Primitive purine biosynthesis connects ancient geochemistry to modern metabolism.

An unresolved question in the origin and evolution of life is whether a continuous path from geochemical precursors to the majority of molecules in the biosphere can be reconstructed from modern-day biochemistry. Here we identified a feasible path by simulating the evolution of biosphere-scale metabolism, using only known biochemical reactions and models of primitive coenzymes. We find that purine synthesis constitutes a bottleneck for metabolic expansion, which can be alleviated by non-autocatalytic phosphoryl coupling agents. Early phases of the expansion are enriched with enzymes that are metal dependent and structurally symmetric, supporting models of early biochemical evolution. This expansion trajectory suggests distinct hypotheses regarding the tempo, mode and timing of metabolic pathway evolution, including a late appearance of methane metabolisms and oxygenic photosynthesis consistent with the geochemical record. The concordance between biological and geological analyses suggests that this trajectory provides a plausible evolutionary history for the vast majority of core biochemistry.

Life detection in a universe of false positives: Can the Fatal Flaws of Exoplanet Biosignatures be Overcome Absent a Theory of Life?

Astrobiology aims to determine the distribution and diversity of life in the universe. But as the word "biosignature" suggests, what will be detected is not life itself, but an observation implicating living systems. Our limited access to other worlds suggests this observation is more likely to reflect out-of-equilibrium gasses than a writhing octopus. Yet, anything short of a writhing octopus will raise skepticism about what has been detected. Resolving that skepticism requires a theory to delineate processes due to life and those due to abiotic mechanisms. This poses an existential question for life detection: How do astrobiologists plan to detect life on exoplanets via features shared between non-living and living systems? We argue that you cannot without an underlying theory of life. We illustrate this by analyzing the hypothetical detection of an "Earth 2.0" exoplanet. Without a theory of life, we argue the community should focus on identifying unambiguous features of life via four areas: examining life on Earth, building life in the lab, probing the solar system, and searching for technosignatures. Ultimately, we ask, what exactly do astrobiologists hope to learn by searching for life?

An open source computational workflow for the discovery of autocatalytic networks in abiotic reactions.

A central question in origins of life research is how non-entailed chemical processes, which simply dissipate chemical energy because they can do so due to immediate reaction kinetics and thermodynamics, enabled the origin of highly-entailed ones, in which concatenated kinetically and thermodynamically favorable processes enhanced some processes over others. Some degree of molecular complexity likely had to be supplied by environmental processes to produce entailed self-replicating processes. The origin of entailment, therefore, must connect to fundamental chemistry that builds molecular complexity. We present here an open-source chemoinformatic workflow to model abiological chemistry to discover such entailment. This pipeline automates generation of chemical reaction networks and their analysis to discover novel compounds and autocatalytic processes. We demonstrate this pipeline's capabilities against a well-studied model system by vetting it against experimental data. This workflow can enable rapid identification of products of complex chemistries and their underlying synthetic relationships to help identify autocatalysis, and potentially self-organization, in such systems. The algorithms used in this study are open-source and reconfigurable by other user-developed workflows.

An Evaluation of Annual Adherence to Lung Cancer Screening in a Large National Cohort.

INTRODUCTION: Lung cancer screening reduces mortality in large RCTs where adherence is high. Unfortunately, recently published adherence rates do not replicate those seen in trials. Previous publications support a centralized approach to ensure patient eligibility and improve adherence. METHODS: Investigators reviewed a large, geographically diverse cohort of patients from 14 health systems, with 73 centers across the U.S. Lung cancer screening patients were screened from 2015 to 2019 and tracked utilizing a commercial system. Data were analyzed in 2019-2021. Demographics, eligibility, imaging results, and cancer diagnosis were collected. Overall return was calculated for 2 years (Time 0-Time 1 and Time 1-Time 2) on the basis of follow-up through March 31, 2020. Only U.S. Preventive Services Task Force-eligible patients with a normal or benign result (Lung-Reporting and Data System 1 or 2) at baseline (Time 0) were included in annual adherence calculations. RESULTS: A total of 30,166 patients were screened; 50% were male, with a mean age of 65 years. Most individuals currently smoked (58.3%), with an average of 48.3 pack years. A total of 58% were White, 6% were Black, and 34% had race information unavailable. U.S. Preventive Services Task Force eligibility criteria were not met by 10.6%. Of the 26,958 patients eligible at baseline, 76% were Lung-Reporting and Data System 1 or 2. Annual adherence at Year 1 (Time 0-Time 1) was 48.4%. Adherence at Year 2 (Time 1-Time 2) was 44.4%. A total of 93 U.S. Preventive Services Task Force‒eligible patients were diagnosed with lung cancers, mostly during the first annual follow-up. CONCLUSIONS: In this large cohort screened and managed primarily using a commercial tracking platform, most patients were U.S. Preventive Services Task Force eligible. However, annual adherence was poor despite this resource, suggesting that additional interventions are needed to recognize the full mortality benefit from screening programs.

