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Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022., Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1-4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models' predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models' forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models' past predictive performance., Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models' forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models' forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models' forecasts of deaths (N=763 predictions from 20 models). Across a 1-4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models., Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks., Funding: AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 (https://www.nfdi4health.de/task-force-covid-19-2) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z)., Competing Interests: KS, HG, RG, HJ, RN, BP, FS, JD, DW, SA, AU, GG, ER, NR, DS, YW, NW, LW, JT, GO, TS, DT, LP, EK, JM, MB, NL, JM, JS, JW, JF, BL, IR, JB, RW, IV, VT, MS, MZ, CP, BR, NB, SM, LC, GF, IM, DO, PA, AM, VE, AK, DK, LP, BD, ML, SS, JD, SM, VP, GR, BB, GA, NP, GZ, WB, SH, TH, DS, MG, JA, DM, SA, EA, DL, CP, JB, AR, TZ, AK, JZ, FP, FD, MC, GL, PG, BT, FB, GJ, MM, AF, AS, PM, AA, BH, BL, MM, PP, SV, RB, FD, AG, KG, MG, BK, AM, KN, JN, MR, FR, MS, ES, JZ, JK, BP, KH, YK, MS, BP, MB, MF, RI, TK, TO, JB, SF No competing interests declared, PB, HG, SS, BS Affiliated with IEM, Inc. The author has no financial interests to declare

Background: Through regulation of signaling pathways, microRNAs (miRNAs) can be involved in sepsis and associated organ dysfunction. The aims of this study were to track the 7-day time course of blood miRNAs in patients with sepsis treated with vancomycin, gentamicin, or a non-nephrotoxic antibiotic and miRNA associations with neutrophil gelatinase-associated lipokalin (NGAL), creatinine, procalcitonin, interleukin-6, and acute kidney injury (AKI) stage., Methods: Of 46 adult patients, 7 were on vancomycin, 20 on gentamicin, and 19 on another antibiotic. Blood samples were collected on days 1, 4, and 7 of treatment, and miRNAs were identified using quantitative reverse transcription PCR., Results: The results showed no relationship between miRNA levels and biochemical variables on day 1. By day 7 of gentamicin treatment miR-15a-5p provided good discrimination between AKI and non-AKI (area under curve, 0.828). In patients taking vancomycin, miR-155-5p and miR-192-5p positively correlated with creatinine and NGAL values, and miR-192-5p and miR-423-5p positively correlated with procalcitonin and interleukin-6 in patients treated with a non-nephrotoxic antibiotic. In patients together we found positive correlation between miR-155-5p and miR-423-5p and all biochemical markers., Conclusion: The results suggest that these four miRNAs may serve as diagnostic or therapeutic tool in sepsis, renal injury and nephrotoxic treatment., Trial Registration: ClinicalTrials.gov , ID: NCT04991376 . Registered on 27 July 2021., (© 2022. The Author(s).)

Our knowledge of yeast genomes remains largely dominated by the extensive studies on Saccharomyces cerevisiae and the consequences of its ancestral duplication, leaving the evolution of the entire class of hemiascomycetes only partly explored. We concentrate here on five species of Saccharomycetaceae, a large subdivision of hemiascomycetes, that we call "protoploid" because they diverged from the S. cerevisiae lineage prior to its genome duplication. We determined the complete genome sequences of three of these species: Kluyveromyces (Lachancea) thermotolerans and Saccharomyces (Lachancea) kluyveri (two members of the newly described Lachancea clade), and Zygosaccharomyces rouxii. We included in our comparisons the previously available sequences of Kluyveromyces lactis and Ashbya (Eremothecium) gossypii. Despite their broad evolutionary range and significant individual variations in each lineage, the five protoploid Saccharomycetaceae share a core repertoire of approximately 3300 protein families and a high degree of conserved synteny. Synteny blocks were used to define gene orthology and to infer ancestors. Far from representing minimal genomes without redundancy, the five protoploid yeasts contain numerous copies of paralogous genes, either dispersed or in tandem arrays, that, altogether, constitute a third of each genome. Ancient, conserved paralogs as well as novel, lineage-specific paralogs were identified.

