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.

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.

Phylogenomics, phenotypic, and functional traits of five novel (Earth-derived) bacterial species isolated from the International Space Station and their prevalence in metagenomes.

With the advent of long-term human habitation in space and on the moon, understanding how the built environment microbiome of space habitats differs from Earth habitats, and how microbes survive, proliferate and spread in space conditions, is becoming more important. The microbial tracking mission series has been monitoring the microbiome of the International Space Station (ISS) for almost a decade. During this mission series, six unique strains of Gram-stain-positive bacteria, including two spore-forming and three non-spore-forming species, were isolated from the environmental surfaces of the ISS. The analysis of their 16S rRNA gene sequences revealed > 99% similarities with previously described bacterial species. To further explore their phylogenetic affiliation, whole genome sequencing was undertaken. For all strains, the gyrB gene exhibited < 93% similarity with closely related species, which proved effective in categorizing these ISS strains as novel species. Average nucleotide identity and digital DNA-DNA hybridization values, when compared to any known bacterial species, were < 94% and <50% respectively for all species described here. Traditional biochemical tests, fatty acid profiling, polar lipid, and cell wall composition analyses were performed to generate phenotypic characterization of these ISS strains. A study of the shotgun metagenomic reads from the ISS samples, from which the novel species were isolated, showed that only 0.1% of the total reads mapped to the novel species, supporting the idea that these novel species are rare in the ISS environments. In-depth annotation of the genomes unveiled a variety of genes linked to amino acid and derivative synthesis, carbohydrate metabolism, cofactors, vitamins, prosthetic groups, pigments, and protein metabolism. Further analysis of these ISS-isolated organisms revealed that, on average, they contain 46 genes associated with virulence, disease, and defense. The main predicted functions of these genes are: conferring resistance to antibiotics and toxic compounds, and enabling invasion and intracellular resistance. After conducting antiSMASH analysis, it was found that there are roughly 16 cluster types across the six strains, including ß-lactone and type III polyketide synthase (T3PKS) clusters. Based on these multi-faceted taxonomic methods, it was concluded that these six ISS strains represent five novel species, which we propose to name as follows: Arthrobacter burdickii IIF3SC-B10T (= NRRL B-65660T = DSM 115933T), Leifsonia virtsii F6_8S_P_1AT (= NRRL B-65661T = DSM 115931T), Leifsonia williamsii F6_8S_P_1BT (= NRRL B-65662T = DSM 115932T), Paenibacillus vandeheii F6_3S_P_1CT (= NRRL B-65663T = DSM 115940T), and Sporosarcina highlanderae F6_3S_P_2T (= NRRL B-65664T = DSM 115943T). Identifying and characterizing the genomes and phenotypes of novel microbes found in space habitats, like those explored in this study, is integral for expanding our genomic databases of space-relevant microbes. This approach offers the only reliable method to determine species composition, track microbial dispersion, and anticipate potential threats to human health from monitoring microbes on the surfaces and equipment within space habitats. By unraveling these microbial mysteries, we take a crucial step towards ensuring the safety and success of future space missions.

BugSigDB captures patterns of differential abundance across a broad range of host-associated microbial signatures.

The literature of human and other host-associated microbiome studies is expanding rapidly, but systematic comparisons among published results of host-associated microbiome signatures of differential abundance remain difficult. We present BugSigDB, a community-editable database of manually curated microbial signatures from published differential abundance studies accompanied by information on study geography, health outcomes, host body site and experimental, epidemiological and statistical methods using controlled vocabulary. The initial release of the database contains >2,500 manually curated signatures from >600 published studies on three host species, enabling high-throughput analysis of signature similarity, taxon enrichment, co-occurrence and coexclusion and consensus signatures. These data allow assessment of microbiome differential abundance within and across experimental conditions, environments or body sites. Database-wide analysis reveals experimental conditions with the highest level of consistency in signatures reported by independent studies and identifies commonalities among disease-associated signatures, including frequent introgression of oral pathobionts into the gut.

Genome-scale metabolic model led engineering of Nothapodytes nimmoniana plant cells for high camptothecin production.