Guideline-Recommended Lung Cancer Screening Adherence Is Superior With a Centralized Approach.

BACKGROUND: To recognize fully the benefit of lung cancer screening (LCS), annual adherence must approach the high levels seen in the National Lung Screening Trial. Emerging data suggest that annual adherence is poor and that a centralized approach to screening improves adherence. RESEARCH QUESTIONS: Do differences in adherence exist between a centralized and decentralized approach to LCS within a hybrid program and what are predictors of adherence? STUDY DESIGN AND METHODS: A retrospective evaluation of a single-center hybrid LCS program was conducted to compare outcomes including patient eligibility and adherence between the centralized and decentralized approaches. Patient demographics and outcomes were compared between those screened with a centralized and decentralized approach and between adherent and nonadherent patients using two-sample t tests, χ 2 tests, or analyses of variance, as appropriate. Annual adherence analysis was conducted using data from patients who remained eligible for screening with a baseline Lung CT Screening Reporting and Data System (Lung-RADS) score of 1 or 2. Logistic regression was used to estimate the association between adherence and the primary exposure, adjusting for potential confounders. RESULTS: A cohort of 1,117 patients underwent baseline low-dose CT imaging. Two hundred eleven patients (19%) were ineligible by United States Preventative Services Task Force criteria and most (90%) were screened with the decentralized approach. After exclusions, 765 patients with Lung-RADS score of 1 or 2 remained eligible for annual screening. Overall adherence was 56%; however, adherence in the centralized program was 70%, compared with 41% with the decentralized approach (P < .001). Individuals screened in a decentralized approach were 73% less likely to be adherent (OR, 0.27; 95% CI, 0.19-0.37). A greater proportion of patients with three or more comorbidities were screened outside the centralized program. INTERPRETATION: Those screened using a centralized approach were more likely to meet eligibility criteria for LCS and more likely to return for annual screening than those screened using a decentralized approach.

Scarcity of scale-free topology is universal across biochemical networks.

Biochemical reactions underlie the functioning of all life. Like many examples of biology or technology, the complex set of interactions among molecules within cells and ecosystems poses a challenge for quantification within simple mathematical objects. A large body of research has indicated many real-world biological and technological systems, including biochemistry, can be described by power-law relationships between the numbers of nodes and edges, often described as "scale-free". Recently, new statistical analyses have revealed true scale-free networks are rare. We provide a first application of these methods to data sampled from across two distinct levels of biological organization: individuals and ecosystems. We analyze a large ensemble of biochemical networks including networks generated from data of 785 metagenomes and 1082 genomes (sampled from the three domains of life). The results confirm no more than a few biochemical networks are any more than super-weakly scale-free. Additionally, we test the distinguishability of individual and ecosystem-level biochemical networks and show there is no sharp transition in the structure of biochemical networks across these levels of organization moving from individuals to ecosystems. This result holds across different network projections. Our results indicate that while biochemical networks are not scale-free, they nonetheless exhibit common structure across different levels of organization, independent of the projection chosen, suggestive of shared organizing principles across all biochemical networks.

Seeding Biochemistry on Other Worlds: Enceladus as a Case Study.

