Design Principles for Perfect Adaptation in Biological Networks with Nonlinear Dynamics.

Establishing a mapping between the emergent biological properties and the repository of network structures has been of great relevance in systems and synthetic biology. Adaptation is one such biological property of paramount importance that promotes regulation in the presence of environmental disturbances. This paper presents a nonlinear systems theory-driven framework to identify the design principles for perfect adaptation with respect to external disturbances of arbitrary magnitude. Based on the prior information about the network, we frame precise mathematical conditions for adaptation using nonlinear systems theory. We first deduce the mathematical conditions for perfect adaptation for constant input disturbances. Subsequently, we translate these conditions to specific necessary structural requirements for adaptation in networks of small size and then extend to argue that there exist only two classes of architectures for a network of any size that can provide local adaptation in the entire state space, namely, incoherent feed-forward (IFF) structure and negative feedback loop with buffer node (NFB). The additional positiveness constraints further narrow the admissible set of network structures. This also aids in establishing the global asymptotic stability for the steady state given a constant input disturbance. The proposed method does not assume any explicit knowledge of the underlying rate kinetics, barring some minimal assumptions. Finally, we also discuss the infeasibility of certain IFF networks in providing adaptation in the presence of downstream connections. Moreover, we propose a generic and novel algorithm based on non-linear systems theory to unravel the design principles for global adaptation. Detailed and extensive simulation studies corroborate the theoretical findings.

Discovering adaptation-capable biological network structures using control-theoretic approaches.

Constructing biological networks capable of performing specific biological functionalities has been of sustained interest in synthetic biology. Adaptation is one such ubiquitous functional property, which enables every living organism to sense a change in its surroundings and return to its operating condition prior to the disturbance. In this paper, we present a generic systems theory-driven method for designing adaptive protein networks. First, we translate the necessary qualitative conditions for adaptation to mathematical constraints using the language of systems theory, which we then map back as 'design requirements' for the underlying networks. We go on to prove that a protein network with different input-output nodes (proteins) needs to be at least of third-order in order to provide adaptation. Next, we show that the necessary design principles obtained for a three-node network in adaptation consist of negative feedback or a feed-forward realization. We argue that presence of a particular class of negative feedback or feed-forward realization is necessary for a network of any size to provide adaptation. Further, we claim that the necessary structural conditions derived in this work are the strictest among the ones hitherto existed in the literature. Finally, we prove that the capability of producing adaptation is retained for the admissible motifs even when the output node is connected with a downstream system in a feedback fashion. This result explains how complex biological networks achieve robustness while keeping the core motifs unchanged in the context of a particular functionality. We corroborate our theoretical results with detailed and thorough numerical simulations. Overall, our results present a generic, systematic and robust framework for designing various kinds of biological networks.

The art of molecular computing: Whence and whither.

An astonishingly diverse biomolecular circuitry orchestrates the functioning machinery underlying every living cell. These biomolecules and their circuits have been engineered not only for various industrial applications but also to perform other atypical functions that they were not evolved for-including computation. Various kinds of computational challenges, such as solving NP-complete problems with many variables, logical computation, neural network operations, and cryptography, have all been attempted through this unconventional computing paradigm. In this review, we highlight key experiments across three different ''eras'' of molecular computation, beginning with molecular solutions, transitioning to logic circuits and ultimately, more complex molecular networks. We also discuss a variety of applications of molecular computation, from solving NP-hard problems to self-assembled nanostructures for delivering molecules, and provide a glimpse into the exciting potential that molecular computing holds for the future. Also see the video abstract here: https://youtu.be/9Mw0K0vCSQw.

Bis-Olefin Based Crystalline Schlenk Hydrocarbon Diradicals with a Triplet Ground State.

