Sequencing accuracy and systematic errors of nanopore direct RNA sequencing.

BACKGROUND: Direct RNA sequencing (dRNA-seq) on the Oxford Nanopore Technologies (ONT) platforms can produce reads covering up to full-length gene transcripts, while containing decipherable information about RNA base modifications and poly-A tail lengths. Although many published studies have been expanding the potential of dRNA-seq, its sequencing accuracy and error patterns remain understudied. RESULTS: We present the first comprehensive evaluation of sequencing accuracy and characterisation of systematic errors in dRNA-seq data from diverse organisms and synthetic in vitro transcribed RNAs. We found that for sequencing kits SQK-RNA001 and SQK-RNA002, the median read accuracy ranged from 87% to 92% across species, and deletions significantly outnumbered mismatches and insertions. Due to their high abundance in the transcriptome, heteropolymers and short homopolymers were the major contributors to the overall sequencing errors. We also observed systematic biases across all species at the levels of single nucleotides and motifs. In general, cytosine/uracil-rich regions were more likely to be erroneous than guanines and adenines. By examining raw signal data, we identified the underlying signal-level features potentially associated with the error patterns and their dependency on sequence contexts. While read quality scores can be used to approximate error rates at base and read levels, failure to detect DNA adapters may be a source of errors and data loss. By comparing distinct basecallers, we reason that some sequencing errors are attributable to signal insufficiency rather than algorithmic (basecalling) artefacts. Lastly, we generated dRNA-seq data using the latest SQK-RNA004 sequencing kit released at the end of 2023 and found that although the overall read accuracy increased, the systematic errors remain largely identical compared to the previous kits. CONCLUSIONS: As the first systematic investigation of dRNA-seq errors, this study offers a comprehensive overview of reproducible error patterns across diverse datasets, identifies potential signal-level insufficiency, and lays the foundation for error correction methods.

Dynamics of HIV PrEP use and coverage during and after COVID-19 in Germany.

BACKGROUND: Pre-exposure prophylaxis (PrEP) with oral emtricitabine/tenofovir disoproxil (FTC/TDF) proved highly efficient in preventing HIV. Since 09/2019, FTC/TDF-PrEP is covered by health insurances in Germany, if prescribed by licensed specialists. However, methods to longitudinally monitor progress in PrEP implementation in Germany are lacking. METHODS: Utilizing anonymous FTC/TDF prescription data from 2017-2021, we developed a mathematical model to disentangle HIV-treatment from PrEP prescriptions, as well as to translate PrEP prescriptions into number of PrEP users. We used the model to estimate past- and future PrEP uptake dynamics, to predict coverage of PrEP needs and to quantify the impact of COVID-19 on PrEP uptake on a national and regional level. RESULTS: We identified significant (p<0.01) decelerating effects of the first- and second COVID-19-lockdown on PrEP uptake in 04/2020 and 12/2020. We estimated 26,159 (CI: 25,751-26,571) PrEP users by 12/2021, corresponding to 33% PrEP coverage of people in need. We projected 64,794 (CI: 62,956-66,557) PrEP users by 12/2030, corresponding to 81% PrEP coverage. We identified profound regional differences, with high PrEP coverage and uptake in metropoles and low coverage in more rural regions. CONCLUSIONS: Our approach presents a comprehensive solution to monitor and forecast PrEP implementation from anonymous data and highlighted that the COVID-19 pandemic significantly decelerated PrEP uptake in Germany. Moreover, slow PrEP uptake in rural areas indicate that structural barriers in PrEP care, education or information exist that may hamper the goal of ending the AIDS epidemic by 2030.

Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research.

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causes the infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformatics tools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection, understanding and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to get insight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for the routine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemic and evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets and development of therapeutic strategies. For each tool, we briefly describe its use case and how it advances research specifically for SARS-CoV-2. All tools are free to use and available online, either through web applications or public code repositories. Contact:evbc@unj-jena.de.

Advancing Precision Vaccinology by Molecular and Genomic Surveillance of Severe Acute Respiratory Syndrome Coronavirus 2 in Germany, 2021.

