Incorporating temporal dynamics of mutations to enhance the prediction capability of antiretroviral therapy's outcome for HIV-1.

MOTIVATION: In predicting HIV therapy outcomes, a critical clinical question is whether using historical information can enhance predictive capabilities compared with current or latest available data analysis. This study analyses whether historical knowledge, which includes viral mutations detected in all genotypic tests before therapy, their temporal occurrence, and concomitant viral load measurements, can bring improvements. We introduce a method to weigh mutations, considering the previously enumerated factors and the reference mutation-drug Stanford resistance tables. We compare a model encompassing history (H) with one not using this information (NH). RESULTS: The H-model demonstrates superior discriminative ability, with a higher ROC-AUC score (76.34%) than the NH-model (74.98%). Wilcoxon test results confirm significant improvement of predictive accuracy for treatment outcomes through incorporating historical information. The increased performance of the H-model might be attributed to its consideration of latent HIV reservoirs, probably obtained when leveraging historical information. The findings emphasize the importance of temporal dynamics in acquiring mutations. However, our result also shows that prediction accuracy remains relatively high even when no historical information is available. AVAILABILITY AND IMPLEMENTATION: This analysis was conducted using the Euresist Integrated DataBase (EIDB). For further validation, we encourage reproducing this study with the latest release of the EIDB, which can be accessed upon request through the Euresist Network.

Torque Teno Virus load is associated with CDC stage and CD4+ cell count in people living with HIV but unrelated to AIDS-defining events and Human Pegivirus load.

BACKGROUND: Torque Teno Virus (TTV) is a non-enveloped, circular single-strand DNA virus and part of the human virome. The replication of TTV was related to the immune status in patients treated with immunosuppressive drugs after organ transplantation. We hypothesize that TTV load could be an additional marker for immune function in people living with HIV (PLWH). METHODS: In this analysis serum samples of PLWH from the RESINA multicenter cohort were reanalysed for TTV. Investigated clinical and epidemiological parameters included Pegivirus (HPgV) load, age, sex, HIV load, CD4+ cell count (CDC 1, 2, 3) and CDC clinical stages (1993 CDC classification system, A, B, C) before initiation of antiretroviral treatment. Regression analysis was used to detect possible associations among parameters. RESULTS: Our analysis confirmed TTV as a strong predictor of CD4+ cell count and CDC class 3. This relationship was used to propose a first classification of TTV load in regard to clinical stage. We found no association with clinical CDC stages A, B and C. HPgV load was inversely correlated with HIV load but not TTV load. CONCLUSIONS: TTV load was associated with immunodeficiency in PLWH. Neither TTV- nor HIV load were predictive for the clinical categories of HIV infection.

Identifying cancer pathway dysregulations using differential causal effects.

MOTIVATION: Signaling pathways control cellular behavior. Dysregulated pathways, for example, due to mutations that cause genes and proteins to be expressed abnormally, can lead to diseases, such as cancer. RESULTS: We introduce a novel computational approach, called Differential Causal Effects (dce), which compares normal to cancerous cells using the statistical framework of causality. The method allows to detect individual edges in a signaling pathway that are dysregulated in cancer cells, while accounting for confounding. Hence, technical artifacts have less influence on the results and dce is more likely to detect the true biological signals. We extend the approach to handle unobserved dense confounding, where each latent variable, such as, for example, batch effects or cell cycle states, affects many covariates. We show that dce outperforms competing methods on synthetic datasets and on CRISPR knockout screens. We validate its latent confounding adjustment properties on a GTEx (Genotype-Tissue Expression) dataset. Finally, in an exploratory analysis on breast cancer data from TCGA (The Cancer Genome Atlas), we recover known and discover new genes involved in breast cancer progression. AVAILABILITY AND IMPLEMENTATION: The method dce is freely available as an R package on Bioconductor (https://bioconductor.org/packages/release/bioc/html/dce.html) as well as on https://github.com/cbg-ethz/dce. The GitHub repository also contains the Snakemake workflows needed to reproduce all results presented here. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Acute orchitis deciphered: Coxsackievirus B strains are the main etiology and their presence in semen is associated with acute inflammation and risk of persistent oligozoospermia.