A modular approach for the synthesis of isolable crystalline Schlenk hydrocarbon diradicals from m-phenylene bridged electron-rich bis-triazaalkenes as synthons is reported. EPR spectroscopy confirms their diradical nature and triplet electronic structure by revealing a half-field signal. A computational analysis confirms the triplet state to be the ground state. As a proof-of-principle for the modular methodology, the 4,6-dimethyl-m-phenylene was further utilized as a coupling unit between two alkene motifs. The steric conjunction of the 4,6-dimethyl groups substantially twists the substituents at the nonbonding electron bearing centers relative to the central coupling m-phenylene motif. As a result, the spin delocalization is decreased and the exchange coupling between the two unpaired spins, hence, significantly reduced. Notably, 108 years after Schlenk's m-phenylene-bis(diphenylmethyl) synthesis as a diradical, for the first time we were able to isolate its derivative with the same spacer, i.e. m-phenylene, between two radical centers in a crystalline form.

Mycobacterium tuberculosis (Mtb) lipid mediated lysosomal rewiring in infected macrophages modulates intracellular Mtb trafficking and survival.

Intracellular pathogens commonly manipulate the host lysosomal system for their survival. However, whether this pathogen-induced alteration affects the organization and functioning of the lysosomal system itself is not known. Here, using in vitro and in vivo infections and quantitative image analysis, we show that the lysosomal content and activity are globally elevated in Mycobacterium tuberculosis (Mtb)-infected macrophages. We observed that this enhanced lysosomal state is sustained over time and defines an adaptive homeostasis in the infected macrophage. Lysosomal alterations are caused by mycobacterial surface components, notably the cell wall-associated lipid sulfolipid-1 (SL-1), which functions through the mTOR complex 1 (mTORC1)-transcription factor EB (TFEB) axis in the host cells. An Mtb mutant lacking SL-1, MtbΔpks2, shows attenuated lysosomal rewiring compared with the WT Mtb in both in vitro and in vivo infections. Exposing macrophages to purified SL-1 enhanced the trafficking of phagocytic cargo to lysosomes. Correspondingly, MtbΔpks2 exhibited a further reduction in lysosomal delivery compared with the WT. Reduced trafficking of this mutant Mtb strain to lysosomes correlated with enhanced intracellular bacterial survival. Our results reveal that global alteration of the host lysosomal system is a defining feature of Mtb-infected macrophages and suggest that this altered lysosomal state protects host cell integrity and contributes to the containment of the pathogen.

MinReact: a systematic approach for identifying minimal metabolic networks.

MOTIVATION: Genome-scale metabolic models are widely constructed and studied for understanding various design principles underlying metabolism, predominantly redundancy. Metabolic networks are highly redundant and it is possible to minimize the metabolic networks into smaller networks that retain the functionality of the original network. RESULTS: Here, we establish a new method, MinReact that systematically removes reactions from a given network to identify minimal reactome(s). We show that our method identifies smaller minimal reactomes than existing methods and also scales well to larger metabolic networks. Notably, our method exploits known aspects of network structure and redundancy to identify multiple minimal metabolic networks. We illustrate the utility of MinReact by identifying multiple minimal networks for 77 organisms from the BiGG database. We show that these multiple minimal reactomes arise due to the presence of compensatory reactions/pathways. We further employed MinReact for a case study to identify the minimal reactomes of different organisms in both glucose and xylose minimal environments. Identification of minimal reactomes of these different organisms elucidate that they exhibit varying levels of redundancy. A comparison of the minimal reactomes on glucose and xylose illustrates that the differences in the reactions required to sustain growth on either medium. Overall, our algorithm provides a rapid and reliable way to identify minimal subsets of reactions that are essential for survival, in a systematic manner. AVAILABILITY AND IMPLEMENTATION: Algorithm is available from https://github.com/RamanLab/MinReact. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SBML Level 3: an extensible format for the exchange and reuse of biological models.

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.

Evolutionary design principles in metabolism.