BACKGROUND: Comprehensive pathogen genomic surveillance represents a powerful tool to complement and advance precision vaccinology. The emergence of the Alpha variant in December 2020 and the resulting efforts to track the spread of this and other severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern led to an expansion of genomic sequencing activities in Germany. METHODS: At Robert Koch Institute (RKI), the German National Institute of Public Health, we established the Integrated Molecular Surveillance for SARS-CoV-2 (IMS-SC2) network to perform SARS-CoV-2 genomic surveillance at the national scale, SARS-CoV-2-positive samples from laboratories distributed across Germany regularly undergo whole-genome sequencing at RKI. RESULTS: We report analyses of 3623 SARS-CoV-2 genomes collected between December 2020 and December 2021, of which 3282 were randomly sampled. All variants of concern were identified in the sequenced sample set, at ratios equivalent to those in the 100-fold larger German GISAID sequence dataset from the same time period. Phylogenetic analysis confirmed variant assignments. Multiple mutations of concern emerged during the observation period. To model vaccine effectiveness in vitro, we employed authentic-virus neutralization assays, confirming that both the Beta and Zeta variants are capable of immune evasion. The IMS-SC2 sequence dataset facilitated an estimate of the SARS-CoV-2 incidence based on genetic evolution rates. Together with modeled vaccine efficacies, Delta-specific incidence estimation indicated that the German vaccination campaign contributed substantially to a deceleration of the nascent German Delta wave. CONCLUSIONS: SARS-CoV-2 molecular and genomic surveillance may inform public health policies including vaccination strategies and enable a proactive approach to controlling coronavirus disease 2019 spread as the virus evolves.

Numerical approaches for the rapid analysis of prophylactic efficacy against HIV with arbitrary drug-dosing schemes.

Pre-exposure prophylaxis (PrEP) is an important pillar to prevent HIV transmission. Because of experimental and clinical shortcomings, mathematical models that integrate pharmacological, viral- and host factors are frequently used to quantify clinical efficacy of PrEP. Stochastic simulations of these models provides sample statistics from which the clinical efficacy is approximated. However, many stochastic simulations are needed to reduce the associated sampling error. To remedy the shortcomings of stochastic simulation, we developed a numerical method that allows predicting the efficacy of arbitrary prophylactic regimen directly from a viral dynamics model, without sampling. We apply the method to various hypothetical dolutegravir (DTG) prophylaxis scenarios. The approach is verified against state-of-the-art stochastic simulation. While the method is more accurate than stochastic simulation, it is superior in terms of computational performance. For example, a continuous 6-month prophylactic profile is computed within a few seconds on a laptop computer. The method's computational performance, therefore, substantially expands the horizon of feasible analysis in the context of PrEP, and possibly other applications.

[SARS-CoV-2 transmission routes and implications for self- and non-self-protection]. / SARS-CoV-2-Übertragungswege und Implikationen für den Selbst- und Fremdschutz.

The global spread of the coronavirus SARS-CoV­2 has massively impacted health, economic, and social systems. Although effective vaccines are now available, it is likely that this pathogen will become endemic and stay with us for years. In order to most effectively protect others and oneself from SARS-CoV­2 infection, an understanding of how SARS-CoV­2 is transmitted is of utmost importance.In this review paper, we explain transmission routes with an eye towards protecting others and oneself. We also address characteristics of SARS-CoV­2 transmission in the community. This work will help to clarify the following questions based on the available literature: When and for how long is an infected person contagious? How is the virus excreted? How is the virus taken up? How does the virus spread in society?Human-to-human transmission of SARS-CoV­2 is strongly determined by pathogen molecular characteristics as well as the kinetics of replication, shedding, and infection. SARS-CoV­2 is transmitted primarily via human aerosols, which infected persons can excrete even if symptoms of the disease are not (yet) present. Most infected people cause only a few secondary cases, whereas a few cases (so-called super-spreaders) cause a high number of secondary infections - at the population level one speaks of a so-called "overdispersion." These special characteristics of SARS-CoV­2 (asymptomatic aerosol transmission and overdispersion) make the pandemic difficult to control.

[Measures to cope with the COVID-19 pandemic in Germany: nonpharmaceutical and pharmaceutical interventions]. / Maßnahmen zur Bewältigung der COVID-19-Pandemie in Deutschland: nichtpharmakologische und pharmakologische Ansätze.