Although various viruses are considered to be the clinical cause for acute orchitis, it is completely unclear to what extent and which viruses are etiologically involved in acute orchitis and what the clinic and course of these patients are like. Therefore, a prospective study was set up to decipher acute isolated orchitis. Between July 2007 and February 2023, a total of 26 patients with isolated orchitis were recruited and compared with 530 patients with acute epididymitis. We were able to show for isolated orchitis, that (1) orchitis is usually of viral origin (20/26, 77%) and enteroviruses with coxsackievirus B strains (16/26, 62%) are predominant, (2) virus isolates could be received from semen indicating the presence of replication-competent virus particles, (3) a polymerase chain reaction (PCR) for enteroviruses should be conducted using semen provided at the onset of disease, because the virus is not detectable in serum/urine, (4) there is a circannual occurrence with the maximum in summer, (5) orchitis is associated with a characteristic inflammatory cytokine panel in the semen and systemic inflammation, (6) orchitis is usually rapidly self-limiting, and (7) about 30% of patients (6/20) suffer ongoing oligozoospermia. These seven emerging aspects are likely to fundamentally change thinking and clinical practice regarding acute isolated orchitis.

Analysis of mutational history of multidrug-resistant genotypes with a mutagenetic tree model.

Human immunodeficiency virus (HIV) can develop resistance to all antiretroviral drugs. Multidrug resistance, however, is a rare event in modern HIV treatment, but can be life-threatening, particular in patients with very long therapy histories and in areas with limited access to novel drugs. To understand the evolution of multidrug resistance, we analyzed the EuResist database to uncover the accumulation of mutations over time. We hypothesize that the accumulation of resistance mutations is not acquired simultaneously and randomly across viral genotypes but rather tends to follow a predetermined order. The knowledge of this order might help to elucidate potential mechanisms of multidrug resistance. Our evolutionary model shows an almost monotonic increase of resistance with each acquired mutation, including less well-known nucleoside reverse transcriptase (RT) inhibitor-related mutations like K223Q, L228H, and Q242H. Mutations within the integrase (IN) (T97A, E138A/K G140S, Q148H, N155H) indicate high probability of multidrug resistance. Hence, these IN mutations also tend to be observed together with mutations in the protease (PR) and RT. We followed up with an analysis of the mutation-specific error rates of our model given the data. We identified several mutations with unusual rates (PR: M41L, L33F, IN: G140S). This could imply the existence of previously unknown virus variants in the viral quasispecies. In conclusion, our bioinformatics model supports the analysis and understanding of multidrug resistance.

Viral Load Dynamics in SARS-CoV-2 Omicron Breakthrough Infections.

To determine viral dynamics in Omicron breakthrough infections, we measured severe acute respiratory syndrome coronavirus 2 RNA in 206 double-vaccinated or boostered individuals. During the first 3 days following the onset of symptoms, viral loads were significantly higher (cycle threshold [Ct], 21.76) in vaccinated compared to boostered (Ct, 23.14) individuals (P = .029). However, by performing a longitudinal analysis on 32 individuals over 14 days, no difference in the viral load trajectory was observed between double-vaccinated and boostered patients. Our results indicate that booster immunization results in a reduction in detectable viral loads without significantly changing viral load dynamics over time.

Inferring perturbation profiles of cancer samples.

MOTIVATION: Cancer is one of the most prevalent diseases in the world. Tumors arise due to important genes changing their activity, e.g. when inhibited or over-expressed. But these gene perturbations are difficult to observe directly. Molecular profiles of tumors can provide indirect evidence of gene perturbations. However, inferring perturbation profiles from molecular alterations is challenging due to error-prone molecular measurements and incomplete coverage of all possible molecular causes of gene perturbations. RESULTS: We have developed a novel mathematical method to analyze cancer driver genes and their patient-specific perturbation profiles. We combine genetic aberrations with gene expression data in a causal network derived across patients to infer unobserved perturbations. We show that our method can predict perturbations in simulations, CRISPR perturbation screens and breast cancer samples from The Cancer Genome Atlas. AVAILABILITY AND IMPLEMENTATION: The method is available as the R-package nempi at https://github.com/cbg-ethz/nempi and http://bioconductor.org/packages/nempi. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

dCas9-mediated dysregulation of gene expression in human induced pluripotent stem cells during primitive streak differentiation.