Microorganisms are ubiquitous and adapt to various dynamic environments to sustain growth. These adaptations accumulate, generating new traits forming the basis of evolution. Organisms adapt at various levels, such as gene regulation, signalling, protein-protein interactions and metabolism. Of these, metabolism forms the integral core of an organism for maintaining the growth and function of a cell. Therefore, studying adaptations in metabolic networks is crucial to understand the emergence of novel metabolic capabilities. Metabolic networks, composed of enzyme-catalysed reactions, exhibit certain repeating paradigms or design principles that arise out of different selection pressures. In this review, we discuss the design principles that are known to exist in metabolic networks, such as functional redundancy, modularity, flux coupling and exaptations. We elaborate on the studies that have helped gain insights highlighting the interplay of these design principles and adaptation. Further, we discuss how evolution plays a role in exploiting such paradigms to enhance the robustness of organisms. Looking forward, we predict that with the availability of ever-increasing numbers of bacterial, archaeal and eukaryotic genomic sequences, novel design principles will be identified, expanding our understanding of these paradigms shaped by varied evolutionary processes.

Understanding the evolution of functional redundancy in metabolic networks.

Motivation: Metabolic networks have evolved to reduce the disruption of key metabolic pathways by the establishment of redundant genes/reactions. Synthetic lethals in metabolic networks provide a window to study these functional redundancies. While synthetic lethals have been previously studied in different organisms, there has been no study on how the synthetic lethals are shaped during adaptation/evolution. Results: To understand the adaptive functional redundancies that exist in metabolic networks, we here explore a vast space of 'random' metabolic networks evolved on a glucose environment. We examine essential and synthetic lethal reactions in these random metabolic networks, evaluating over 39 billion phenotypes using an efficient algorithm previously developed in our lab, Fast-SL. We establish that nature tends to harbour higher levels of functional redundancies compared with random networks. We then examined the propensity for different reactions to compensate for one another and show that certain key metabolic reactions that are necessary for growth in a particular growth medium show much higher redundancies, and can partner with hundreds of different reactions across the metabolic networks that we studied. We also observe that certain redundancies are unique to environments while some others are observed in all environments. Interestingly, we observe that even very diverse reactions, such as those belonging to distant pathways, show synthetic lethality, illustrating the distributed nature of robustness in metabolism. Our study paves the way for understanding the evolution of redundancy in metabolic networks, and sheds light on the varied compensation mechanisms that serve to enhance robustness. Supplementary information: Supplementary data are available at Bioinformatics online.

Interface-engineered templates for molecular spin memory devices.

The use of molecular spin state as a quantum of information for storage, sensing and computing has generated considerable interest in the context of next-generation data storage and communication devices, opening avenues for developing multifunctional molecular spintronics. Such ideas have been researched extensively, using single-molecule magnets and molecules with a metal ion or nitrogen vacancy as localized spin-carrying centres for storage and for realizing logic operations. However, the electronic coupling between the spin centres of these molecules is rather weak, which makes construction of quantum memory registers a challenging task. In this regard, delocalized carbon-based radical species with unpaired spin, such as phenalenyl, have shown promise. These phenalenyl moieties, which can be regarded as graphene fragments, are formed by the fusion of three benzene rings and belong to the class of open-shell systems. The spin structure of these molecules responds to external stimuli (such as light, and electric and magnetic fields), which provides novel schemes for performing spin memory and logic operations. Here we construct a molecular device using such molecules as templates to engineer interfacial spin transfer resulting from hybridization and magnetic exchange interaction with the surface of a ferromagnet; the device shows an unexpected interfacial magnetoresistance of more than 20 per cent near room temperature. Moreover, we successfully demonstrate the formation of a nanoscale magnetic molecule with a well-defined magnetic hysteresis on ferromagnetic surfaces. Owing to strong magnetic coupling with the ferromagnet, such independent switching of an adsorbed magnetic molecule has been unsuccessful with single-molecule magnets. Our findings suggest the use of chemically amenable phenalenyl-based molecules as a viable and scalable platform for building molecular-scale quantum spin memory and processors for technological development.

Predicting novel metabolic pathways through subgraph mining.