When the emerging novel SARS-CoV­2 virus first appeared in December 2019, neither specific therapeutic options nor vaccinations were available. The role of nonpharmaceutical interventions (NPIs) became of central importance. At the Robert Koch Institute, a multilayer strategy consisting of population-based and individual preventive measures to control the pandemic was developed, which built upon existing influenza pandemic plans as well as generic plans. This paper explains the recommended NPIs and illustrates the pharmaceutical approaches developed in parallel.Among others, general contact bans, providing material for infection prevention and control, ban of events, closing educational institutions, and restricting travel are counted among population-based measures. Additional individual preventive measures are necessary, e.g., keeping a minimum distance, reducing contacts, and wearing a mouth-nose covering as well as quarantine and isolation. Measures within the health system are based on recommendations of the Commission on Hospital Hygiene and Infection Protection (Kommission für Krankenhaushygiene und Infektionsprävention (KRINKO)) and specified and implemented by professional societies. Since November 2020, an antiviral therapy with remdesivir and treatment with the glucocorticoid dexamethasone have been available as pharmaceutical interventions. Monoclonal antibodies are at this time not approved. Therapeutic anticoagulation is recommended.Recommendations are constantly adapted to the increasing knowledge on the pathogen and its means of transmission. A challenge is to strengthen the trust of the population. Many measures have to be applied on an individual basis in order to work together.

Reconstruction of the Genetic History and the Current Spread of HIV-1 Subtype A in Germany.

HIV-1 non-B infections have been increasing in Europe for several years. In Germany, subtype A belongs to the most abundant non-B subtypes showing an increasing prevalence of 8.3% among new infections in 2016. Here we trace the origin and examine the current spread of the German HIV-1 subtype A epidemic. Bayesian coalescence and birth-death analyses were performed with 180 German HIV-1 pol sequences and 528 related and publicly available sequences to reconstruct the population dynamics and fluctuations for each of the transmission groups. Our reconstructions indicate two distinct sources of the German subtype A epidemic, with an Eastern European and an Eastern African lineage both cocirculating in the country. A total of 13 German-origin clusters were identified; among these, 6 clusters showed recent activity. Introductions leading to further countrywide spread originated predominantly from Eastern Africa when introduced before 2005. Since 2005, however, spreading introductions have occurred exclusively within the Eastern European clade. Moreover, we observed changes in the main route of subtype A transmission. The beginning of the German epidemic (1985 to 1995) was dominated by heterosexual transmission of the Eastern African lineage. Since 2005, transmissions among German men who have sex with men (MSM) have been increasing and have been associated with the Eastern European lineage. Infections among people who inject drugs dominated between 1998 and 2005. Our findings on HIV-1 subtype A infections provide new insights into the spread of this virus and extend the understanding of the HIV epidemic in Germany.IMPORTANCE HIV-1 subtype A is the second most prevalent subtype worldwide, with a high prevalence in Eastern Africa and Eastern Europe. However, an increase of non-B infections, including subtype A infections, has been observed in Germany and other European countries. There has simultaneously been an increased flow of refugees into Europe and especially into Germany, raising the question of whether the surge in non-B infections resulted from this increased immigration or whether German transmission chains are mainly involved. This study is the first comprehensive subtype A study from a western European country analyzing in detail its phylogenetic origin, the impact of various transmission routes, and its current spread. The results and conclusions presented provide new and substantial insights for virologists, epidemiologists, and the general public health sector. In this regard, they should be useful to those authorities responsible for developing public health intervention strategies to combat the further spread of HIV/AIDS.

Mechanistic framework predicts drug-class specific utility of antiretrovirals for HIV prophylaxis.

Currently, there is no effective vaccine to halt HIV transmission. However, pre-exposure prophylaxis (PrEP) with the drug combination Truvada can substantially decrease HIV transmission in individuals at risk. Despite its benefits, Truvada-based PrEP is expensive and needs to be taken once-daily, which often leads to inadequate adherence and incomplete protection. These deficits may be overcome by next-generation PrEP regimen, including currently investigated long-acting formulations, or patent-expired drugs. However, poor translatability of animal- and ex vivo/in vitro experiments, and the necessity to conduct long-term (several years) human trials involving considerable sample sizes (N>1000 individuals) are major obstacles to rationalize drug-candidate selection. We developed a prophylaxis modelling tool that mechanistically considers the mode-of-action of all available drugs. We used the tool to screen antivirals for their prophylactic utility and identify lower bound effective concentrations that can guide dose selection in PrEP trials. While in vitro measurable drug potency usually guides PrEP trial design, we found that it may over-predict PrEP potency for all drug classes except reverse transcriptase inhibitors. While most drugs displayed graded concentration-prophylaxis profiles, protease inhibitors tended to switch between none- and complete protection. While several treatment-approved drugs could be ruled out as PrEP candidates based on lack-of-prophylactic efficacy, darunavir, efavirenz, nevirapine, etravirine and rilpivirine could more potently prevent infection than existing PrEP regimen (Truvada). Notably, some drugs from this candidate set are patent-expired and currently neglected for PrEP repurposing. A next step is to further trim this candidate set by ruling out compounds with ominous safety profiles, to assess different administration schemes in silico and to test the remaining candidates in human trials.