CRISPR-based systems have fundamentally transformed our ability to study and manipulate stem cells. We explored the possibility of using catalytically dead Cas9 (dCas9) from S. pyogenes as a platform for targeted epigenetic editing in stem cells to enhance the expression of the eomesodermin gene (EOMES) during differentiation. We observed, however, that the dCas9 protein itself exerts a potential non-specific effect in hiPSCs, affecting the cell's phenotype and gene expression patterns during subsequent directed differentiation. We show that this effect is specific to the condition when cells are cultured in medium that does not actively maintain the pluripotency network, and that the sgRNA-free apo-dCas9 protein itself influences endogenous gene expression. Transcriptomics analysis revealed that a significant number of genes involved in developmental processes and various other genes with non-overlapping biological functions are affected by dCas9 overexpression. This suggests a potential adverse phenotypic effect of dCas9 itself in hiPSCs, which could have implications for when and how CRISPR/Cas9-based tools can be used reliably and safely in pluripotent stem cells.

Single cell network analysis with a mixture of Nested Effects Models.

Motivation: New technologies allow for the elaborate measurement of different traits of single cells under genetic perturbations. These interventional data promise to elucidate intra-cellular networks in unprecedented detail and further help to improve treatment of diseases like cancer. However, cell populations can be very heterogeneous. Results: We developed a mixture of Nested Effects Models (M&NEM) for single-cell data to simultaneously identify different cellular subpopulations and their corresponding causal networks to explain the heterogeneity in a cell population. For inference, we assign each cell to a network with a certain probability and iteratively update the optimal networks and cell probabilities in an Expectation Maximization scheme. We validate our method in the controlled setting of a simulation study and apply it to three data sets of pooled CRISPR screens generated previously by two novel experimental techniques, namely Crop-Seq and Perturb-Seq. Availability and implementation: The mixture Nested Effects Model (M&NEM) is available as the R-package mnem at https://github.com/cbg-ethz/mnem/. Supplementary information: Supplementary data are available at Bioinformatics online.

Inferring modulators of genetic interactions with epistatic nested effects models.

Maps of genetic interactions can dissect functional redundancies in cellular networks. Gene expression profiles as high-dimensional molecular readouts of combinatorial perturbations provide a detailed view of genetic interactions, but can be hard to interpret if different gene sets respond in different ways (called mixed epistasis). Here we test the hypothesis that mixed epistasis between a gene pair can be explained by the action of a third gene that modulates the interaction. We have extended the framework of Nested Effects Models (NEMs), a type of graphical model specifically tailored to analyze high-dimensional gene perturbation data, to incorporate logical functions that describe interactions between regulators on downstream genes and proteins. We benchmark our approach in the controlled setting of a simulation study and show high accuracy in inferring the correct model. In an application to data from deletion mutants of kinases and phosphatases in S. cerevisiae we show that epistatic NEMs can point to modulators of genetic interactions. Our approach is implemented in the R-package 'epiNEM' available from https://github.com/cbg-ethz/epiNEM and https://bioconductor.org/packages/epiNEM/.

Analyzing synergistic and non-synergistic interactions in signalling pathways using Boolean Nested Effect Models.

MOTIVATION: Understanding the structure and interplay of cellular signalling pathways is one of the great challenges in molecular biology. Boolean Networks can infer signalling networks from observations of protein activation. In situations where it is difficult to assess protein activation directly, Nested Effect Models are an alternative. They derive the network structure indirectly from downstream effects of pathway perturbations. To date, Nested Effect Models cannot resolve signalling details like the formation of signalling complexes or the activation of proteins by multiple alternative input signals. Here we introduce Boolean Nested Effect Models (B-NEM). B-NEMs combine the use of downstream effects with the higher resolution of signalling pathway structures in Boolean Networks. RESULTS: We show that B-NEMs accurately reconstruct signal flows in simulated data. Using B-NEM we then resolve BCR signalling via PI3K and TAK1 kinases in BL2 lymphoma cell lines. AVAILABILITY AND IMPLEMENTATION: R code is available at https://github.com/MartinFXP/B-NEM (github). The BCR signalling dataset is available at the GEO database (http://www.ncbi.nlm.nih.gov/geo/) through accession number GSE68761. CONTACT: martin-franz-xaver.pirkl@ukr.de, Rainer.Spang@ukr.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SARS-CoV-2 Test-to-Stay in Daycare.

