal component analysis computed on a similarity matrix that compares all pairs of the SARS-CoV-2 nucleotide sequences at all loci simultaneously, using the Jaccard index, we find that the newly discovered virus genomes from Beijing are in a genetic cluster that consists mostly of cases from Europe and South(east) Asia. The sequences of the new cases are most related to virus genomes from a small number of cases from China (March 2020), cases from Europe (February to early May 2020), and cases from South(east) Asia (May to June 2020). These findings could suggest that the original cases of this genetic cluster originated from China in March 2020 and were re-introduced to China by transmissions from samples from South(east) Asia between April and June 2020.

bioinformatics↗

bioRxiv2020

WNT6-ACC2-induced accumulation of triacylglycerol rich lipid droplets is exploited by Mycobacterium tuberculosis

In view of emerging drug-resistant tuberculosis, host directed therapies are urgently needed to improve treatment outcomes with currently available anti-tuberculosis therapies. One option is to interfere with the formation of lipid-laden “foamy” macrophages in the infected host. Here, we provide evidence that WNT6, a member of the evolutionary conserved WNT signaling pathway, promotes foam cell formation by regulating key lipid metabolic genes including acetyl-CoA carboxylase-2 (ACC2) during pulmonary TB. In addition, we demonstrate that Mycobacterium tuberculosis (Mtb) facilitates its intracellular growth and dissemination in the host by exploiting the WNT6-ACC2 pathway. Using genetic and pharmacological approaches, we show that lack of functional WNT6 or ACC2 significantly reduces intracellular TAG levels, Mtb growth and necrotic cell death of macrophages. In combination with the anti-TB drug isoniazid, pharmacological inhibition of ACC2 improved anti-mycobacterial treatment in vitro and in vivo. Therefore, we propose the WNT6-ACC2 signaling pathway as a promising target for a host-directed therapy to reduce intracellular replication of Mtb by modulating neutral lipid metabolism.Competing Interest StatementDrs. N. Reiling and J. Brandenburg (Research Center Borstel, Leibniz Lung Center, 23845 Borstel, Germany) have filed a patent application entitled ACC inhibitors as means and methods for treating mycobacterial diseases(WO2018007430A1, patent pending).View Full Text

immunology↗

bioRxiv2020

Unsupervised cluster analysis of SARS-CoV-2 genomes reflects its geographic progression and identifies distinct genetic subgroups of SARS-CoV-2 virus

Over 10,000 viral genome sequences of the SARS-CoV-2 virus have been made readily available during the ongoing coronavirus pandemic since the initial genome sequence of the virus was released on the open access Virological website (http://virological.org/) early on January 11. We utilize the published data on the single stranded RNAs of 11, 132 SARS-CoV-2 patients in the GISAID (Elbe and Buckland-Merrett, 2017; Shu and McCauley, 2017) database, which contains fully or partially sequenced SARS-CoV-2 samples from laboratories around the world. Among many important research questions which are currently being investigated, one aspect pertains to the genetic characterization/classification of the virus. We analyze data on the nucleotide sequencing of the virus and geographic information of a subset of 7, 640 SARS-CoV-2 patients without missing entries that are available in the GISAID database. Instead of modelling the mutation rate, applying phylogenetic tree approaches, etc., we here utilize a model-free clustering approach that compares the viruses at a genome-wide level. We apply principal component analysis to a similarity matrix that compares all pairs of these SARS-CoV-2 nucleotide sequences at all loci simultaneously, using the Jaccard index (Jaccard, 1901; Tan et al., 2005; Prokopenko et al., 2016; Schlauch et al., 2017). Our analysis results of the SARS-CoV-2 genome data illustrates the geographic and chronological progression of the virus, starting from the first cases that were observed in China to the current wave of cases in Europe and North America. This is in line with a phylogenetic analysis which we use to contrast our results. We also observe that, based on their sequence data, the SARS-CoV-2 viruses cluster in distinct genetic subgroups. It is the subject of ongoing research to examine whether the genetic subgroup could be related to diseases outcome and its potential implications for vaccine development.

