Motivation:The emergence of multidrug class resistance (MDR) in Human Immunodeficiency Virus (HIV) is a rare but significant challenge in antiretroviral therapy (ART). MDR, which may arise from prolonged drug exposure, treatment failures, or transmission of resistant strains, accelerates disease progression and poses particular challenges in resource-limited settings with restricted access to resistance testing and advanced therapies. Early prediction of future MDR development is important to inform therapeutic decisions and mitigate its occurrence. Results:In this study, we employ various machine learning classifiers to predict future resistance to all four major antiretroviral drug classes using features extracted from clinical HIV sequence data. We systematically explore several variations of the problem that differ in the pre-existing resistance level and the temporal gap between sample collection and observed MDR occurrence. Our models show the ability to predict multidrug class resistance even in the most challenging variations, albeit at a reduced accuracy. Feature importance analysis reveals that our models primarily utilize known drug resistance mutations for easier classification tasks, but rely on new mutations for the difficult task of distinguishing four class drug resistance from three class drug resistance. Availability and implementation:All analysis was performed using the Euresist Integrated DataBase (EIDB). Researchers wishing to reproduce, validate or extend these findings can request access to the latest EIDB release via the Euresist Network.

BACKGROUND:Respiratory viral diseases are one of the greatest challenges facing our healthcare system, with them being one of the main causes of death. This has been demonstrated once again by the impact of the SARS-CoV-2 pandemic in recent years. We study the impact of the SARS-CoV-2 pandemic on the prevalence of respiratory viruses by analysing a subset of the Clinical Virology network database, covering 2,216,198 samples tested for 18 different viral pathogens in the time span from 2010 to 2024. METHODS:We calculated the prevalence of 17 respiratory viruses before and after onset of the SARS-CoV-2 pandemic and compared the degree of seasonality shift with a newly developed a metric dubbed seasonal disruption index. In addition, we compared coinfection statistics prior to and after the pandemic onset, and also studied the correlation of infection counts with non-pharmaceutical interventions in the time frame from early 2020 to end of 2022. RESULTS:We found that the viral pathogens show a varying degree of seasonality disruption. It is largest among those that are known to show a highly seasonal behavior, namely Influenza and RSV, the latter having the highest seasonal disruption index. Most perennial viruses continued to appear throughout the year. Coinfections occurred before and after the pandemic; patterns before and after pandemic onset are surprisingly similar. The occurrence of most viruses is nonlinearly correlated with the degree of non-pharmaceutical interventions. CONCLUSION:The SARS-CoV-2 pandemic had a considerable impact on the occurrence and seasonality of other respiratory viruses. While nearly all seasonality patterns were initially disrupted due to the heavy non-pharmaceutical interventions, viruses are regaining their pre-pandemic seasonality.

Background Fracture detection by artificial intelligence and especially Deep Convolutional Neural Networks (DCNN) is a topic of growing interest in current orthopaedic and radiological research. As learning a DCNN usually needs a large amount of training data, mostly frequent fractures as well as conventional X-ray are used. Therefore, less common fractures like acetabular fractures (AF) are underrepresented in the literature. The aim of this pilot study was to establish a DCNN for detection of AF using computer tomography (CT) scans. Methods Patients with an acetabular fracture were identified from the monocentric consecutive pelvic injury registry at the BG Trauma Center XXX from 01/2003-12/2019. All patients with unilateral AF and CT scans available in DICOM-format were included for further processing. All datasets were automatically anonymised and digitally post-processed. Extraction of the relevant region of interests was performed and the technique of data augmentation (DA) was implemented to artificially increase the number of training samples. A DCNN based on Med3D was used for autonomous fracture detection, using global average pooling (GAP) to reduce overfitting. Results From a total of 2,340 patients with a pelvic fracture, 654 patients suffered from an AF. After screening and post-processing of the datasets, a total of 159 datasets were enrolled for training of the algorithm. A randomassignment into training datasets (80%) and test datasets (20%) was performed. The technique of bone area extraction, DA and GAP increased the accuracy of fracture detection from 58.8% (native DCNN) up to an accuracy of 82.8% despite the low number of datasets. Conclusion The accuracy of fracture detection of our trained DCNN is comparable to published values despite the low number of training datasets. The techniques of bone extraction, DA and GAP are useful for increasing the detection rates of rare fractures by a DCNN. Based on the used DCNN in combination with the described techniques from this pilot study, the possibility of an automatic fracture classification of AF is under investigation in a multicentre study.