MOTIVATION: The ability to predict pathways for biosynthesis of metabolites is very important in metabolic engineering. It is possible to mine the repertoire of biochemical transformations from reaction databases, and apply the knowledge to predict reactions to synthesize new molecules. However, this usually involves a careful understanding of the mechanism and the knowledge of the exact bonds being created and broken. There is a need for a method to rapidly predict reactions for synthesizing new molecules, which relies only on the structures of the molecules, without demanding additional information such as thermodynamics or hand-curated reactant mapping, which are often hard to obtain accurately. RESULTS: We here describe a robust method based on subgraph mining, to predict a series of biochemical transformations, which can convert between two (even previously unseen) molecules. We first describe a reliable method based on subgraph edit distance to map reactants and products, using only their chemical structures. Having mapped reactants and products, we identify the reaction centre and its neighbourhood, the reaction signature, and store this in a reaction rule network. This novel representation enables us to rapidly predict pathways, even between previously unseen molecules. We demonstrate this ability by predicting pathways to molecules not present in the KEGG database. We also propose a heuristic that predominantly recovers natural biosynthetic pathways from amongst hundreds of possible alternatives, through a directed search of the reaction rule network, enabling us to provide a reliable ranking of the different pathways. Our approach scales well, even to databases with >100 000 reactions. AVAILABILITY AND IMPLEMENTATION: A Java-based implementation of our algorithms is available at https://github.com/RamanLab/ReactionMiner. CONTACT: sayanranu@cse.iitd.ac.in or kraman@iitm.ac.in. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A General Mechanism for the Propagation of Mutational Effects in Proteins.

Mutations in the hydrophobic interior of proteins are generally thought to weaken the interactions only in their immediate neighborhood. This forms the basis of protein engineering-based studies of folding mechanism and function. However, mutational work on diverse proteins has shown that distant residues are thermodynamically coupled, with the network of interactions within the protein acting as signal conduits, thus raising an intriguing paradox. Are mutational effects localized, and if not, is there a general rule for the extent of percolation and the functional form of this propagation? We explore these questions from multiple perspectives in this work. Perturbation analysis of interaction networks within proteins and microsecond long molecular dynamics simulations of several aliphatic mutants of ubiquitin reveal strong evidence of the distinct alteration of distal residue-residue communication networks. We find that mutational effects consistently propagate into the second shell of the altered site (even up to 15-20 Å) in proportion to the perturbation magnitude and dissipate exponentially with a decay distance constant of ∼4-5 Å. We also report evidence for this phenomenon from published experimental nuclear magnetic resonance data that strikingly resemble predictions from network theory and molecular dynamics simulations. Reformulating these observations onto a statistical mechanical model, we reproduce the stability changes of 375 mutations from 19 single-domain proteins. Our work thus reveals a robust energy dissipation-cum-signaling mechanism in the interaction network within proteins, quantifies the partitioning of destabilization energetics around the mutation neighborhood, and presents a simple theoretical framework for modeling the allosteric effects of point mutations.

Critical assessment of genome-scale metabolic networks: the need for a unified standard.

Genome-scale metabolic networks have been reconstructed for several organisms. These metabolic networks provide detailed information about the metabolism inside the cells, coupled with the genomic, proteomic and thermodynamic information. These networks are widely simulated using 'constraint-based' modelling techniques and find applications ranging from strain improvement for metabolic engineering to prediction of drug targets in pathogenic organisms. Components of these metabolic networks are represented in multiple file formats and also using different markup languages, with varying levels of annotations; this leads to inconsistencies and increases the complexities in comparing and analysing reconstructions on multiple platforms. In this work, we critically examine nearly 100 published genome-scale metabolic networks and their corresponding constraint-based models and discuss various issues with respect to model quality. One of the major concerns is the lack of annotations using standard identifiers that can uniquely describe several components such as metabolites, genes, proteins and reactions. We also find that many models do not have complete information regarding constraints on reactions fluxes and objective functions for carrying out simulations. Overall, our analysis highlights the need for a widely acceptable standard for representing constraint-based models. A rigorous standard can help in streamlining the process of reconstruction and improve the quality of reconstructed metabolic models.