genomics↗

bioRxiv2020

A fast and efficient smoothing approach to LASSO regression and an application in statistical genetics: polygenic risk scores for Chronic obstructive pulmonary disease (COPD)

High dimensional linear regression problems are often fitted using Lasso approaches. Although the Lasso objective function is convex, it is not differentiable everywhere, making the use of gradient descent methods for minimization not straightforward. To avoid this technical issue, we apply Nesterov smoothing to the original (unsmoothed) Lasso objective function. We introduce a closed-form smoothed Lasso which preserves the convexity of the Lasso function, is uniformly close to the unsmoothed Lasso, and allows us to obtain closed-form derivatives everywhere for efficient and fast minimization via gradient descent. Our simulation studies are focused on polygenic risk scores using genetic data from a genome-wide association study (GWAS) for chronic obstructive pulmonary disease (COPD). We compare accuracy and run-time of our approach to the current gold standard in the literature, the FISTA algorithm. Our results suggest that the proposed methodology provides estimates with equal or higher accuracy than the FISTA algorithm while having the same asymptotic runtime scaling. The proposed methodology is implemented in the R-package smoothedLasso, available on the Comprehensive R Archive Network (CRAN).

bioinformatics↗

bioRxiv2020

locStra: Fast analysis of regional/global stratification in whole genome sequencing (WGS) studies

locStra is an R-package for the analysis of regional and global population stratification in whole genome sequencing studies, where regional stratification refers to the substructure defined by the loci in a particular region on the genome. Population substructure can be assessed based on the genetic covariance matrix, the genomic relationship matrix, and the unweighted/weighted genetic Jaccard similarity matrix. Using a sliding window approach, the regional similarity matrices are compared to the global ones, based on user-defined window sizes and metrics, e.g. the correlation between regional and global eigenvectors. An algorithm for the specification of the window size is provided. As the implementation fully exploits sparse matrix algebra and is written in C++, the analysis is highly efficient. Even on single cores, for realistic study sizes (several thousand subjects, several million RVs per subject), the runtime for the genome-wide computation of all regional similarity matrices does typically not exceed one hour, enabling an unprecedented investigation of regional stratification across the entire genome. The package is applied to three WGS studies, illustrating the varying patterns of regional substructure across the genome and its beneficial effects on association testing.

genetics↗

bioRxiv2020

Single-cell transcriptome analysis reveals cell-cell communication and thyrocyte diversity in the zebrafish thyroid gland

The thyroid gland regulates growth and metabolism via production of thyroid hormone in follicles composed of thyrocytes. So far, thyrocytes have been assumed to be a homogenous population. To uncover genetic heterogeneity in the thyrocyte population, and molecularly characterize the non-thyrocyte cells surrounding the follicle, we developed a single-cell transcriptome atlas of the zebrafish thyroid gland. The 6249-cell atlas includes profiles of thyrocytes, blood vessels, lymphatic vessels, immune cells and fibroblasts. Further, the thyrocytes could be split into two sub-populations with unique transcriptional signature, including differential expression of the transcription factor pax2a. To validate thyrocyte heterogeneity, we generated a CRISPR/Cas9-based pax2a knock-in line, which demonstrated specific pax2a expression in the thyrocytes. However, a population of pax2a-low mature thyrocytes interspersed within individual follicles could be distinguished, corroborating heterogeneity within the thyrocyte population. Our results identify and validate transcriptional differences within the nominally homogenous thyrocyte population. One-line summarySingle-cell analysis uncovers latent heterogeneity in thyroid follicular cells. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=74 SRC="FIGDIR/small/891630v2_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@f6df3org.highwire.dtl.DTLVardef@fb8c59org.highwire.dtl.DTLVardef@bb5be1org.highwire.dtl.DTLVardef@8d174e_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2019