In cell mutational interference mapping experiment (in cell MIME) identifies the 5' polyadenylation signal as a dual regulator of HIV-1 genomic RNA production and packaging.

Non-coding RNA regulatory elements are important for viral replication, making them promising targets for therapeutic intervention. However, regulatory RNA is challenging to detect and characterise using classical structure-function assays. Here, we present in cell Mutational Interference Mapping Experiment (in cell MIME) as a way to define RNA regulatory landscapes at single nucleotide resolution under native conditions. In cell MIME is based on (i) random mutation of an RNA target, (ii) expression of mutated RNA in cells, (iii) physical separation of RNA into functional and non-functional populations, and (iv) high-throughput sequencing to identify mutations affecting function. We used in cell MIME to define RNA elements within the 5' region of the HIV-1 genomic RNA (gRNA) that are important for viral replication in cells. We identified three distinct RNA motifs controlling intracellular gRNA production, and two distinct motifs required for gRNA packaging into virions. Our analysis reveals the 73AAUAAA78 polyadenylation motif within the 5' PolyA domain as a dual regulator of gRNA production and gRNA packaging, and demonstrates that a functional polyadenylation signal is required for viral packaging even though it negatively affects gRNA production.

Hybrid stochastic framework predicts efficacy of prophylaxis against HIV: An example with different dolutegravir prophylaxis schemes.

To achieve the 90-90-90 goals set by UNAIDS, the number of new HIV infections needs to decrease to approximately 500,000 by 2020. One of the 'five pillars' to achieve this goal is pre-exposure prophylaxis (PrEP). Truvada (emtricitabine-tenofovir) is currently the only medication approved for PrEP. Despite its advantages, Truvada is costly and requires individuals to adhere to the once-daily regimen. To improve PrEP, many next-generation regimen, including long-acting formulations, are currently investigated. However, pre-clinical testing may not guide candidate selection, since it often fails to translate into clinical efficacy. On the other hand, quantifying prophylactic efficacy in the clinic is ethically problematic and requires to conduct long (years) and large (N>1000 individuals) trials, precluding systematic evaluation of candidates and deployment strategies. To prioritize- and help design PrEP regimen, tools are urgently needed that integrate pharmacological-, viral- and host factors determining prophylactic efficacy. Integrating the aforementioned factors, we developed an efficient and exact stochastic simulation approach to predict prophylactic efficacy, as an example for dolutegravir (DTG). Combining the population pharmacokinetics of DTG with the stochastic framework, we predicted that plasma concentrations of 145.18 and 722.23nM prevent 50- and 90% sexual transmissions respectively. We then predicted the reduction in HIV infection when DTG was used in PrEP, PrEP 'on demand' and post-exposure prophylaxis (PEP) before/after virus exposure. Once daily PrEP with 50mg oral DTG prevented 99-100% infections, and 85% of infections when 50% of dosing events were missed. PrEP 'on demand' prevented 79-84% infections and PEP >80% when initiated within 6 hours after virus exposure and continued for as long as possible. While the simulation framework can easily be adapted to other PrEP candidates, our simulations indicated that oral 50mg DTG is non-inferior to Truvada. Moreover, the predicted 90% preventive concentrations can guide release kinetics of currently developed DTG nano-formulations.

Mutational interference mapping experiment (MIME) for studying RNA structure and function.

RNA regulates many biological processes; however, identifying functional RNA sequences and structures is complex and time-consuming. We introduce a method, mutational interference mapping experiment (MIME), to identify, at single-nucleotide resolution, the primary sequence and secondary structures of an RNA molecule that are crucial for its function. MIME is based on random mutagenesis of the RNA target followed by functional selection and next-generation sequencing. Our analytical approach allows the recovery of quantitative binding parameters and permits the identification of base-pairing partners directly from the sequencing data. We used this method to map the binding site of the human immunodeficiency virus-1 (HIV-1) Pr55(Gag) protein on the viral genomic RNA in vitro, and showed that, by analyzing permitted base-pairing patterns, we could model RNA structure motifs that are crucial for protein binding.