Camptothecin (CPT) is a vital monoterpene indole alkaloid used in anti-cancer therapeutics. It is primarily derived from Camptotheca acuminata and Nothapodytes nimmoniana plants that are indigenous to Southeast Asia. Plants have intricate metabolic networks and use them to produce secondary metabolites such as CPT, which is a prerequisite for rational metabolic engineering design to optimize their production. By reconstructing metabolic models, we can predict plant metabolic behavior, facilitating the selection of suitable approaches and saving time, cost, and energy, over traditional hit and trial experimental approaches. In this study, we reconstructed a genome-scale metabolic model for N. nimmoniana (NothaGEM iSM1809) and curated it using experimentally obtained biochemical data. We also used in silico tools to identify and rank suitable enzyme targets for overexpression and knockout to maximize camptothecin production. The predicted over-expression targets encompass enzymes involved in the camptothecin biosynthesis pathway, including strictosidine synthase and geraniol 10-hydroxylase, as well as targets related to plant metabolism, such as amino acid biosynthesis and the tricarboxylic acid cycle. The top-ranked knockout targets included reactions responsible for the formation of folates and serine, as well as the conversion of acetyl CoA and oxaloacetate to malate and citrate. One of the top-ranked overexpression targets, strictosidine synthase, was chosen to generate metabolically engineered cell lines of N. nimmoniana using Agrobacterium tumefaciens-mediated transformation. The transformed cell line showed a 5-fold increase in camptothecin production, with a yield of up to 5 µg g-1.

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.

Phylogenetic affiliations and genomic characterization of novel bacterial species and their abundance in the International Space Station.

Background: With the advent of long-term human habitation in space and on the moon, understanding how the built environment microbiome of space habitats differs from Earth habits, and how microbes survive, proliferate and spread in space conditions, is coming more and more important. The Microbial Tracking mission series has been monitoring the microbiome of the International Space Station (ISS) for almost a decade. During this mission series, six unique strains of Gram-positive bacteria, including two spore-forming and three non-spore-forming species, were isolated from the environmental surfaces of the International Space Station (ISS). Results: The analysis of their 16S rRNA gene sequences revealed <99% similarities with previously described bacterial species. To further explore their phylogenetic affiliation, whole genome sequencing (WGS) was undertaken. For all strains, the gyrB gene exhibited <93% similarity with closely related species, which proved effective in categorizing these ISS strains as novel species. Average ucleotide identity (ANI) and digital DNA-DNA hybridization (dDDH) values, when compared to any known bacterial species, were less than <94% and 50% respectively for all species described here. Traditional biochemical tests, fatty acid profiling, polar lipid, and cell wall composition analyses were performed to generate phenotypic characterization of these ISS strains. A study of the shotgun metagenomic reads from the ISS samples, from which the novel species were isolated, showed that only 0.1% of the total reads mapped to the novel species, supporting the idea that these novel species are rare in the ISS environments. In-depth annotation of the genomes unveiled a variety of genes linked to amino acid and derivative synthesis, carbohydrate metabolism, cofactors, vitamins, prosthetic groups, pigments, and protein metabolism. Further analysis of these ISS-isolated organisms revealed that, on average, they contain 46 genes associated with virulence, disease, and defense. The main predicted functions of these genes are: conferring resistance to antibiotics and toxic compounds, and enabling invasion and intracellular resistance. After conducting antiSMASH analysis, it was found that there are roughly 16 cluster types across the six strains, including ß-lactone and type III polyketide synthase (T3PKS) clusters. Conclusions: Based on these multi-faceted taxonomic methods, it was concluded that these six ISS strains represent five novel species, which we propose to name as follows: Arthrobacter burdickii IIF3SC-B10T (=NRRL B-65660T), Leifsonia virtsii, F6_8S_P_1AT (=NRRL B-65661T), Leifsonia williamsii, F6_8S_P_1BT (=NRRL B- 65662T and DSMZ 115932T), Paenibacillus vandeheii, F6_3S_P_1CT(=NRRL B-65663T and DSMZ 115940T), and Sporosarcina highlanderae F6_3S_P_2 T(=NRRL B-65664T and DSMZ 115943T). Identifying and characterizing the genomes and phenotypes of novel microbes found in space habitats, like those explored in this study, is integral for expanding our genomic databases of space-relevant microbes. This approach offers the only reliable method to determine species composition, track microbial dispersion, and anticipate potential threats to human health from monitoring microbes on the surfaces and equipment within space habitats. By unraveling these microbial mysteries, we take a crucial step towards ensuring the safety and success of future space missions.

COWAVE: A labelled COVID-19 wave dataset for building predictive models.