The Solar System is becoming increasingly accessible to exploration by robotic missions to search for life. However, astrobiologists currently lack well-defined frameworks to quantitatively assess the chemical space accessible to life in these alien environments. Such frameworks will be critical for developing concrete predictions needed for future mission planning, both to determine the potential viability of life on other worlds and to anticipate the molecular biosignatures that life could produce. Here, we describe how uniting existing methods provides a framework to study the accessibility of biochemical space across diverse planetary environments. Our approach combines observational data from planetary missions with genomic data catalogued from across Earth and analyzed using computational methods from network theory. To demonstrate this, we use 307 biochemical networks generated from genomic data collected across Earth and "seed" these networks with molecules confirmed to be present on Saturn's moon Enceladus. By expanding through known biochemical reaction space starting from these seed compounds, we are able to determine which products of Earth's biochemistry are, in principle, reachable from compounds available in the environment on Enceladus, and how this varies across different examples of life from Earth (organisms, ecosystems, planetary-scale biochemistry). While we find that none of the 307 prokaryotes analyzed meet the threshold for viability, the reaction space covered by this process can provide a map of possible targets for detection of Earth-like life on Enceladus, as well as targets for synthetic biology approaches to seed life on Enceladus. In cases where biochemistry is not viable because key compounds are missing, we identify the environmental precursors required to make it viable, thus providing a set of compounds to prioritize for detection in future planetary exploration missions aimed at assessing the ability of Enceladus to sustain Earth-like life or directed panspermia.

Universal scaling across biochemical networks on Earth.

The application of network science to biology has advanced our understanding of the metabolism of individual organisms and the organization of ecosystems but has scarcely been applied to life at a planetary scale. To characterize planetary-scale biochemistry, we constructed biochemical networks using a global database of 28,146 annotated genomes and metagenomes and 8658 cataloged biochemical reactions. We uncover scaling laws governing biochemical diversity and network structure shared across levels of organization from individuals to ecosystems, to the biosphere as a whole. Comparing real biochemical reaction networks to random reaction networks reveals that the observed biological scaling is not a product of chemistry alone but instead emerges due to the particular structure of selected reactions commonly participating in living processes. We show that the topology of biochemical networks for the three domains of life is quantitatively distinguishable, with >80% accuracy in predicting evolutionary domain based on biochemical network size and average topology. Together, our results point to a deeper level of organization in biochemical networks than what has been understood so far.

Criticality Distinguishes the Ensemble of Biological Regulatory Networks.

The hypothesis that many living systems should exhibit near-critical behavior is well motivated theoretically, and an increasing number of cases have been demonstrated empirically. However, a systematic analysis across biological networks, which would enable identification of the network properties that drive criticality, has not yet been realized. Here, we provide a first comprehensive survey of criticality across a diverse sample of biological networks, leveraging a publicly available database of 67 Boolean models of regulatory circuits. We find all 67 networks to be near critical. By comparing to ensembles of random networks with similar topological and logical properties, we show that criticality in biological networks is not predictable solely from macroscale properties such as mean degree ⟨K⟩ and mean bias in the logic functions ⟨p⟩, as previously emphasized in theories of random Boolean networks. Instead, the ensemble of real biological circuits is jointly constrained by the local causal structure and logic of each node. In this way, biological regulatory networks are more distinguished from random networks by their criticality than by other macroscale network properties such as degree distribution, edge density, or fraction of activating conditions.

Exoplanet Biosignatures: Future Directions.

We introduce a Bayesian method for guiding future directions for detection of life on exoplanets. We describe empirical and theoretical work necessary to place constraints on the relevant likelihoods, including those emerging from better understanding stellar environment, planetary climate and geophysics, geochemical cycling, the universalities of physics and chemistry, the contingencies of evolutionary history, the properties of life as an emergent complex system, and the mechanisms driving the emergence of life. We provide examples for how the Bayesian formalism could guide future search strategies, including determining observations to prioritize or deciding between targeted searches or larger lower resolution surveys to generate ensemble statistics and address how a Bayesian methodology could constrain the prior probability of life with or without a positive detection. Key Words: Exoplanets-Biosignatures-Life detection-Bayesian analysis. Astrobiology 18, 779-824.