Human immunodeficiency virus type 1 (HIV-1)-neutralizing antibodies (nAbs) that prevent infection are the main goal of HIV vaccine discovery. But as no nAb-eliciting vaccines are yet available, only data from HIV-1 neutralizers-persons with HIV-1 who naturally develop broad and potent nAbs-can inform about the dynamics and durability of nAb responses in humans, knowledge which is crucial for the design of future HIV-1 vaccine regimens. To address this, we assessed HIV-1-neutralizing immunoglobulin G (IgG) from 2,354 persons with HIV-1 on or off antiretroviral therapy (ART). Infection with non-clade B viruses, CD4(+) T cell counts <200 mu l(-1), being off ART and a longer time off ART were independent predictors of a more potent and broad neutralization. In longitudinal analyses, we found nAb half-lives of 9.3 and 16.9 years in individuals with no- or low-level viremia, respectively, and 4.0 years in persons who newly initiated ART. Finally, in a potent HIV-1 neutralizer, we identified lower fractions of serum nAbs and of nAb-encoding memory B cells after ART initiation, suggesting that a decreasing neutralizing serum activity after antigen withdrawal is due to lower levels of nAbs. These results collectively show that HIV-1-neutralizing responses can persist for several years, even at low antigen levels, suggesting that an HIV-1 vaccine may elicit a durable nAb response.

Background Lower respiratory tract infections are among the main causes of death. Although there are many respiratory viruses, diagnostic efforts are focused mainly on influenza. The Respiratory Viruses Network (RespVir) collects infection data, primarily from German university hospitals, for a high diversity of infections by respiratory pathogens. In this study, we computationally analysed a subset of the RespVir database, covering 217,150 samples tested for 17 different viral pathogens in the time span from 2010 to 2019. Methods We calculated the prevalence of 17 respiratory viruses, analysed their seasonality patterns using information-theoretic measures and agglomerative clustering, and analysed their propensity for dual infection using a new metric dubbed average coinfection exclusion score (ACES). Results After initial data pre-processing, we retained 206,814 samples, corresponding to 1,408,657 performed tests. We found that Influenza viruses were reported for almost the half of all infections and that they exhibited the highest degree of seasonality. Coinfections of viruses are frequent; the most prevalent coinfection was rhinovirus/bocavirus and most of the virus pairs had a positive ACES indicating a tendency to exclude each other regarding infection. Conclusions The analysis of respiratory viruses dynamics in monoinfection and coinfection contributes to the prevention, diagnostic, treatment, and development of new therapeutics. Data obtained from multiplex testing is fundamental for this analysis and should be prioritized over single pathogen testing.