The ongoing COVID-19 pandemic has posed a significant global challenge to healthcare systems. Every country has seen multiple waves of this disease, placing a considerable strain on healthcare resources. Across the world, the pandemic has motivated diligent data collection, with an enormous amount of data being available in the public domain. In this manuscript, we collate COVID-19 case data from around the world (available on the World Health Organization (WHO) website), and provide various definitions for waves. Using these definitions to define labels, we create a labelled dataset, which can be used while building supervised learning classifiers. We also use a simple eXtreme Gradient Boosting (XGBoost) model to provide a minimum standard for future classifiers trained on this dataset and demonstrate the utility of our dataset for the prediction of (future) waves. This dataset will be a valuable resource for epidemiologists and others interested in the early prediction of future waves. The datasets are available from https://github.com/RamanLab/COWAVE/.

Big Data for a Small World: A Review on Databases and Resources for Studying Microbiomes.

Microorganisms are ubiquitous in nature and form complex community networks to survive in various environments. This community structure depends on numerous factors like nutrient availability, abiotic factors like temperature and pH as well as microbial composition. Categorising accessible biomes according to their habitats would help in understanding the complexity of the environment-specific communities. Owing to the recent improvements in sequencing facilities, researchers have started to explore diverse microbiomes rapidly and attempts have been made to study microbial crosstalk. However, different metagenomics sampling, preprocessing, and annotation methods make it difficult to compare multiple studies and hinder the recycling of data. Huge datasets originating from these experiments demand systematic computational methods to extract biological information beyond microbial compositions. Further exploration of microbial co-occurring patterns across the biomes could help us in designing cross-biome experiments. In this review, we catalogue databases with system-specific microbiomes, discussing publicly available common databases as well as specialised databases for a range of microbiomes. If the new datasets generated in the future could maintain at least biome-specific annotation, then researchers could use those contemporary tools for relevant and bias-free analysis of complex metagenomics data.

Metagenome-based metabolic modelling predicts unique microbial interactions in deep-sea hydrothermal plume microbiomes.

Deep-sea hydrothermal vents are abundant on the ocean floor and play important roles in ocean biogeochemistry. In vent ecosystems such as hydrothermal plumes, microorganisms rely on reduced chemicals and gases in hydrothermal fluids to fuel primary production and form diverse and complex microbial communities. However, microbial interactions that drive these complex microbiomes remain poorly understood. Here, we use microbiomes from the Guaymas Basin hydrothermal system in the Pacific Ocean to shed more light on the key species in these communities and their interactions. We built metabolic models from metagenomically assembled genomes (MAGs) and infer possible metabolic exchanges and horizontal gene transfer (HGT) events within the community. We highlight possible archaea-archaea and archaea-bacteria interactions and their contributions to the robustness of the community. Cellobiose, D-Mannose 1-phosphate, O2, CO2, and H2S were among the most exchanged metabolites. These interactions enhanced the metabolic capabilities of the community by exchange of metabolites that cannot be produced by any other community member. Archaea from the DPANN group stood out as key microbes, benefiting significantly as acceptors in the community. Overall, our study provides key insights into the microbial interactions that drive community structure and organisation in complex hydrothermal plume microbiomes.

Sloppiness: Fundamental study, new formalism and its application in model assessment.

Computational modelling of biological processes poses multiple challenges in each stage of the modelling exercise. Some significant challenges include identifiability, precisely estimating parameters from limited data, informative experiments and anisotropic sensitivity in the parameter space. One of these challenges' crucial but inconspicuous sources is the possible presence of large regions in the parameter space over which model predictions are nearly identical. This property, known as sloppiness, has been reasonably well-addressed in the past decade, studying its possible impacts and remedies. However, certain critical unanswered questions concerning sloppiness, particularly related to its quantification and practical implications in various stages of system identification, still prevail. In this work, we systematically examine sloppiness at a fundamental level and formalise two new theoretical definitions of sloppiness. Using the proposed definitions, we establish a mathematical relationship between the parameter estimates' precision and sloppiness in linear predictors. Further, we develop a novel computational method and a visual tool to assess the goodness of a model around a point in parameter space by identifying local structural identifiability and sloppiness and finding the most sensitive and least sensitive parameters for non-infinitesimal perturbations. We demonstrate the working of our method in benchmark systems biology models of various complexities. The pharmacokinetic HIV infection model analysis identified a new set of biologically relevant parameters that can be used to control the free virus in an active HIV infection.

On biological networks capable of robust adaptation in the presence of uncertainties: A linear systems-theoretic approach.

