An optimal antibiotic selection framework for Sepsis patients using Artificial Intelligence.

In this work we present OptAB, the first completely data-driven online-updateable antibiotic selection model based on Artificial Intelligence for Sepsis patients accounting for side-effects. OptAB performs an iterative optimal antibiotic selection for real-world Sepsis patients focussing on minimizing the Sepsis-related organ failure score (SOFA-Score) as treatment success while accounting for nephrotoxicity and hepatotoxicity as serious antibiotic side-effects. OptAB provides disease progression forecasts for (combinations of) the antibiotics Vancomycin, Ceftriaxone and Piperacillin/Tazobactam and learns realistic treatment influences on the SOFA-Score and the laboratory values creatinine, bilirubin total and alanine-transaminase indicating possible side-effects.

Identification of antibody-resistant SARS-CoV-2 mutants via N4-Hydroxycytidine mutagenesis.

Monoclonal antibodies targeting the Spike protein of SARS-CoV-2 are effective against COVID-19 and might mitigate future pandemics. However, their efficacy is challenged by the emergence of antibody-resistant virus variants. We developed a method to efficiently identify such resistant mutants based on selection from mutagenized virus pools.

Profiling Numerical and Structural Chromosomal Instability in Different Cancer Types.

Many cancers display whole chromosome instability (W-CIN) and structural chromosomal instability (S-CIN), referring to increased rates of acquiring numerically and structurally abnormal chromosome changes. This protocol provides detailed steps to analyze the W-CIN and S-CIN across cancer types, intending to leverage large-scale bulk sequencing and SNP array data complemented with the computational models to gain a better understanding of W-CIN and S-CIN.

N4-hydroxycytidine, the active compound of Molnupiravir, promotes SARS-CoV-2 mutagenesis and escape from a neutralizing nanobody.

N4-hydroxycytidine (NHC), the active compound of the drug Molnupiravir, is incorporated into SARS-CoV-2 RNA, causing false base pairing. The desired result is an "error catastrophe," but this bears the risk of mutated virus progeny. To address this experimentally, we propagated the initial SARS-CoV-2 strain in the presence of NHC.

Machine learning models for predicting severe COVID-19 outcomes in hospitals.

The aim of this observational retrospective study is to improve early risk stratification of hospitalized Covid-19 patients by predicting in-hospital mortality, transfer to intensive care unit (ICU) and mechanical ventilation from electronic health record data of the first 24 h after admission. Our machine learning model predicts in-hospital mortality (AUC = 0.918), transfer to ICU (AUC = 0.

External Validation of COVID-19 Risk Scores during Three Waves of Pandemic in a German Cohort-A Retrospective Study.

Several risk scores were developed during the COVID-19 pandemic to identify patients at risk for critical illness as a basic step to personalizing medicine even in pandemic circumstances. However, the generalizability of these scores with regard to different populations, clinical settings, healthcare systems, and new epidemiological circumstances is unknown. The aim of our study was to compare the predictive validity of qSOFA, CRB65, NEWS, COVID-GRAM, and 4C-Mortality score.

Increased replication origin firing links replication stress to whole chromosomal instability in human cancer.

Chromosomal instability (CIN) is a hallmark of cancer and comprises structural CIN (S-CIN) and numerical or whole chromosomal CIN (W-CIN). Recent work indicated that replication stress (RS), known to contribute to S-CIN, also affects mitotic chromosome segregation, possibly explaining the common co-existence of S-CIN and W-CIN in human cancer. Here, we show that RS-induced increased origin firing is sufficient to trigger W-CIN in human cancer cells.

Generation of realistic synthetic data using Multimodal Neural Ordinary Differential Equations.

Individual organizations, such as hospitals, pharmaceutical companies, and health insurance providers, are currently limited in their ability to collect data that are fully representative of a disease population. This can, in turn, negatively impact the generalization ability of statistical models and scientific insights. However, sharing data across different organizations is highly restricted by legal regulations.

Stemness Correlates Inversely with MHC Class I Expression in Pediatric Small Round Blue Cell Tumors.

Recently, immunotherapeutic approaches have become a feasible option for a subset of pediatric cancer patients. Low MHC class I expression hampers the use of immunotherapies relying on antigen presentation. A well-established stemness score (mRNAsi) was determined using the bulk transcriptomes of 1134 pediatric small round blue cell tumors.

Distinct and Common Features of Numerical and Structural Chromosomal Instability across Different Cancer Types.

A large proportion of tumours is characterised by numerical or structural chromosomal instability (CIN), defined as an increased rate of gaining or losing whole chromosomes (W-CIN) or of accumulating structural aberrations (S-CIN). Both W-CIN and S-CIN are associated with tumourigenesis, cancer progression, treatment resistance and clinical outcome. Although W-CIN and S-CIN can co-occur, they are initiated by different molecular events.

Loss of USP28 and SPINT2 expression promotes cancer cell survival after whole genome doubling.

Background: Whole genome doubling is a frequent event during cancer evolution and shapes the cancer genome due to the occurrence of chromosomal instability. Yet, erroneously arising human tetraploid cells usually do not proliferate due to p53 activation that leads to CDKN1A expression, cell cycle arrest, senescence and/or apoptosis.

Methods: To uncover the barriers that block the proliferation of tetraploids, we performed a RNAi mediated genome-wide screen in a human colorectal cancer cell line (HCT116).