Advanced age is a key risk factor for morbidity and mortality associated with SARS-CoV-2 infection. Therefore, older adults have generally been prioritised for COVID-19 vaccination. Moreover, lower vaccine immunogenicity and more pronounced waning of humoral immunity in older individuals than in younger individuals have prompted early booster campaigns.1Levin EG Lustig Y Cohen C et al.Waning immune humoral response to BNT162b2 COVID-19 vaccine over 6 months.N Engl J Med. 2021; 385: e84Crossref PubMed Scopus (312) Google Scholar The omicron variant (B.1.1.529) of SARS-CoV-2 shows substantial resistance to vaccine-induced serum neutralising activity and hence is of particular concern.2Cele S Jackson L Khoury DS et al.Omicron extensively but incompletely escapes Pfizer BNT162b2 neutralization.Nature. 2021; (published online Dec 23.)https://doi.org/10.1038/s41586-021-04387-1Crossref Scopus (178) Google Scholar Although booster immunisations can elicit omicron-neutralising activity,3Schmidt F Muecksch F Weisblum Y et al.Plasma neutralization of the SARS-CoV-2 omicron variant.N Engl J Med. 2021; 386: 599-601Crossref PubMed Scopus (78) Google Scholar their immediate and long-term effects in older individuals are not known, which limits informed guidance on vaccination strategies in this susceptible population. We longitudinally determined SARS-CoV-2-neutralising serum activity in a prospective cohort of 37 individuals with a median age of 82 years (range 76–96; appendix p 2).4Tober-Lau P Schwarz T Vanshylla K et al.Long-term immunogenicity of BNT162b2 vaccination in older people and younger health-care workers.Lancet Respir Med. 2021; 9: e104-e105Summary Full Text Full Text PDF PubMed Scopus (19) Google Scholar Individuals were recruited at a general practitioner surgery in Berlin, Germany, with the support of the Charité-Universitätsmedizin Berlin, and received their first COVID-19 vaccination on Jan 15, 2021. Participants were followed-up for 10 months after their second dose of BNT162b2 (Pfizer-BioNTech) and up to 4·5 months after a booster dose of BNT162b2. We determined geometric mean 50% inhibitory serum dilutions (ID50) against the Wu01 vaccine strain as well as the delta (B.1.617.2) and omicron variants (BA.1) using an in-house pseudovirus assay. After their second dose of BNT162b2, serum samples were collected at 1 month (median 26 days [IQR 25–27]; visit 1) and 5 months (median 153 days [151–154]; visit 2) of follow-up. Two BNT162b2 doses induced detectable Wu01-neutralising and delta-neutralising activity in most individuals (35 [95%] of 37 for Wu01 and 31 [84%] for delta), while activity against omicron was not or only minimally detectable (figure; appendix p 3). Over the next 4 months, Wu01-neutralising titres decreased 6-fold (from a geometric mean ID50 of 260 on visit 1 to 42 on visit 2) and delta-neutralising titres decreased 7-fold (from a geometric mean ID50 of 89 to 13). All individuals received a booster dose of BNT162b2 at 7 months (median 209 days [IQR 189–228]) and early post-boost serum samples were obtained 1 month later (median 23 days [IQR 21–29]; visit 3). Booster immunisation resulted in an over 50-fold increase in Wu01-neutralising and delta-neutralising titres (to a geometric mean serum ID50 of 2912 for Wu01 and 750 for delta). The BNT162b2 booster elicited robust omicron-neutralising activity (to a geometric mean ID50 of 256) in 33 (89%) of 37 participants (figure; appendix p 3). To determine post-boost durability of SARS-CoV-2-neutralising activity in older adults, we obtained samples 3·5 months (median 106 days [IQR 86–125]) after booster vaccination (visit 4). Neutralising titres decreased by 2·7-fold (to geometric mean ID50 of 1077) against the Wu01 variant, 2·3-fold (to 345) against the delta variant, and 3·0-fold (to 85) against the omicron variant. However, most individuals maintained detectable serum neutralisation against Wu01 (36 [97%] of 37), delta (34 [92%]), and omicron (30 [81%]; corresponding to 30 [91%] of 34 individuals with activity at the early post-boost visit [ie, visit 3]). To assess the rate of decrease in neutralising activity, we separately analysed the pre-booster (visit 1–2) and post-booster (visit 3–4) periods using linear mixed-effect models (appendix p 4). Neutralising activity against the variants showed similar changes, with estimated post-booster half-lives of 52 days (95% CI 46–59) for the Wu01 variant, 64 days (52–83) for the delta variant, and 41 days (34–52) for the omicron variant (appendix p 4). In the absence of omicron variant-specific vaccines, booster immunisations are crucial to restore vaccine effectiveness against severe outcomes.5Barda N Dagan N Cohen C et al.Effectiveness of a third dose of the BNT162b2 mRNA COVID-19 vaccine for preventing severe outcomes in Israel: an observational study.Lancet. 2021; 398: 2093-2100Summary Full Text Full Text PDF PubMed Scopus (199) Google Scholar We found that booster immunisations can effectively elicit omicron variant-neutralising activity in the majority of older individuals. Although our analyses were limited to four sampling timepoints and different pre-boost and post-boost observational periods, our results suggest that neutralising activity against different variants decreases at similar decay rates. Although neutralising activity does not equal protection from infection, our findings suggest that previous observations on waning humoral immunity can guide subsequent booster vaccination strategies in the older population. KV, HG, and FKl are listed as inventors on patent applications regarding SARS-CoV-2-neutralising antibodies filed by the University of Cologne. All other authors declare no competing interests. KV, PT-L, and HG contributed equally. FKu, LES, and FKl contributed equally. Acknowledgments are listed in the appendix (p 7). Download .pdf (.78 MB) Help with pdf files Supplementary appendix