Biological adaptation, the tendency of every living organism to regulate its essential activities in environmental fluctuations, is a well-studied functionality in systems and synthetic biology. In this work, we present a generic methodology inspired by systems theory to discover the design principles for robust adaptation, perfect and imperfect, in two different contexts: (1) in the presence of deterministic external and parametric disturbances and (2) in a stochastic setting. In all the cases, firstly, 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. Thus, contrary to the existing approaches, the proposed methodologies provide an exhaustive set of admissible network structures without resorting to computationally burdensome brute-force techniques. Further, the proposed frameworks do not assume prior knowledge about the particular rate kinetics, thereby validating the conclusions for a large class of biological networks. In the deterministic setting, we show that unlike the incoherent feed-forward network structures (IFFLP or opposer modules), the modules containing negative feedback with buffer action (NFBLB or balancer modules) are robust to parametric fluctuations when a specific part of the network is assumed to remain unaffected. To this end, we propose a sufficient condition for imperfect adaptation and show that adding negative feedback in an IFFLP topology improves the robustness concerning parametric fluctuations. Further, we propose a stricter set of necessary conditions for imperfect adaptation. Turning to the stochastic scenario, we adopt a Wiener-Kolmogorov filter strategy to tune the parameters of a given network structure towards minimum output variance. We show that both NFBLB and IFFLP can be used as a reduced-order W-K filter. Further, we define the notion of nearest neighboring motifs to compare the output variances across different network structures. We argue that the NFBLB achieves adaptation at the cost of a variance higher than its nearest neighboring motifs whereas the IFFLP topology produces locally minimum variance while compared with its nearest neighboring motifs. We present numerical simulations to support the theoretical results. Overall, our results present a generic, systematic, and robust framework for advancing the understanding of complex biological networks.

Probing patterning in microbial consortia with a cellular automaton for spatial organisation.

Microbial consortia exhibit spatial patterning across diverse environments. Since probing the self-organization of natural microbial communities is limited by their inherent complexity, synthetic models have emerged as attractive alternatives. In this study, we develop novel frameworks of bacterial communication and explore the emergent spatiotemporal organization of microbes. Specifically, we built quorum sensing-mediated models of microbial growth that are utilized to characterize the dynamics of communities from arbitrary initial configurations and establish the effectiveness of our communication strategies in coupling the growth rates of microbes. Our simulations indicate that the behavior of quorum sensing-coupled consortia can be most effectively modulated by the rates of secretion of acyl homoserine lactones. Such a mechanism of control enables the construction of desired relative populations of constituent species in spatially organized populations. Our models accurately recapitulate previous experiments that have investigated pattern formation in synthetic multi-cellular systems. Additionally, our software tool enables the easy implementation and analysis of our frameworks for a variety of initial configurations and simplifies the development of sophisticated gene circuits facilitating distributed computing. Overall, we demonstrate the potential of spatial organization as a tunable parameter in synthetic biology by introducing a communication paradigm based on the location and strength of coupling of microbial strains.

Discovering design principles for biological functionalities:Perspectives from systems biology.

Network architecture plays a crucial role in governing the dynamics of any biological network. Further, network structures have been shown to remain conserved across organisms for a given phenotype. Therefore, the mapping between network structures and the output functionality not only aids in understanding of biological systems but also finds application in synthetic biology and therapeutics. Based on the approaches involved, most of the efforts hitherto invested in this field can be classified into three broad categories, namely, computational efforts, rule-based methods and systems-theoretic approaches. The present review provides a qualitative and quantitative study of all three approaches in the light of three well-researched biological phenotypes, namely, oscillation, toggle switching, and adaptation. We also discuss the advantages, limitations, and future research scope for all three approaches along with their possible applications to other emergent properties of biological relevance.

iCOMIC: a graphical interface-driven bioinformatics pipeline for analyzing cancer omics data.

Despite the tremendous increase in omics data generated by modern sequencing technologies, their analysis can be tricky and often requires substantial expertise in bioinformatics. To address this concern, we have developed a user-friendly pipeline to analyze (cancer) genomic data that takes in raw sequencing data (FASTQ format) as input and outputs insightful statistics. Our iCOMIC toolkit pipeline featuring many independent workflows is embedded in the popular Snakemake workflow management system. It can analyze whole-genome and transcriptome data and is characterized by a user-friendly GUI that offers several advantages, including minimal execution steps and eliminating the need for complex command-line arguments. Notably, we have integrated algorithms developed in-house to predict pathogenicity among cancer-causing mutations and differentiate between tumor suppressor genes and oncogenes from somatic mutation data. We benchmarked our tool against Genome In A Bottle benchmark dataset (NA12878) and got the highest F1 score of 0.971 and 0.988 for indels and SNPs, respectively, using the BWA MEM-GATK HC DNA-Seq pipeline. Similarly, we achieved a correlation coefficient of r = 0.85 using the HISAT2-StringTie-ballgown and STAR-StringTie-ballgown RNA-Seq pipelines on the human monocyte dataset (SRP082682). Overall, our tool enables easy analyses of omics datasets, significantly ameliorating complex data analysis pipelines.