Fast-SL: an efficient algorithm to identify synthetic lethal sets in metabolic networks.

MOTIVATION: Synthetic lethal sets are sets of reactions/genes where only the simultaneous removal of all reactions/genes in the set abolishes growth of an organism. Previous approaches to identify synthetic lethal genes in genome-scale metabolic networks have built on the framework of flux balance analysis (FBA), extending it either to exhaustively analyze all possible combinations of genes or formulate the problem as a bi-level mixed integer linear programming (MILP) problem. We here propose an algorithm, Fast-SL, which surmounts the computational complexity of previous approaches by iteratively reducing the search space for synthetic lethals, resulting in a substantial reduction in running time, even for higher order synthetic lethals. RESULTS: We performed synthetic reaction and gene lethality analysis, using Fast-SL, for genome-scale metabolic networks of Escherichia coli, Salmonella enterica Typhimurium and Mycobacterium tuberculosis. Fast-SL also rigorously identifies synthetic lethal gene deletions, uncovering synthetic lethal triplets that were not reported previously. We confirm that the triple lethal gene sets obtained for the three organisms have a precise match with the results obtained through exhaustive enumeration of lethals performed on a computer cluster. We also parallelized our algorithm, enabling the identification of synthetic lethal gene quadruplets for all three organisms in under 6 h. Overall, Fast-SL enables an efficient enumeration of higher order synthetic lethals in metabolic networks, which may help uncover previously unknown genetic interactions and combinatorial drug targets. AVAILABILITY AND IMPLEMENTATION: The MATLAB implementation of the algorithm, compatible with COBRA toolbox v2.0, is available at https://github.com/RamanLab/FastSL CONTACT: kraman@iitm.ac.in SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A nanosensor for ultrasensitive detection of oversulfated chondroitin sulfate contaminant in heparin.

Heparin has been extensively used as an anticoagulant for the last eight decades. Recently, the administration of a contaminated batch of heparin caused 149 deaths in several countries including USA, Germany, and Japan. The contaminant responsible for the adverse effects was identified as oversulfated chondroitin sulfate (OSCS). Here, we report a rapid, ultrasensitive method of detecting OSCS in heparin using a nanometal surface energy transfer (NSET) based gold-heparin-dye nanosensor. The sensor is an excellent substrate for heparitinase enzyme, as evidenced by ~70% recovery of fluorescence from the dye upon heparitinase treatment. However, the presence of OSCS results in diminished fluorescence recovery from the nanosensor upon heparitinase treatment, as the enzyme is inhibited by the contaminant. The newly designed nanosensor can detect as low as 1 × 10(-9) % (w/w) OSCS making it the most sensitive tool to date for the detection of trace amounts of OSCS in pharmaceutical heparins.

Designing function-specific minimal microbiomes from large microbial communities.

Microorganisms exist in large communities of diverse species, exhibiting various functionalities. The mammalian gut microbiome, for instance, has the functionality of digesting dietary fibre and producing different short-chain fatty acids. Not all microbes present in a community contribute to a given functionality; it is possible to find a minimal microbiome, which is a subset of the large microbiome, that is capable of performing the functionality while maintaining other community properties such as growth rate and metabolite production. Such a minimal microbiome will also contain keystone species for SCFA production in that community. In this work, we present a systematic constraint-based approach to identify a minimal microbiome from a large community for a user-proposed function. We employ a top-down approach with sequential deletion followed by solving a mixed-integer linear programming problem with the objective of minimising the L1-norm of the membership vector. Notably, we consider quantitative measures of community growth rate and metabolite production rates. We demonstrate the utility of our algorithm by identifying the minimal microbiomes corresponding to three model communities of the gut, and discuss their validity based on the presence of the keystone species in the community. Our approach is generic, flexible and finds application in studying a variety of microbial communities. The algorithm is available from https://github.com/RamanLab/minMicrobiome .