Elderly individuals are at high risk for severe COVID-19. Due to modest vaccine responses compared to younger individuals and the time elapsed since prioritized vaccinations, the emerging immune-evasive Omicron variant of SARS-CoV-2 is a particular concern for the elderly. Here we longitudinally determined SARS-CoV-2-neutralizing serum activity against different variants in a cohort of 37 individuals with a median age of 82 years. Participants were followed for 10 months after an initial two-dose BNT162b2 vaccination and up to 4.5 months after a BNT162b2 booster. Detectable Omicron-neutralizing activity was nearly absent after two vaccinations but elicited in 89% of individuals by the booster immunization. Neutralizing titers against the Wu01, Delta, and Omicron variants showed similar post-boost declines and 81% of individuals maintained detectable activity against Omicron. Our study demonstrates the mRNA booster effectiveness in inducing anti-Omicron activity and provides critical information on vaccine response durability in the highly vulnerable elderly population.

The identification and isolation of highly infectious SARS-CoV-2-infected individuals is an important public health strategy. Rapid antigen detection tests (RADT) are promising candidates for large-scale screenings due to timely results and feasibility for on-site testing. Nonetheless, the diagnostic performance of RADT in detecting infectious individuals is yet to be fully determined. Two combined oro- and nasopharyngeal swabs were collected from individuals at a routine SARS-CoV-2 diagnostic center. Side-by-side evaluations of RT-qPCR and RADT as well as live virus cultures of positive samples were performed to determine the sensitivity of the Standard Q COVID-19 Ag Test (SD Biosensor/Roche) in detecting SARS-CoV-2-infected individuals with cultivable virus. A total of 2,028 samples were tested and 118 virus cultures inoculated. SARS-CoV-2 infection was detected in 210 samples by RT-qPCR, representing a positive rate of 10.36%. The Standard Q COVID-19 Ag Test yielded a positive result in 92 (4.54%) samples resulting in an overall sensitivity and specificity of 42.86% and 99.89%. For adjusted Ct values <20, <25, and <30 the RADT reached sensitivities of 100%, 98.15%, and 88.64%, respectively. All 29 culture positive samples were detected by RADT. While overall sensitivity was low, Standard Q COVID-19 RADT reliably detected patients with high RNA loads. Additionally, negative RADT results fully corresponded with the lack of viral cultivability in Vero E6 cells. These results indicate that RADT can be a valuable tool for the detection of individuals that are likely to transmit SARS-CoV-2. RADT testing could therefore guide public health testing strategies to combat the COVID-19 pandemic.

a Institute of Virology, Faculty of Medicine and University Hospital Cologne, University of 9 Cologne; 50931 Cologne, Germany. 10 b Methods in Medical Informatics, Department of Computer Science, University of Tübingen; 11 72076 Tübingen, Germany. 12 c German Center for Infection Research, Partner Site Bonn-Cologne; 50931 Cologne, 13 Germany. 14 d Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne 15 University of Cologne; 50937 Cologne, Germany. 16 e Center for Molecular Medicine Cologne (CMMC), University of Cologne; 50931 Cologne, 17 Germany. 18 f Faculty of Medicine, University of Tübingen; 72076, Germany. 19 g German Center for Infection Research, Partner Site Tübingen; 72076 Tübingen, Germany. 20 * These authors contributed equally 21

The identification and isolation of highly infectious SARS-CoV-2-infected individuals is an important public health strategy. Rapid antigen detection tests (RADT) are promising candidates for large-scale screenings due to timely results and feasibility for on-site testing.

A detailed understanding of antibody-based SARS-CoV-2 immunity has critical implications for overcoming the COVID-19 pandemic and for informing on vaccination strategies. In this study, we evaluated the dynamics of the SARS-CoV-2 antibody response in a cohort of 963 recovered individuals over a period of 10 months. Investigating a total of 2,146 samples, we detected an initial SARS-CoV-2 antibody response in 94.4% of individuals, with 82% and 79% exhibiting serum and IgG neutralization, respectively. Approximately 3% of recovered patients demonstrated exceptional SARS-CoV-2 neutralizing activity, defining them as ‘elite neutralizers’. These individuals also possessed effective cross-neutralizing IgG antibodies to SARS-CoV-1 without any known prior exposure to this virus. By applying multivariate statistical modeling, we found that sero-reactivity, age, time since disease onset, and fever are key factors predicting SARS-CoV-2 neutralizing activity in mild courses of COVID-19. Investigating longevity of the antibody response, we detected loss of anti-spike reactivity in 13% of individuals 10 months after infection. Moreover, neutralizing activity had an initial half-life of 6.7 weeks in serum versus 30.8 weeks in purified IgG samples indicating the presence of a more stable and long-term memory IgG B cell repertoire in the majority of individuals recovered from COVID-19. Our results demonstrate a broad spectrum of the initial SARS-CoV-2 neutralizing antibody response depending on clinical characteristics, with antibodies being maintained in the majority of individuals for the first 10 months after mild course of COVID-19.