BACKGROUND AND OBJECTIVES: Test-to-stay concepts apply serial testing of children in daycare after exposure to SARS-CoV-2 without use of quarantine. This study aims to assess the safety of a test-to-stay screening in daycare facilities. METHODS: 714 daycare facilities and approximately 50 000 children ≤6 years in Cologne, Germany participated in a SARS-CoV-2 Pool-polymerase chain reaction (PCR) screening from March 2021 to April 2022. The screening initially comprised post-exposure quarantine and was adapted to a test-to-stay approach during its course. To assess safety of the test-to-stay approach, we explored potential changes in frequencies of infections among children after the adaptation to the test-to-stay approach by applying regression discontinuity in time (RDiT) analyses. To this end, PCR-test data were linked with routinely collected data on reported infections in children and analyzed using ordinary least squares regressions. RESULTS: 219 885 Pool-PCRs and 352 305 Single-PCRs were performed. 6440 (2.93%) Pool-PCRs tested positive, and 17 208 infections in children were reported. We estimated that during a period of 30 weeks, the test-to-stay concept avoided between 7 and 20 days of quarantine per eligible daycare child. RDiT revealed a 26% reduction (Exp. Coef: 0.74, confidence interval 0.52-1.06) in infection frequency among children and indicated no significant increase attributable to the test-to-stay approach. This result was not sensitive to adjustments for 7-day incidence, season, SARS-CoV-2 variant, and socioeconomic status. CONCLUSIONS: Our analyses provide evidence that suggest safety of the test-to-stay approach compared with quarantine measures. This approach offers a promising option to avoid use of quarantine after exposure to respiratory pathogens in daycare settings.

Interplay between HIV and Human Pegivirus (HPgV) Load in Co-Infected Patients: Insights from Prevalence and Genotype Analysis.

Human pegivirus (HPgV) is transmitted through sexual or parenteral exposure and is common among patients receiving blood products. HPgV is associated with lower levels of human immunodeficiency virus (HIV) RNA and better survival among HIV-infected patients. This study aimed to investigate the prevalence of HPgV and determine its subtypes in HIV-infected individuals living in Istanbul, which has the highest rate of HIV infection in Türkiye. Total RNA extraction from plasma, cDNA synthesis, and nested PCR were performed for HPgV on plasma samples taken from 351 HIV-1-infected patients. The HPgV viral load was quantified on HPgV-positive samples. HPgV genotyping was performed by sequencing the corresponding amplicons. In the present study, the overall prevalence of HPgV RNA in HIV-infected patients was 27.3%. HPgV subtypes 1, 2a, and 2b were found, with subtype 2a being the most frequent (91.6%). Statistical analysis of HIV-1 viral load on HPgV viral load showed an opposing correlation between HIV-1 and HPgV loads. In conclusion, these data show that HPgV infection is common among HIV-positive individuals in Istanbul, Türkiye. Further comprehensive studies are needed to clarify both the cellular and molecular pathways of these two infections and to provide more information on the effect of HPgV on the course of the disease in HIV-infected individuals.

Autologous blood transfusion reduces the requirement for perioperative allogenic blood transfusion in patients undergoing major hepatopancreatobiliary surgery: a retrospective cohort study.

INTRODUCTION: Major hepatopancreatobiliary surgery is associated with a risk of major blood loss. The authors aimed to assess whether autologous transfusion of blood salvaged intraoperatively reduces the requirement for postoperative allogenic transfusion in this patient cohort. MATERIALS AND METHODS: In this single centre study, information from a prospective database of 501 patients undergoing major hepatopancreatobiliary resection (2015-2022) was analysed. Patients who received cell salvage ( n =264) were compared with those who did not ( n =237). Nonautologous (allogenic) transfusion was assessed from the time of surgery to 5 days postsurgery, and blood loss tolerance was calculated using the Lemmens-Bernstein-Brodosky formula. Multivariate analysis was used to identify factors associated with allogenic blood transfusion avoidance. RESULTS: 32% of the lost blood volume was replaced through autologous transfusion in patients receiving cell salvage. Although the cell salvage group experienced significantly higher intraoperative blood loss compared with the noncell salvage group (1360 ml vs. 971 ml, P =0.0005), they received significantly less allogenic red blood cell units (1.5 vs. 0.92 units/patient, P =0.03). Correction of blood loss tolerance in patients who underwent cell salvage was independently associated with avoidance of allogenic transfusion (Odds ratio 0.05 (0.006-0.38) P =0.005). In a subgroup analysis, cell salvage use was associated with a significant reduction in 30-day mortality in patients undergoing major hepatectomy (6 vs. 1%, P =0.04). CONCLUSION: Cell salvage use was associated with a reduction in allogenic blood transfusion and a reduction in 30-day mortality in patients undergoing major hepatectomy. Prospective trials are warranted to understand whether the use of cell salvage should be routinely utilised for major hepatectomy.

