Fractionation of Hydrogen Isotopes by Sulfate- and Nitrate-Reducing Bacteria.

Hydrogen atoms from water and food are incorporated into biomass during cellular metabolism and biosynthesis, fractionating the isotopes of hydrogen-protium and deuterium-that are recorded in biomolecules. While these fractionations are often relatively constant in plants, large variations in the magnitude of fractionation are observed for many heterotrophic microbes utilizing different central metabolic pathways. The correlation between metabolism and lipid δ(2)H provides a potential basis for reconstructing environmental and ecological parameters, but the calibration dataset has thus far been limited mainly to aerobes. Here we report on the hydrogen isotopic fractionations of lipids produced by nitrate-respiring and sulfate-reducing bacteria. We observe only small differences in fractionation between oxygen- and nitrate-respiring growth conditions, with a typical pattern of variation between substrates that is broadly consistent with previously described trends. In contrast, fractionation by sulfate-reducing bacteria does not vary significantly between different substrates, even when autotrophic and heterotrophic growth conditions are compared. This result is in marked contrast to previously published observations and has significant implications for the interpretation of environmental hydrogen isotope data. We evaluate these trends in light of metabolic gene content of each strain, growth rate, and potential flux and reservoir-size effects of cellular hydrogen, but find no single variable that can account for the differences between nitrate- and sulfate-respiring bacteria. The emerging picture of bacterial hydrogen isotope fractionation is therefore more complex than the simple correspondence between δ(2)H and metabolic pathway previously understood from aerobes. Despite the complexity, the large signals and rich variability of observed lipid δ(2)H suggest much potential as an environmental recorder of metabolism

Methyl-compound use and slow growth characterize microbial life in 2-km-deep subseafloor coal and shale beds

The past decade of scientific ocean drilling has revealed seemingly ubiquitous, slow-growing microbial life within a range of deep biosphere habitats. Integrated Ocean Drilling Program Expedition 337 expanded these studies by successfully coring Miocene-aged coal beds 2 km below the seafloor hypothesized to be “hot spots” for microbial life. To characterize the activity of coal-associated microorganisms from this site, a series of stable isotope probing (SIP) experiments were conducted using intact pieces of coal and overlying shale incubated at in situ temperatures (45 °C). The 30-month SIP incubations were amended with deuterated water as a passive tracer for growth and different combinations of ^(13)C- or ^(15)N-labeled methanol, methylamine, and ammonium added at low (micromolar) concentrations to investigate methylotrophy in the deep subseafloor biosphere. Although the cell densities were low (50–2,000 cells per cubic centimeter), bulk geochemical measurements and single-cell–targeted nanometer-scale secondary ion mass spectrometry demonstrated active metabolism of methylated substrates by the thermally adapted microbial assemblage, with differing substrate utilization profiles between coal and shale incubations. The conversion of labeled methylamine and methanol was predominantly through heterotrophic processes, with only minor stimulation of methanogenesis. These findings were consistent with in situ and incubation 16S rRNA gene surveys. Microbial growth estimates in the incubations ranged from several months to over 100 y, representing some of the slowest direct measurements of environmental microbial biosynthesis rates. Collectively, these data highlight a small, but viable, deep coal bed biosphere characterized by extremely slow-growing heterotrophs that can utilize a diverse range of carbon and nitrogen substrates

Combining random forest and 2D correlation analysis to identify serum spectral signatures for neuro-oncology

Fourier transform infrared (FTIR) spectroscopy has long been established as an analytical tech- nique for the measurement of vibrational modes of molecular systems. More recently, FTIR has been used for the analysis of biofluids with the aim of becoming a tool to aid diagnosis. For the clinician, this represents a convenient, fast, non-subjective option for the study of biofluids and the diagnosis of disease states. The patient also benefits from this method, as the procedure for the collection of serum is much less invasive and stressful than traditional biopsy. This is especially true of patients in whom brain cancer is suspected. A brain biopsy carries a degree of morbidity and mortality and on occasion may even be inconclusive. We therefore present a method for the diagnosis of brain cancer from serum samples using FTIR and machine learning techniques. The scope of the study involved 433 patients from whom were collected 9 spectra each in the range 600-4000 cm−1. To begin development of the novel method, various pre-processing steps were investigated and ranked in terms of final accuracy of the diagnosis. Random Forest machine learning was utilised as a classifier to separate patients into cancer or non-cancer categories based upon the intensities of wavenumbers present in their spectra. Generalised 2D correlational analysis was then employed to further augment the machine learning, and also to establish spec- tral features important for the distinction between cancer and non-cancer serum samples. Using these methods, sensitivities of up to 92.8% and specificities of up to 91.5% were possible. Fur- thermore, ratiometrics were also investigated in order to establish any correlations present in the dataset. We show a rapid, computationally light, accurate, statistically robust methodology for the identification of spectral features present in differing disease states. With current advances in IR technology, such as the development of rapid discrete frequency collection, this approach is import to allow future clinical translation and enables IR to achieve its potential