Metabolic modeling of the International Space Station microbiome reveals key microbial interactions.

BACKGROUND: Recent studies have provided insights into the persistence and succession of microbes aboard the International Space Station (ISS), notably the dominance of Klebsiella pneumoniae. However, the interactions between the various microbes aboard the ISS and how they shape the microbiome remain to be clearly understood. In this study, we apply a computational approach to predict possible metabolic interactions in the ISS microbiome and shed further light on its organization. RESULTS: Through a combination of a systems-based graph-theoretical approach, and a constraint-based community metabolic modeling approach, we demonstrated several key interactions in the ISS microbiome. These complementary approaches provided insights into the metabolic interactions and dependencies present amongst various microbes in a community, highlighting key interactions and keystone species. Our results showed that the presence of K. pneumoniae is beneficial to many other microorganisms it coexists with, notably those from the Pantoea genus. Species belonging to the Enterobacteriaceae family were often found to be the most beneficial for the survival of other microorganisms in the ISS microbiome. However, K. pneumoniae was found to exhibit parasitic and amensalistic interactions with Aspergillus and Penicillium species, respectively. To prove this metabolic prediction, K. pneumoniae and Aspergillus fumigatus were co-cultured under normal and simulated microgravity, where K. pneumoniae cells showed parasitic characteristics to the fungus. The electron micrography revealed that the presence of K. pneumoniae compromised the morphology of fungal conidia and degenerated its biofilm-forming structures. CONCLUSION: Our study underscores the importance of K. pneumoniae in the ISS, and its potential positive and negative interactions with other microbes, including potential pathogens. This integrated modeling approach, combined with experiments, demonstrates the potential for understanding the organization of other such microbiomes, unravelling key organisms and their interdependencies. Video Abstract.

Multi-Omic Data Improve Prediction of Personalized Tumor Suppressors and Oncogenes.

The progression of tumorigenesis starts with a few mutational and structural driver events in the cell. Various cohort-based computational tools exist to identify driver genes but require multiple samples to identify less frequently mutated driver genes. Many studies use different methods to identify driver mutations/genes from mutations that have no impact on tumor progression; however, a small fraction of patients show no mutational events in any known driver genes. Current unsupervised methods map somatic and expression data onto a network to identify personalized driver genes based on changes in expression. Our method is the first machine learning model to classify genes as tumor suppressor gene (TSG), oncogene (OG), or neutral, thus assigning the functional impact of the gene in the patient. In this study, we develop a multi-omic approach, PIVOT (Personalized Identification of driVer OGs and TSGs), to train on experimentally or computationally validated mutational and structural driver events. Given the lack of any gold standards for the identification of personalized driver genes, we label the data using four strategies and, based on classification metrics, show gene-based labeling strategies perform best. We build different models using SNV, RNA, and multi-omic features to be used based on the data available. Our models trained on multi-omic data improved predictions compared with mutation and expression data, achieving an accuracy ≥ 0.99 for BRCA, LUAD, and COAD datasets. We show network and expression-based features contribute the most to PIVOT. Our predictions on BRCA, COAD, and LUAD cancer types reveal commonly altered genes such as TP53 and PIK3CA, which are predicted drivers for multiple cancer types. Along with known driver genes, our models also identify new driver genes such as PRKCA, SOX9, and PSMD4. Our multi-omic model labels both CNV and mutations with a more considerable contribution by CNV alterations. While predicting labels for genes mutated in multiple samples, we also label rare driver events occurring in as few as one sample. We also identify genes with dual roles within the same cancer type. Overall, PIVOT labels personalized driver genes as TSGs and OGs and also identified rare driver genes.

Designing Biological Circuits: From Principles to Applications.

Genetic circuit design is a well-studied problem in synthetic biology. Ever since the first genetic circuits─the repressilator and the toggle switch─were designed and implemented, many advances have been made in this area of research. The current review systematically organizes a number of key works in this domain by employing the versatile framework of generalized morphological analysis. Literature in the area has been mapped on the basis of (a) the design methodologies used, ranging from brute-force searches to control-theoretic approaches, (b) the modeling techniques employed, (c) various circuit functionalities implemented, (d) key design characteristics, and (e) the strategies used for the robust design of genetic circuits. We conclude our review with an outlook on multiple exciting areas for future research, based on the systematic assessment of key research gaps that have been readily unravelled by our analysis framework.

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.