Impaired humoral immunity to BQ.1.1 in convalescent and vaccinated patients.

Determining SARS-CoV-2 immunity is critical to assess COVID-19 risk and the need for prevention and mitigation strategies. We measured SARS-CoV-2 Spike/Nucleocapsid seroprevalence and serum neutralizing activity against Wu01, BA.4/5 and BQ.1.1 in a convenience sample of 1,411 patients receiving medical treatment in the emergency departments of five university hospitals in North Rhine-Westphalia, Germany, in August/September 2022. 62% reported underlying medical conditions and 67.7% were vaccinated according to German COVID-19 vaccination recommendations (13.9% fully vaccinated, 54.3% one booster, 23.4% two boosters). We detected Spike-IgG in 95.6%, Nucleocapsid-IgG in 24.0%, and neutralization against Wu01, BA.4/5 and BQ.1.1 in 94.4%, 85.0%, and 73.8% of participants, respectively. Neutralization against BA.4/5 and BQ.1.1 was 5.6- and 23.4-fold lower compared to Wu01. Accuracy of S-IgG detection for determination of neutralizing activity against BQ.1.1 was reduced substantially. We explored previous vaccinations and infections as correlates of BQ.1.1 neutralization using multivariable and Bayesian network analyses. Given a rather moderate adherence to COVID-19 vaccination recommendations, this analysis highlights the need to improve vaccine-uptake to reduce the COVID-19 risk of immune evasive variants. The study was registered as clinical trial (DRKS00029414).

Hierarchy of TGFß/SMAD, Hippo/YAP/TAZ, and Wnt/ß-catenin signaling in melanoma phenotype switching.

In melanoma, a switch from a proliferative melanocytic to an invasive mesenchymal phenotype is based on dramatic transcriptional reprogramming which involves complex interactions between a variety of signaling pathways and their downstream transcriptional regulators. TGFß/SMAD, Hippo/YAP/TAZ, and Wnt/ß-catenin signaling pathways are major inducers of transcriptional reprogramming and converge at several levels. Here, we report that TGFß/SMAD, YAP/TAZ, and ß-catenin are all required for a proliferative-to-invasive phenotype switch. Loss and gain of function experimentation, global gene expression analysis, and computational nested effects models revealed the hierarchy between these signaling pathways and identified shared target genes. SMAD-mediated transcription at the top of the hierarchy leads to the activation of YAP/TAZ and of ß-catenin, with YAP/TAZ governing an essential subprogram of TGFß-induced phenotype switching. Wnt/ß-catenin signaling is situated further downstream and exerts a dual role: it promotes the proliferative, differentiated melanoma cell phenotype and it is essential but not sufficient for SMAD or YAP/TAZ-induced phenotype switching. The results identify epistatic interactions among the signaling pathways underlying melanoma phenotype switching and highlight the priorities in targets for melanoma therapy.

Genome-Scale CRISPR Screening in Human Intestinal Organoids Identifies Drivers of TGF-ß Resistance.

Forward genetic screens with genome-wide CRISPR libraries are powerful tools for resolving cellular circuits and signaling pathways. Applying this technology to organoids, however, has been hampered by technical limitations. Here we report improved accuracy and robustness for pooled-library CRISPR screens by capturing sgRNA integrations in single organoids, substantially reducing required cell numbers for genome-scale screening. We applied our approach to wild-type and APC mutant human intestinal organoids to identify genes involved in resistance to TGF-ß-mediated growth restriction, a key process during colorectal cancer progression, and validated hits including multiple subunits of the tumor-suppressive SWI/SNF chromatin remodeling complex. Mutations within these genes require concurrent inactivation of APC to promote TGF-ß resistance and attenuate TGF-ß target gene transcription. Our approach can be applied to a variety of assays and organoid types to facilitate biological discovery in primary 3D tissue models.