UNLABELLED: A comprehensive microbial surveillance was conducted at NASAs Mars 2020 spacecraft assembly facility (SAF), where whole-genome sequencing (WGS) of 110 bacterial strains was performed. One isolate, designated 179-BFC-A-HST, exhibited less than 80% average nucleotide identity (ANI) to known species, suggesting a novel organism. This strain demonstrated high-level resistance [minimum inhibitory concentration (MIC) >256 mg/L] to third-generation cephalosporins, including ceftazidime, cefpodoxime, combination ceftazidime/avibactam, and the fourth-generation cephalosporin cefepime. The results of a comparative genomic analysis revealed that 179-BFC-A-HST is most closely related to Virgibacillus halophilus 5B73CT, sharing an ANI of 78.7% and a digital DNA-DNA hybridization (dDDH) value of 23.5%, while their 16S rRNA gene sequences shared 97.7% nucleotide identity. Based on these results and the recent recognition that the genus Virgibacillus is polyphyletic, strain 179-BFC-A-HST is proposed as a novel species of a novel genus, Tigheibacillus jepli gen. nov., sp. nov (type strain 179-BFC-A-HST = DSM 115946T = NRRL B-65666T), and its closest neighbor, V. halophilus, is proposed to be reassigned to this genus as Tigheibacillus halophilus comb. nov. (type strain 5B73CT = DSM 21623T = JCM 21758T = KCTC 13935T). It was also necessary to reclassify its second closest neighbor Virgibacillus soli, as a member of a novel genus Paracerasibacillus, reflecting its phylogenetic position relative to the genus Cerasibacillus, for which we propose Paracerasibacillus soli comb. nov. (type strain CC-YMP-6T = DSM 22952T = CCM 7714T). Within Amphibacillaceae (n = 64), P. soli exhibited 11 antibiotic resistance genes (ARG), while T. jepli encoded for 3, lacking any known β-lactamases, suggesting resistance from variant penicillin-binding proteins, disrupting cephalosporin efficacy. P. soli was highly resistant to azithromycin (MIC >64 mg/L) yet susceptible to cephalosporins and penicillins. IMPORTANCE: The significance of this research extends to understanding microbial survival and adaptation in oligotrophic environments, such as those found in SAF. Whole-genome sequencing of several strains isolated from Mars 2020 mission assembly cleanroom facilities, including the discovery of the novel species Tigheibacillus jepli, highlights the resilience and antimicrobial resistance (AMR) in clinically relevant antibiotic classes of microbes in nutrient-scarce settings. The study also redefines the taxonomic classifications within the Amphibacillaceae family, aligning genetic identities with phylogenetic data. Investigating ARG and virulence factors (VF) across these strains illuminates the microbial capability for resistance under resource-limited conditions while emphasizing the role of human-associated VF in microbial survival, informing sterilization practices and microbial management in similar oligotrophic settings beyond spacecraft assembly cleanrooms such as pharmaceutical and medical industry cleanrooms.

Recent technological developments allow us to measure the status of dozens of proteins in individual cells. This opens the way to understand the heterogeneity of complex multi-signaling networks across cells and cell types, with important implications to understand and treat diseases such as cancer. These technologies are, however, limited to proteins for which antibodies are available and are fairly costly, making predictions of new markers and of existing markers under new conditions a valuable alternative. To assess our capacity to make such predictions and boost further methodological development, we organized the Single Cell Signaling in Breast Cancer DREAM challenge. We used a mass cytometry dataset, covering 36 markers in over 4,000 conditions totaling 80 million single cells across 67 breast cancer cell lines. Through four increasingly difficult subchallenges, the participants predicted missing markers, new conditions, and the time-course response of single cells to stimuli in the presence and absence of kinase inhibitors. The challenge results show that despite the stochastic nature of signal transduction in single cells, the signaling events are tightly controlled and machine learning methods can accurately predict new experimental data.

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.