Modern exact algorithms for structure learning in Bayesian networks first compute an exact local score of every candidate parent set, and then find a network structure by combinatorial optimization so as to maximize the global score. This approach assumes that each local score can be computed fast, which can be problematic when the scarcity of the data calls for structured local models or when there are both continuous and discrete variables, for these cases have lacked efficient-to-compute local scores. To address this challenge, we introduce a local score that is based on a class of classification and regression trees. We show that under modest restrictions on the possible branchings in the tree structure, it is feasible to find a structure that maximizes a Bayes score in a range of moderate-size problem instances. In particular, this enables global optimization of the Bayesian network structure, including the local structure. In addition, we introduce a related model class that extends ordinary conditional probability tables to continuous variables by employing an adaptive discretization approach. The two model classes are compared empirically by learning Bayesian networks from benchmark real-world and synthetic data sets. We discuss the relative strengths of the model classes in terms of their structure learning capability, predictive performance, and running time.

To learn a Bayesian network structure from data, one popular approach is to maximize a decomposable likelihood-based score. While various scores have been proposed, they usually assume a uniform prior, or "penalty," over the possible directed acyclic graphs (DAGs); relatively little attention has been paid to alternative priors. We investigate empirically several structure priors in combination with different scores, using benchmark data sets and data sets generated from benchmark networks. Our results suggest that, in practice, priors that strongly favor sparsity perform significantly better than the uniform prior or even the informed variant that is conditioned on the correct number of parents for each node. For an analytic comparison of different priors, we generalize a known recurrence equation for the number of DAGs to accommodate modular weightings of DAGs, a result that is also of independent interest.

Motivation Predictive models are a powerful tool for solving complex problems in computational biology. They are typically designed to predict or classify data coming from the same unknown distribution as the training data. In many real-world settings, however, uncontrolled biological or technical factors can lead to a distribution mismatch between datasets acquired at different times, causing model performance to deteriorate on new data. A common additional obstacle in computational biology is scarce data with many more features than samples. To address these problems, we propose a method for unsupervised domain adaptation that is based on a weighted elastic net. The key idea of our approach is to compare dependencies between inputs in training and test data and to increase the cost of differently behaving features in the elastic net regularization term. In doing so, we encourage the model to assign a higher importance to features that are robust and behave similarly across domains. Results We evaluate our method both on simulated data with varying degrees of distribution mismatch and on real data, considering the problem of age prediction based on DNA methylation data across multiple tissues. Compared with a non-adaptive standard model, our approach substantially reduces errors on samples with a mismatched distribution. On real data, we achieve far lower errors on cerebellum samples, a tissue which is not part of the training data and poorly predicted by standard models. Our results demonstrate that unsupervised domain adaptation is possible for applications in computational biology, even with many more features than samples. Availability and implementation Source code is available at https://github.com/PfeiferLabTue/wenda. Supplementary information Supplementary data are available at Bioinformatics online.

Recombinant vesicular stomatitis virus-Zaire Ebola virus (rVSV-ZEBOV) is the most advanced Ebola virus vaccine candidate and is currently being used to combat the outbreak of Ebola virus disease (EVD) in the Democratic Republic of the Congo (DRC). Here we examine the humoral immune response in a subset of human volunteers enrolled in a phase 1 rVSV-ZEBOV vaccination trial by performing comprehensive single B cell and electron microscopy structure analyses. Four studied vaccinees show polyclonal, yet reproducible and convergent B cell responses with shared sequence characteristics. EBOV-targeting antibodies cross-react with other Ebolavirus species, and detailed epitope mapping revealed overlapping target epitopes with antibodies isolated from EVD survivors. Moreover, in all vaccinees, we detected highly potent EBOV-neutralizing antibodies with activities comparable or superior to the monoclonal antibodies currently used in clinical trials. These include antibodies combining the IGHV3-15/IGLV1-40 immunoglobulin gene segments that were identified in all investigated individuals. Our findings will help to evaluate and direct current and future vaccination strategies and offer opportunities for novel EVD therapies.