Fractionation of Hydrogen Isotopes by Sulfate- and Nitrate-Reducing Bacteria

Hydrogen atoms from water and food are incorporated into biomass during cellular metabolism and biosynthesis, fractionating the isotopes of hydrogen—protium and deuterium—that are recorded in biomolecules. While these fractionations are often relatively constant in plants, large variations in the magnitude of fractionation are observed for many heterotrophic microbes utilizing different central metabolic pathways. The correlation between metabolism and lipid δ^2H provides a potential basis for reconstructing environmental and ecological parameters, but the calibration dataset has thus far been limited mainly to aerobes. Here we report on the hydrogen isotopic fractionations of lipids produced by nitrate-respiring and sulfate-reducing bacteria. We observe only small differences in fractionation between oxygen- and nitrate-respiring growth conditions, with a typical pattern of variation between substrates that is broadly consistent with previously described trends. In contrast, fractionation by sulfate-reducing bacteria does not vary significantly between different substrates, even when autotrophic and heterotrophic growth conditions are compared. This result is in marked contrast to previously published observations and has significant implications for the interpretation of environmental hydrogen isotope data. We evaluate these trends in light of metabolic gene content of each strain, growth rate, and potential flux and reservoir-size effects of cellular hydrogen, but find no single variable that can account for the differences between nitrate- and sulfate-respiring bacteria. The emerging picture of bacterial hydrogen isotope fractionation is therefore more complex than the simple correspondence between δ^2H and metabolic pathway previously understood from aerobes. Despite the complexity, the large signals and rich variability of observed lipid δ^2H suggest much potential as an environmental recorder of metabolism

Geochemical, metagenomic and metaproteomic insights into trace metal utilization by methane-oxidizing microbial consortia in sulphidic marine sediments

Microbes have obligate requirements for trace metals in metalloenzymes that catalyse important biogeochemical reactions. In anoxic methane- and sulphide-rich environments, microbes may have unique adaptations for metal acquisition and utilization because of decreased bioavailability as a result of metal sulphide precipitation. However, micronutrient cycling is largely unexplored in cold (≤ 10°C) and sulphidic (> 1 mM ΣH_(2)S) deep-sea methane seep ecosystems. We investigated trace metal geochemistry and microbial metal utilization in methane seeps offshore Oregon and California, USA, and report dissolved concentrations of nickel (0.5–270 nM), cobalt (0.5–6 nM), molybdenum (10–5600 nM) and tungsten (0.3–8 nM) in Hydrate Ridge sediment porewaters. Despite low levels of cobalt and tungsten, metagenomic and metaproteomic data suggest that microbial consortia catalysing anaerobic oxidation of methane (AOM) utilize both scarce micronutrients in addition to nickel and molybdenum. Genetic machinery for cobalt-containing vitamin B_(12) biosynthesis was present in both anaerobic methanotrophic archaea (ANME) and sulphate-reducing bacteria. Proteins affiliated with the tungsten-containing form of formylmethanofuran dehydrogenase were expressed in ANME from two seep ecosystems, the first evidence for expression of a tungstoenzyme in psychrophilic microorganisms. Overall, our data suggest that AOM consortia use specialized biochemical strategies to overcome the challenges of metal availability in sulphidic environments

Rethinking entrenched narratives about protected areas and human wellbeing in the Global South