An exact, unifying framework for region-based association testing in family-based designs, including higher criticism approaches, SKATs, multivariate and burden tests

Analysis of rare variants in family-based studies remains a challenge. To perform a region/set-based association analysis of rare variants in family-based studies, we propose a general methodological framework that integrates higher criticism, maximum, SKATs, and burden approaches into the family-based association testing (FBAT) framework. Using the haplotype algorithm for FBATs to compute the conditional genotype distribution under the null hypothesis of Mendelian transmissions, virtually any association test statistics can be implemented in our approach and simulation-based or exact p-values can be computed without the need for asymptotic settings. Using simulations, we compare the features of the proposed test statistics in our framework with the existing region-based methodology for family-based studies under various scenarios. The tests of our framework outperform the existing approaches. We provide general guidelines for which scenarios, e.g., sparseness of the signals or local LD structure, which test statistic will have distinct power advantages over the others. We also illustrate our approach in an application to a whole-genome sequencing dataset with 897 asthmatic trios.

genetics↗

bioRxiv2019

Cortisol associated with hypometabolism across the Alzheimer’s disease spectrum

Objective: Hypothalamic-pituitary-adrenal (HPA) dysregulation is proposed as a risk factor for Alzheimer's disease (AD). This cross-sectional study assessed relationships between plasma cortisol levels and neuroimaging biomarkers, specifically brain glucose metabolism and gray matter volume, across the AD spectrum. Methods: Cognitively normal older adults and patients with mild cognitive impairment (MCI) and AD dementia were included from the Alzheimer's Disease Neuroimaging Initiative. Participants (n = 556) were selected based on availability of baseline measures of plasma cortisol levels and gray matter volume, as estimated with magnetic resonance imaging. Within a subsample (n = 288), we examined brain glucose metabolism (n = 288) as with positron emission tomography. Relationships between plasma cortisol and AD neuroimaging biomarkers were assessed using regions-of-interest and voxel-wise analyses. Results: Across the entire cohort, higher plasma cortisol was also related to lower gray matter volume, most notably in the left lateral temporal-parietal-occipital regions. Importantly, higher plasma cortisol concentration was also related to hypometabolism, especially in lateral temporo-parietal and medial parietal regions. When stratified by diagnosis, these negative associations were most pronounced in MCI and AD patients. Interpretation: High plasma cortisol was associated with hypometabolism predominantly in AD-sensitive regions. Our results indicate that HPA axis activation could influence brain metabolism and exacerbate existing AD pathological processes. This is consistent with a notion that stress is a conceivable target for intervention to slow down AD progression. Future studies should delineate underlying pathological mechanisms and investigate if clinical or lifestyle interventions could alleviate negative actions of stress on AD.

neuroscience↗

bioRxiv2017

An almost nearly optimal sequential testing approach to permutation-based association testing

The following technical report describes the technical details for the implementation of a sequential testing approach to permutation-based association testing in whole-genome sequencing studies. The sequential testing approach enables to control the probability of a type 1 and type 2 error at arbitrary small pre-specified levels and approaches the theoretical minimum of expected number of required permutations as these levels go to zero.

genetics↗

bioRxiv2017

Reporting correct p-values in VEGAS analyses

VEGAS (versatile gene-based association study) is a popular methodological framework to perform gene-based tests based on summary statistics from single-variant analyses. The approach incorporates linkage disequilibrium information from reference panels to account for the correlation of test statistics. The gene-based test can utilize three different types of tests. In 2015, the improved framework VEGAS2, using more detailed reference panels, was published. Both versions provide user-friendly web- and offline-based tools for the analysis. However, the implementation of the popular top-percentage test is erroneous in both versions. The p-values provided by VEGAS2 are deflated/anti-conservative. Based on real data examples, we demonstrate that this can increase substantially the rate of false positive findings and can lead to inconsistencies between different test options. We also provide code that allows the user of VEGAS to compute correct p-values.

genetics↗