MFmap: A semi-supervised generative model matching cell lines to tumours and cancer subtypes.

Translating in vitro results from experiments with cancer cell lines to clinical applications requires the selection of appropriate cell line models. Here we present MFmap (model fidelity map), a machine learning model to simultaneously predict the cancer subtype of a cell line and its similarity to an individual tumour sample. The MFmap is a semi-supervised generative model, which compresses high dimensional gene expression, copy number variation and mutation data into cancer subtype informed low dimensional latent representations.

Systems approaches identify the consequences of monosomy in somatic human cells.

Chromosome loss that results in monosomy is detrimental to viability, yet it is frequently observed in cancers. How cancers survive with monosomy is unknown. Using p53-deficient monosomic cell lines, we find that chromosome loss impairs proliferation and genomic stability.

Searching for Errors in Models of Complex Dynamic Systems.

Mathematical modeling is seen as a key step to understand, predict, and control the temporal dynamics of interacting systems in such diverse areas like physics, biology, medicine, and economics. However, for large and complex systems we usually have only partial knowledge about the network, the coupling functions, and the interactions with the environment governing the dynamic behavior. This incomplete knowledge induces structural model errors which can in turn be the cause of erroneous model predictions or misguided interpretations.

The p53/p73 - p21 tumor suppressor axis guards against chromosomal instability by restraining CDK1 in human cancer cells.

Whole chromosome instability (W-CIN) is a hallmark of human cancer and contributes to the evolvement of aneuploidy. W-CIN can be induced by abnormally increased microtubule plus end assembly rates during mitosis leading to the generation of lagging chromosomes during anaphase as a major form of mitotic errors in human cancer cells. Here, we show that loss of the tumor suppressor genes TP53 and TP73 can trigger increased mitotic microtubule assembly rates, lagging chromosomes, and W-CIN.

Germline ancestry influences the evolutionary disease course in lung adenocarcinomas.

Precision medicine relies on targeting specific somatic alterations present in a patient's tumor. However, the extent to which germline ancestry may influence the somatic burden of disease has received little attention. We estimated the genetic ancestry of non-small-cell lung cancer (NSCLC) patients and performed an in-depth analysis of the influence of genetic ancestry on the evolutionary disease course.

SEEDS: data driven inference of structural model errors and unknown inputs for dynamic systems biology.

Summary: Dynamic models formulated as ordinary differential equations can provide information about the mechanistic and causal interactions in biological systems to guide targeted interventions and to design further experiments. Inaccurate knowledge about the structure, functional form and parameters of interactions is a major obstacle to mechanistic modeling. A further challenge is the open nature of biological systems which receive unknown inputs from their environment.

A Bayesian approach to estimating hidden variables as well as missing and wrong molecular interactions in ordinary differential equation-based mathematical models.

Ordinary differential equations (ODEs) are a popular approach to quantitatively model molecular networks based on biological knowledge. However, such knowledge is typically restricted. Wrongly modelled biological mechanisms as well as relevant external influence factors that are not included into the model are likely to manifest in major discrepancies between model predictions and experimental data.

Modelling and mathematical analysis of the M$_{2}$ receptor-dependent joint signalling and secondary messenger network in CHO cells.

The muscarinic M$_{2}$ receptor is a prominent member of the GPCR family and strongly involved in heart diseases. Recently published experimental work explored the cellular response to iperoxo-induced M$_{2}$ receptor stimulation in Chinese hamster ovary (CHO) cells. To better understand these responses, we modelled and analysed the muscarinic M$_{2}$ receptor-dependent signalling pathway combined with relevant secondary messenger molecules using mass action.

Correction: The Subclonal Architecture of Metastatic Breast Cancer: Results from a Prospective Community-Based Rapid Autopsy Program "CASCADE".

[This corrects the article DOI: 10.1371/journal.pmed.

The Subclonal Architecture of Metastatic Breast Cancer: Results from a Prospective Community-Based Rapid Autopsy Program "CASCADE".

Background: Understanding the cancer genome is seen as a key step in improving outcomes for cancer patients. Genomic assays are emerging as a possible avenue to personalised medicine in breast cancer. However, evolution of the cancer genome during the natural history of breast cancer is largely unknown, as is the profile of disease at death.

DNA replication stress mediates APOBEC3 family mutagenesis in breast cancer.

Background: The APOBEC3 family of cytidine deaminases mutate the cancer genome in a range of cancer types. Although many studies have documented the downstream effects of APOBEC3 activity through next-generation sequencing, less is known about their upstream regulation. In this study, we sought to identify a molecular basis for APOBEC3 expression and activation.

Learning (from) the errors of a systems biology model.

Mathematical modelling is a labour intensive process involving several iterations of testing on real data and manual model modifications. In biology, the domain knowledge guiding model development is in many cases itself incomplete and uncertain. A major problem in this context is that biological systems are open.

Mathematical Modelling of Cation Transport and Regulation in Yeast.

Mathematical modelling of ion transport is a strategy to understand the complex interplay between various ionic species and their transporters. Such models should provide new insights and suggest new interesting experiments. Two essential variables in models for ion transport and control are the membrane potential and the intracellular pH, which generates an additional layer of complexity absent from many other models of biochemical reaction pathways.