MIMEAnTo: profiling functional RNA in mutational interference mapping experiments.

The mutational interference mapping experiment (MIME) is a powerful method that, coupled to a bioinformatics analysis pipeline, allows the identification of domains and structures in RNA that are important for its function. In MIME, target RNAs are randomly mutated, selected by function, physically separated and sequenced using next-generation sequencing (NGS). Quantitative effects of each mutation at each position in the RNA can be recovered with statistical certainty using the herein developed user-friendly, cross-platform software MIMEAnTo (MIME Analysis Tool). AVAILABILITY AND IMPLEMENTATION: MIMEAnTo is implemented in C ++ using the boost library as well as Qt for the graphical user interface and is distributed under GPL (http://www.gnu.org/licences/gpl). The libraries are statically linked in a stand alone executable and are not required on the system. The plots are generated with gnuplot. Gnuplot-iostream (https://github.com/dstahlke/gnuplot-iostream) serves as gnuplot interface. Standalone executables including examples and source code can be downloaded from https://github.com/maureensmith/MIMEAnTo CONTACTS: msmith@zedat.fu-berlin.de or vkleist@zedat.fu-berlin.deSupplementary information: Supplementary data are available at Bioinformatics online.

Molecular evolution of HIV-1 integrase during the 20 years prior to the first approval of integrase inhibitors.

BACKGROUND: Detailed knowledge of the evolutionary potential of polymorphic sites in a viral protein is important for understanding the development of drug resistance in the presence of an inhibitor. We therefore set out to analyse the molecular evolution of the HIV-1 subtype B integrase at the inter-patient level in Germany during a 20-year period prior to the first introduction of integrase strand inhibitors (INSTIs). METHODS: We determined 337 HIV-1 integrase subtype B sequences (amino acids 1-278) from stored plasma samples of antiretroviral treatment-naïve individuals newly diagnosed with HIV-1 between 1986 and 2006. Shannon entropy was calculated to determine the variability at each amino acid position. Time trends in the frequency of amino acid variants were identified by linear regression. Direct coupling analysis was applied to detect covarying sites. RESULTS: Twenty-two time trends in the frequency of amino acid variants demonstrated either single amino acid exchanges or variation in the degree of polymorphy. Covariation was observed for 17 amino acid variants with a temporal trend. Some minor INSTI resistance mutations (T124A, V151I, K156 N, T206S, S230 N) and some INSTI-selected mutations (M50I, L101I, T122I, T124 N, T125A, M154I, G193E, V201I) were identified at overall frequencies >5%. Among these, the frequencies of L101I, T122I, and V201I increased over time, whereas the frequency of M154I decreased. Moreover, L101I, T122I, T124A, T125A, M154I, and V201I covaried with non-resistance-associated variants. CONCLUSIONS: Time-trending, covarying polymorphisms indicate that long-term evolutionary changes of the HIV-1 integrase involve defined clusters of possibly structurally or functionally associated sites independent of selective pressure through INSTIs at the inter-patient level. Linkage between polymorphic resistance- and non-resistance-associated sites can impact the selection of INSTI resistance mutations in complex ways. Identification of these sites can help in improving genotypic resistance assays, resistance prediction algorithms, and the development of new integrase inhibitors.

Optimal Treatment Strategies in the Context of 'Treatment for Prevention' against HIV-1 in Resource-Poor Settings.

