Bacterial autotrophs often rely on CO₂ concentrating mechanisms (CCMs) to assimilate carbon. Although many CCM proteins have been identified, a systematic screen of the components of CCMs is lacking. Here, we performed a genome-wide barcoded transposon screen to identify essential and CCM-related genes in the γ-proteobacterium Halothiobacillus neapolitanus. Screening revealed that the CCM comprises at least 17 and probably no more than 25 genes, most of which are encoded in 3 operons. Two of these operons (DAB1 and DAB2) contain a two-gene locus that encodes a domain of unknown function (Pfam: PF10070) and a putative cation transporter (Pfam: PF00361). Physiological and biochemical assays demonstrated that these proteins-which we name DabA and DabB, for DABs accumulate bicarbonate-assemble into a heterodimeric complex, which contains a putative β-carbonic anhydrase-like active site and functions as an energy-coupled inorganic carbon (C_i) pump. Interestingly, DAB operons are found in a diverse range of bacteria and archaea. We demonstrate that functional DABs are present in the human pathogens Bacillus anthracis and Vibrio cholerae. On the basis of these results, we propose that DABs constitute a class of energized C_i pumps and play a critical role in the metabolism of C_i throughout prokaryotic phyla.

The history of Earth's carbon cycle reflects trends in atmospheric composition convolved with the evolution of photosynthesis. Fortunately, key parts of the carbon cycle have been recorded in the carbon isotope ratios of sedimentary rocks. The dominant model used to interpret this record as a proxy for ancient atmospheric CO₂ is based on carbon isotope fractionations of modern photoautotrophs, and longstanding questions remain about how their evolution might have impacted the record. Therefore, we measured both biomass (ε_p) and enzymatic (ε_Rubisco) carbon isotope fractionations of a cyanobacterial strain (Synechococcus elongatus PCC 7942) solely expressing a putative ancestral Form 1B rubisco dating to ≫1 Ga. This strain, nicknamed ANC, grows in ambient pCO₂ and displays larger ε_p values than WT, despite having a much smaller ε_Rubisco (17.23 ± 0.61‰ vs. 25.18 ± 0.31‰, respectively). Surprisingly, ANC ε_p exceeded ANC ε_Rubisco in all conditions tested, contradicting prevailing models of cyanobacterial carbon isotope fractionation. Such models can be rectified by introducing additional isotopic fractionation associated with powered inorganic carbon uptake mechanisms present in Cyanobacteria, but this amendment hinders the ability to accurately estimate historical pCO₂ from geological data. Understanding the evolution of rubisco and the CO₂ concentrating mechanism is therefore critical for interpreting the carbon isotope record, and fluctuations in the record may reflect the evolving efficiency of carbon fixing metabolisms in addition to changes in atmospheric CO₂.

Wastewater surveillance for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA can be integrated with COVID-19 case data to inform timely pandemic response. However, more research is needed to apply and develop systematic methods to interpret the true SARS-CoV-2 signal from noise introduced in wastewater samples (e.g., from sewer conditions, sampling and extraction methods, etc.). In this study, raw wastewater was collected weekly from five sewersheds and one residential facility. The concentrations of SARS-CoV-2 in wastewater samples were compared to geocoded COVID-19 clinical testing data. SARS-CoV-2 was reliably detected (95% positivity) in frozen wastewater samples when reported daily new COVID-19 cases were 2.4 or more per 100,000 people. To adjust for variation in sample fecal content, four normalization biomarkers were evaluated: crAssphage, pepper mild mottle virus, Bacteroides ribosomal RNA (rRNA), and human 18S rRNA. Of these, crAssphage displayed the least spatial and temporal variability. Both unnormalized SARS-CoV-2 RNA signal and signal normalized to crAssphage had positive and significant correlation with clinical testing data (Kendall's Tau-b (τ)=0.43 and 0.38, respectively), but no normalization biomarker strengthened the correlation with clinical testing data. Locational dependencies and the date associated with testing data impacted the lead time of wastewater for clinical trends, and no lead time was observed when the sample collection date (versus the result date) was used for both wastewater and clinical testing data. This study supports that trends in wastewater surveillance data reflect trends in COVID-19 disease occurrence and presents tools that could be applied to make wastewater signal more interpretable and comparable across studies.