Parsimonious context trees, PCTs, provide a sparse parameterization of conditional probability distributions. They are particularly powerful for modeling context-specific independencies in sequential discrete data. Learning PCTs from data is computationally hard due to the combinatorial explosion of the space of model structures as the number of predictor variables grows. Under the score-and-search paradigm, the fastest algorithm for finding an optimal PCT, prior to the present work, is based on dynamic programming. While the algorithm can handle small instances fast, it becomes infeasible already when there are half a dozen four-state predictor variables. Here, we show that common scoring functions enable the use of new algorithmic ideas, which can significantly expedite the dynamic programming algorithm on typical data. Specifically, we introduce a memoization technique, which exploits regularities within the predictor variables by equating different contexts associated with the same data subset, and a bound-and-prune technique, which exploits regularities within the response variable by pruning parts of the search space based on score upper bounds. On real-world data from recent applications of PCTs within computational biology the ideas are shown to reduce the traversed search space and the computation time by several orders of magnitude in typical cases.

The binding motifs of many transcription factors (TFs) comprise a higher degree of complexity than a single position weight matrix model permits. Additional complexity is typically taken into account either as intra-motif dependencies via more sophisticated probabilistic models or as heterogeneities via multiple weight matrices. However, both orthogonal approaches have limitations when learning from in vivo data where binding sites of other factors in close proximity can interfere with motif discovery for the protein of interest. In this work, we demonstrate how intra-motif complexity can, purely by analyzing the statistical properties of a given set of TF-binding sites, be distinguished from complexity arising from an intermix with motifs of co-binding TFs or other artifacts. In addition, we study the related question whether intra-motif complexity is represented more effectively by dependencies, heterogeneities or variants in between. Benchmarks demonstrate the effectiveness of both methods for their respective tasks and applications on motif discovery output from recent tools detect and correct many undesirable artifacts. These results further suggest that the prevalence of intra-motif dependencies may have been overestimated in previous studies onin vivo data and should thus be reassessed.

To compare learning algorithms that differ by the adopted statistical paradigm, model class, or search heuristic, it is common to evaluate the performance on training data of varying size. Measuring the performance is straightforward if the data are generated from a known model, the ground truth. However, when the study concerns real-world data, the current methodology is limited to estimating predictive performance, typically by cross-validation. This work introduces a method to compare algorithms’ ability to learn the model structure, assuming no ground truth is given. The idea is to identify a partial structure on which the algorithms agree, and measure the performance in relation to that structure on subsamples of the data. The method is instantiated to structure learning in Bayesian networks, measuring the performance by the structural Hamming distance. It is tested using benchmark ground truth networks and algorithms that maximize various scoring functions. The results show that the method can produce evaluation outcomes that are close to those one would obtain if the ground truth was available.

Alterations in gene regulation are considered major driving forces in divergent evolution. This is reflected in different species by the variable architecture of regulatory networks controlling highly conserved metabolic pathways. While many regulatory proteins are surprisingly conserved their wiring has evolved more rapidly. This project focuses on the adaptation to nutrient limitation, which requires the activation of the conserved AMP-activated protein kinase (AMPK alias Snf1 in yeast) and its downstream effectors. The goal is to uncover basic principles of adaptation and steps in the evolutionary process associated with regulatory network rearrangement. This requires improving the prediction of gene regulation based experimental data, DNA sequence information and information theory. In this project Context Tree (CT) models and Parsimonious Context Tree (PCT) models and the corresponding algorithms for extended Context Tree Maximization (CTM) and extended Parsimonious Context Tree Maximization (PCTM) are derived, implemented, and applied. Computational predictions and experimental validation will establish an iterative cycle to improve algorithms in each cycle leading to a growing set of experimentally verified and falsified predictions, finally allowing a deeper understanding of the evolution of the transcriptional regulatory network controlling energy metabolism, one of the most fundamental processes conserved across all kingdoms of life.