An estimated 2.7 million new HIV-1 infections occurred in 2010. `Treatment-for-prevention' may strongly prevent HIV-1 transmission. The basic idea is that immediate treatment initiation rapidly decreases virus burden, which reduces the number of transmittable viruses and thereby the probability of infection. However, HIV inevitably develops drug resistance, which leads to virus rebound and nullifies the effect of `treatment-for-prevention' for the time it remains unrecognized. While timely conducted treatment changes may avert periods of viral rebound, necessary treatment options and diagnostics may be lacking in resource-constrained settings. Within this work, we provide a mathematical platform for comparing different treatment paradigms that can be applied to many medical phenomena. We use this platform to optimize two distinct approaches for the treatment of HIV-1: (i) a diagnostic-guided treatment strategy, based on infrequent and patient-specific diagnostic schedules and (ii) a pro-active strategy that allows treatment adaptation prior to diagnostic ascertainment. Both strategies are compared to current clinical protocols (standard of care and the HPTN052 protocol) in terms of patient health, economic means and reduction in HIV-1 onward transmission exemplarily for South Africa. All therapeutic strategies are assessed using a coarse-grained stochastic model of within-host HIV dynamics and pseudo-codes for solving the respective optimal control problems are provided. Our mathematical model suggests that both optimal strategies (i)-(ii) perform better than the current clinical protocols and no treatment in terms of economic means, life prolongation and reduction of HIV-transmission. The optimal diagnostic-guided strategy suggests rare diagnostics and performs similar to the optimal pro-active strategy. Our results suggest that 'treatment-for-prevention' may be further improved using either of the two analyzed treatment paradigms.

Assessment of ambiguous base calls in HIV-1 pol population sequences as a biomarker for identification of recent infections in HIV-1 incidence studies.

An increase in the proportion of ambiguous base calls in HIV-1 pol population sequences during the course of infection has been demonstrated in different study populations, and sequence ambiguity thresholds to classify infections as recent or nonrecent have been suggested. The aim of our study was to evaluate sequence ambiguities as a candidate biomarker for use in an HIV-1 incidence assay using samples from antiretroviral treatment-naive seroconverters with known durations of infection (German HIV-1 Seroconverter Study). We used 2,203 HIV-1 pol population sequences derived from 1,334 seroconverters to assess the sequence ambiguity method (SAM). We then compared the serological incidence BED capture enzyme immunoassay (BED-CEIA) with the SAM for a subset of 723 samples from 495 seroconverters and evaluated a multianalyte algorithm that includes BED-CEIA results, SAM results, viral loads, and CD4 cell counts for 453 samples from 325 seroconverters. We observed a significant increase in the proportion of sequence ambiguities with the duration of infection. A sequence ambiguity threshold of 0.5% best identified recent infections with 76.7% accuracy. The mean duration of recency was determined to be 208 (95% confidence interval, 196 to 221) days. In the subset analysis, BED-CEIA achieved a significantly higher accuracy than the SAM (84.6 versus 75.5%, P < 0.001) and results were concordant for 64.2% (464/723) of the samples. Also, the multianalyte algorithm did not show better accuracy than the BED-CEIA (83.4 versus 84.3%, P = 0.786). In conclusion, the SAM and the multianalyte algorithm including SAM were inferior to the BED-CEIA, and the proportion of sequence ambiguities is therefore not a preferable biomarker for HIV-1 incidence testing.

Multi-Input data ASsembly for joint Analysis (MIASA): A framework for the joint analysis of disjoint sets of variables.

The joint analysis of two datasets [Formula: see text] and [Formula: see text] that describe the same phenomena (e.g. the cellular state), but measure disjoint sets of variables (e.g. mRNA vs. protein levels) is currently challenging. Traditional methods typically analyze single interaction patterns such as variance or covariance. However, problem-tailored external knowledge may contain multiple different information about the interaction between the measured variables. We introduce MIASA, a holistic framework for the joint analysis of multiple different variables. It consists of assembling multiple different information such as similarity vs. association, expressed in terms of interaction-scores or distances, for subsequent clustering/classification. In addition, our framework includes a novel qualitative Euclidean embedding method (qEE-Transition) which enables using Euclidean-distance/vector-based clustering/classification methods on datasets that have a non-Euclidean-based interaction structure. As an alternative to conventional optimization-based multidimensional scaling methods which are prone to uncertainties, our qEE-Transition generates a new vector representation for each element of the dataset union [Formula: see text] in a common Euclidean space while strictly preserving the original ordering of the assembled interaction-distances. To demonstrate our work, we applied the framework to three types of simulated datasets: samples from families of distributions, samples from correlated random variables, and time-courses of statistical moments for three different types of stochastic two-gene interaction models. We then compared different clustering methods with vs. without the qEE-Transition. For all examples, we found that the qEE-Transition followed by Ward clustering had superior performance compared to non-agglomerative clustering methods but had a varied performance against ultrametric-based agglomerative methods. We also tested the qEE-Transition followed by supervised and unsupervised machine learning methods and found promising results, however, more work is needed for optimal parametrization of these methods. As a future perspective, our framework points to the importance of more developments and validation of distance-distribution models aiming to capture multiple-complex interactions between different variables.