Design Principles for Biological Adaptation: A Systems and Control-Theoretic Treatment.

Establishing a mapping between (from and to) the functionality of interest and the underlying network structure (design principles) remains a crucial step toward understanding and design of bio-systems. Perfect adaptation is one such crucial functionality that enables every living organism to regulate its essential activities in the presence of external disturbances. Previous approaches to deducing the design principles for adaptation have either relied on computationally burdensome brute-force methods or rule-based design strategies detecting only a subset of all possible adaptive network structures. This chapter outlines a scalable and generalizable method inspired by systems theory that unravels an exhaustive set of adaptation-capable structures. We first use the well-known performance parameters to characterize perfect adaptation. These performance parameters are then mapped back to a few parameters (poles, zeros, gain) characteristic of the underlying dynamical system constituted by the rate equations. Therefore, the performance parameters evaluated for the scenario of perfect adaptation can be expressed as a set of precise mathematical conditions involving the system parameters. Finally, we use algebraic graph theory to translate these abstract mathematical conditions to certain structural requirements for adaptation. The proposed algorithm does not assume any particular dynamics and is applicable to networks of any size. Moreover, the results offer a significant advancement in the realm of understanding and designing complex biochemical networks.

Genomic, functional, and metabolic enhancements in multidrug-resistant Enterobacter bugandensis facilitating its persistence and succession in the International Space Station.

BACKGROUND: The International Space Station (ISS) stands as a testament to human achievement in space exploration. Despite its highly controlled environment, characterised by microgravity, increased CO 2 levels, and elevated solar radiation, microorganisms occupy a unique niche. These microbial inhabitants play a significant role in influencing the health and well-being of astronauts on board. One microorganism of particular interest in our study is Enterobacter bugandensis, primarily found in clinical specimens including the human gastrointestinal tract, and also reported to possess pathogenic traits, leading to a plethora of infections. RESULTS: Distinct from their Earth counterparts, ISS E. bugandensis strains have exhibited resistance mechanisms that categorise them within the ESKAPE pathogen group, a collection of pathogens recognised for their formidable resistance to antimicrobial treatments. During the 2-year Microbial Tracking 1 mission, 13 strains of multidrug-resistant E. bugandensis were isolated from various locations within the ISS. We have carried out a comprehensive study to understand the genomic intricacies of ISS-derived E. bugandensis in comparison to terrestrial strains, with a keen focus on those associated with clinical infections. We unravel the evolutionary trajectories of pivotal genes, especially those contributing to functional adaptations and potential antimicrobial resistance. A hypothesis central to our study was that the singular nature of the stresses of the space environment, distinct from any on Earth, could be driving these genomic adaptations. Extending our investigation, we meticulously mapped the prevalence and distribution of E. bugandensis across the ISS over time. This temporal analysis provided insights into the persistence, succession, and potential patterns of colonisation of E. bugandensis in space. Furthermore, by leveraging advanced analytical techniques, including metabolic modelling, we delved into the coexisting microbial communities alongside E. bugandensis in the ISS across multiple missions and spatial locations. This exploration revealed intricate microbial interactions, offering a window into the microbial ecosystem dynamics within the ISS. CONCLUSIONS: Our comprehensive analysis illuminated not only the ways these interactions sculpt microbial diversity but also the factors that might contribute to the potential dominance and succession of E. bugandensis within the ISS environment. The implications of these findings are twofold. Firstly, they shed light on microbial behaviour, adaptation, and evolution in extreme, isolated environments. Secondly, they underscore the need for robust preventive measures, ensuring the health and safety of astronauts by mitigating risks associated with potential pathogenic threats. Video Abstract.

Novel spore-forming species exhibiting intrinsic resistance to third- and fourth-generation cephalosporins and description of Tigheibacillus jepli gen. nov., sp. nov.

A comprehensive microbial surveillance was conducted at NASA's 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 ß-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.