Antiporters exporting Na(+) and K(+) in exchange for protons are conserved among yeast species. The only exception so far has been Zygosaccharomyces rouxii, an osmotolerant species closely related to Saccharomyces cerevisiae. Z. rouxii was described as possessing one plasma-membrane antiporter transporting only Na(+) (ZrSod2-22p in the CBS 732(T) type strain). We report the characterization of a second gene, ZrNHA1, encoding a new K(+)(Na(+))/H(+)-antiporter capable of both K(+) and Na(+) export. Synteny analyses suggested that ZrSOD2-22 originated by single duplication of the ZrNHA1 gene. Substrate specificities and transport properties of ZrNha1p and ZrSod2-22p were compared upon heterologous expression in S. cerevisiae, and then directly in Z. rouxii. Deletion mutants and phenotype analyses revealed that ZrSod2-22 antiporter is important for Na(+) detoxification, probably together with ZrEna1 ATPase; ZrNha1p is indispensable to maintain potassium homeostasis and ZrEna1p is not, in contrast to the situation in S. cerevisiae, involved in this function.

A set of tools for the genetic manipulation of the osmotolerant yeast Zygosaccharomyces rouxii was developed. Auxotrophic mutants (ura3 leu2, ura3 ade2, ura3 leu2 ade2) derived from the CBS 732 type strain were prepared. Centromeric and episomal Z. rouxii/Escherichia coli shuttle plasmids with different marker genes (ScURA3, ZrLEU2, ZrADE2) and with multiple cloning sites were constructed, together with a plasmid enabling green fluorescent protein-tagging. A system for repeatable targeted gene deletion in Z. rouxii was established, involving first the integration of a PCR-generated loxP-kanMX-loxP cassette and second the removal of kanMX from the genome using a Z. rouxii plasmid harbouring cre recombinase.

The yeast Zygosaccharomyces rouxii is known for its high tolerance to osmotic stress, which is thought to be caused by sets of specific genes. Relatively few Z. rouxii genes have been identified so far, all of them having homologues in Saccharomyces cerevisiae; none of them was Z. rouxii-specific. Most of the known Z. rouxii genes were isolated from two wild-type strains, ATCC 2623 and ATCC 42981. In this study, we compared these two strains with regard to some of their morphological, physiological and genomic properties. Important differences were found in their salt tolerance and assimilation of glycerol and karyotype; slight differences were also present in their cell morphology. The ATCC 42981 strain showed a higher resistance to salts, higher glycerol production and, unlike ATCC 2623, was able to assimilate glycerol. Under conditions of osmotic stress, the glycerol production in both Z. rouxii strains was much lower than in a S. cerevisiae S288c culture, which suggested the presence of a system that efficiently retains glycerol inside Z. rouxii cells. The karyotype analysis revealed that ATCC 42981 cells contain more chromosomes and have a bigger genome size than those of ATCC 2623.

Zygosaccharomyces rouxii is a hemiascomycetous yeast known for its high osmotolerance, the basis of which still remains unknown. By exploring the Génolevures I database, four Z. rouxii fragments homologous to Saccharomyces cerevisiae centromeres were identified. Two of them were subjected to further analysis. Their function as centromeres in Z. rouxii was proved, and they were localized to Z. rouxii chromosomes II and VII, respectively. The species-specificity of centromeres was observed; plasmids with a Z. rouxii centromere were not recognized as centromeric in S. cerevisiae, and a S. cerevisiae centromere did not function as a centromere in Z. rouxii. Constructed plasmids bearing Z. rouxii centromeres serve as the first specific centromeric plasmids, and thus contribute to the so-far limited set of genetic tools needed to study the Z. rouxii specific features.

A rapid and simple electroporation method to transform osmotolerant yeast Zygosaccharomyces rouxii has been developed and conditions for efficient transformation of mutants derived from different Z. rouxii wild-type strains optimized.