Axonal self-sorting without target guidance in Drosophila visual map formation.

The idea of guidance toward a target is central to axon pathfinding and brain wiring in general. In this work, we show how several thousand axonal growth cones self-pattern without target-dependent guidance during neural superposition wiring in Drosophila. Ablation of all target lamina neurons or loss of target adhesion prevents the stabilization but not the development of the pattern. Intravital imaging at the spatiotemporal resolution of growth cone dynamics in intact pupae and data-driven dynamics simulations reveal a mechanism by which >30,000 filopodia do not explore potential targets, but instead simultaneously generate and navigate a dynamic filopodial meshwork that steers growth directions. Hence, a guidance mechanism can emerge from the interactions of the axons being guided, suggesting self-organization as a more general feature of brain wiring.

Viral genome sequencing to decipher in-hospital SARS-CoV-2 transmission events.

The SARS-CoV-2 pandemic has highlighted the need to better define in-hospital transmissions, a need that extends to all other common infectious diseases encountered in clinical settings. To evaluate how whole viral genome sequencing can contribute to deciphering nosocomial SARS-CoV-2 transmission 926 SARS-CoV-2 viral genomes from 622 staff members and patients were collected between February 2020 and January 2021 at a university hospital in Munich, Germany, and analysed along with the place of work, duration of hospital stay, and ward transfers. Bioinformatically defined transmission clusters inferred from viral genome sequencing were compared to those inferred from interview-based contact tracing. An additional dataset collected at the same time at another university hospital in the same city was used to account for multiple independent introductions. Clustering analysis of 619 viral genomes generated 19 clusters ranging from 3 to 31 individuals. Sequencing-based transmission clusters showed little overlap with those based on contact tracing data. The viral genomes were significantly more closely related to each other than comparable genomes collected simultaneously at other hospitals in the same city (n = 829), suggesting nosocomial transmission. Longitudinal sampling from individual patients suggested possible cross-infection events during the hospital stay in 19.2% of individuals (14 of 73 individuals). Clustering analysis of SARS-CoV-2 whole genome sequences can reveal cryptic transmission events missed by classical, interview-based contact tracing, helping to decipher in-hospital transmissions. These results, in line with other studies, advocate for viral genome sequencing as a pathogen transmission surveillance tool in hospitals.

HIV-1 polymerase inhibition by nucleoside analogs: cellular- and kinetic parameters of efficacy, susceptibility and resistance selection.

Nucleoside analogs (NAs) are used to treat numerous viral infections and cancer. They compete with endogenous nucleotides (dNTP/NTP) for incorporation into nascent DNA/RNA and inhibit replication by preventing subsequent primer extension. To date, an integrated mathematical model that could allow the analysis of their mechanism of action, of the various resistance mechanisms, and their effect on viral fitness is still lacking. We present the first mechanistic mathematical model of polymerase inhibition by NAs that takes into account the reversibility of polymerase inhibition. Analytical solutions for the model point out the cellular- and kinetic aspects of inhibition. Our model correctly predicts for HIV-1 that resistance against nucleoside analog reverse transcriptase inhibitors (NRTIs) can be conferred by decreasing their incorporation rate, increasing their excision rate, or decreasing their affinity for the polymerase enzyme. For all analyzed NRTIs and their combinations, model-predicted macroscopic parameters (efficacy, fitness and toxicity) were consistent with observations. NRTI efficacy was found to greatly vary between distinct target cells. Surprisingly, target cells with low dNTP/NTP levels may not confer hyper-susceptibility to inhibition, whereas cells with high dNTP/NTP contents are likely to confer natural resistance. Our model also allows quantification of the selective advantage of mutations by integrating their effects on viral fitness and drug susceptibility. For zidovudine triphosphate (AZT-TP), we predict that this selective advantage, as well as the minimal concentration required to select thymidine-associated mutations (TAMs) are highly cell-dependent. The developed model allows studying various resistance mechanisms, inherent fitness effects, selection forces and epistasis based on microscopic kinetic data. It can readily be embedded in extended models of the complete HIV-1 reverse transcription process, or analogous processes in other viruses and help to guide drug development and improve our understanding of the mechanisms of resistance development during treatment.