Attempts to link human development and biodiversity conservation goals remain a constant feature of policy and practice related to protected areas (PAs). Underlying these approaches are narratives that simplify assumptions, shaping how interventions are designed and implemented. We examine evidence for five key narratives: 1) conservation is pro-poor; 2) poverty reduction benefits conservation; 3) compensation neutralises costs of conservation; 4) local participation is good for conservation; 5) secure tenure rights for local communities support effective conservation. Through a mixed-method synthesis combining a review of 100 peer-reviewed papers and 25 expert interviews, we examined if and how each narrative is supported or countered by the evidence. The first three narratives are particularly problematic. PAs can reduce material poverty, but exclusion brings substantial local costs to wellbeing, often felt by the poorest. Poverty reduction will not inevitably deliver on conservation goals and trade-offs are common. Compensation (for damage due to human wildlife conflict, or for opportunity costs), is rarely sufficient or commensurate with costs to wellbeing and experienced injustices. There is more support for narratives 4 and 5 on participation and secure tenure rights, highlighting the importance of redistributing power towards Indigenous Peoples and Local Communities in successful conservation. In light of the proposed expansion of PAs under the post-2020 Global Biodiversity Framework, we outline implications of our review for the enhancement and implementation of global targets in order to proactively integrate social equity into conservation and the accountability of conservation actors

Methanotrophic bacterial symbionts fuel dense populations of deep-sea feather duster worms (Sabellida, Annelida) and extend the spatial influence of methane seepage

Deep-sea cold seeps are dynamic sources of methane release and unique habitats supporting ocean biodiversity and productivity. Here, we describe newly discovered animal-bacterial symbioses fueled by methane, between two species of annelid (a serpulid Laminatubus and sabellid Bispira) and distinct aerobic methane-oxidizing bacteria belonging to the Methylococcales, localized to the host respiratory crown. Worm tissue δ¹³C of −44 to −58‰ are consistent with methane-fueled nutrition for both species, and shipboard stable isotope labeling experiments revealed active assimilation of ¹³C-labeled methane into animal biomass, which occurs via the engulfment of methanotrophic bacteria across the crown epidermal surface. These worms represent a new addition to the few animals known to intimately associate with methane-oxidizing bacteria and may further explain their enigmatic mass occurrence at 150–million year–old fossil seeps. High-resolution seafloor surveys document significant coverage by these symbioses, beyond typical obligate seep fauna. These findings uncover novel consumers of methane in the deep sea and, by expanding the known spatial extent of methane seeps, may have important implications for deep-sea conservation

Developing infrared spectroscopic detection for stratifying brain tumour patients: glioblastoma multiforme vs. lymphoma

Over a third of brain tumour patients visit their general practitioner more than five times prior to diagnosis in the UK, leading to 62% of patients being diagnosed as emergency presentations. Unfortunately, symptoms are non-specific to brain tumours, and the majority of these patients complain of headaches on multiple occasions before being referred to a neurologist. As there are currently no methods in place for the early detection of brain cancer, the affected patients’ average life expectancy is reduced by 20 years. These statistics indicate that the current pathway is ineffective, and there is a vast need for a rapid diagnostic test. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy is sensitive to the hallmarks of cancer, as it analyses the full range of macromolecular classes. The combination of serum spectroscopy and advanced data analysis has previously been shown to rapidly and objectively distinguish brain tumour severity. Recently, a novel high-throughput ATR accessory has been developed, which could be cost-effective to the National Health Service in the UK, and valuable for clinical translation. In this study, 765 blood serum samples have been collected from healthy controls and patients diagnosed with various types of brain cancer, contributing to one of the largest spectroscopic studies to date. Three robust machine learning techniques - random forest, partial least squares-discriminant analysis and support vector machine - have all provided promising results. The novel high-throughput technology has been validated by separating brain cancer and non-cancer with balanced accuracies of 90% which is comparable to the traditional fixed diamond crystal methodology. Furthermore, the differentiation of brain tumour type could be useful for neurologists, as some are difficult to distinguish through medical imaging alone. For example, the highly aggressive glioblastoma multiforme and primary cerebral lymphoma can appear similar on magnetic resonance imaging (MRI) scans, thus are often misdiagnosed. Here, we report the ability of infrared spectroscopy to distinguish between glioblastoma and lymphoma patients, at a sensitivity and specificity of 90.1% and 86.3%, respectively. A reliable serum diagnostic test could avoid the need for surgery and speed up time to definitive chemotherapy and radiotherapy