Stable Isotopic Evidence for Methane Seeps in Neoproterozoic Postglacial Cap Carbonates

The Earth's most severe glaciations are thought to have occurred about 600 million years ago, in the late Neoproterozoic era. A puzzling feature of glacial deposits from this interval is that they are overlain by 1–5-m-thick 'cap carbonates' (particulate deep-water marine carbonate rocks) associated with a prominent negative carbon isotope excursion. Cap carbonates have been controversially ascribed to the aftermath of almost complete shutdown of the ocean ecosystems for millions of years during such ice ages—the 'snowball Earth' hypothesis. Conversely, it has also been suggested that these carbonate rocks were the result of destabilization of methane hydrates during deglaciation and concomitant flooding of continental shelves and interior basins. The most compelling criticism of the latter 'methane hydrate' hypothesis has been the apparent lack of extreme isotopic variation in cap carbonates inferred locally to be associated with methane seeps. Here we report carbon isotopic and petrographic data from a Neoproterozoic postglacial cap carbonate in south China that provide direct evidence for methane-influenced processes during deglaciation. This evidence lends strong support to the hypothesis that methane hydrate destabilization contributed to the enigmatic cap carbonate deposition and strongly negative carbon isotopic anomalies following Neoproterozoic ice ages. This explanation requires less extreme environmental disturbance than that implied by the snowball Earth hypothesis

Predicting enzyme targets for cancer drugs by profiling human Metabolic reactions in NCI-60 cell lines

<p>Abstract</p> <p>Background</p> <p>Drugs can influence the whole metabolic system by targeting enzymes which catalyze metabolic reactions. The existence of interactions between drugs and metabolic reactions suggests a potential way to discover drug targets.</p> <p>Results</p> <p>In this paper, we present a computational method to predict new targets for approved anti-cancer drugs by exploring drug-reaction interactions. We construct a Drug-Reaction Network to provide a global view of drug-reaction interactions and drug-pathway interactions. The recent reconstruction of the human metabolic network and development of flux analysis approaches make it possible to predict each metabolic reaction's cell line-specific flux state based on the cell line-specific gene expressions. We first profile each reaction by its flux states in NCI-60 cancer cell lines, and then propose a kernel k-nearest neighbor model to predict related metabolic reactions and enzyme targets for approved cancer drugs. We also integrate the target structure data with reaction flux profiles to predict drug targets and the area under curves can reach 0.92.</p> <p>Conclusions</p> <p>The cross validations using the methods with and without metabolic network indicate that the former method is significantly better than the latter. Further experiments show the synergism of reaction flux profiles and target structure for drug target prediction. It also implies the significant contribution of metabolic network to predict drug targets. Finally, we apply our method to predict new reactions and possible enzyme targets for cancer drugs.</p

Magnetism, FeS colloids, and Origins of Life

A number of features of living systems: reversible interactions and weak bonds underlying motor-dynamics; gel-sol transitions; cellular connected fractal organization; asymmetry in interactions and organization; quantum coherent phenomena; to name some, can have a natural accounting via $physical$ interactions, which we therefore seek to incorporate by expanding the horizons of `chemistry-only' approaches to the origins of life. It is suggested that the magnetic 'face' of the minerals from the inorganic world, recognized to have played a pivotal role in initiating Life, may throw light on some of these issues. A magnetic environment in the form of rocks in the Hadean Ocean could have enabled the accretion and therefore an ordered confinement of super-paramagnetic colloids within a structured phase. A moderate H-field can help magnetic nano-particles to not only overcome thermal fluctuations but also harness them. Such controlled dynamics brings in the possibility of accessing quantum effects, which together with frustrations in magnetic ordering and hysteresis (a natural mechanism for a primitive memory) could throw light on the birth of biological information which, as Abel argues, requires a combination of order and complexity. This scenario gains strength from observations of scale-free framboidal forms of the greigite mineral, with a magnetic basis of assembly. And greigite's metabolic potential plays a key role in the mound scenario of Russell and coworkers-an expansion of which is suggested for including magnetism.Comment: 42 pages, 5 figures, to be published in A.R. Memorial volume, Ed Krishnaswami Alladi, Springer 201

Effects of X-ray dose on rhizosphere studies using X-ray computed tomography

X-ray Computed Tomography (CT) is a non-destructive imaging technique originally designed for diagnostic medicine, which was adopted for rhizosphere and soil science applications in the early 1980s. X-ray CT enables researchers to simultaneously visualise and quantify the heterogeneous soil matrix of mineral grains, organic matter, air-filled pores and water-filled pores. Additionally, X-ray CT allows visualisation of plant roots in situ without the need for traditional invasive methods such as root washing. However, one routinely unreported aspect of X-ray CT is the potential effect of X-ray dose on the soil-borne microorganisms and plants in rhizosphere investigations. Here we aimed to i) highlight the need for more consistent reporting of X-ray CT parameters for dose to sample, ii) to provide an overview of previously reported impacts of X-rays on soil microorganisms and plant roots and iii) present new data investigating the response of plant roots and microbial communities to X-ray exposure. Fewer than 5% of the 126 publications included in the literature review contained sufficient information to calculate dose and only 2.4% of the publications explicitly state an estimate of dose received by each sample. We conducted a study involving rice roots growing in soil, observing no significant difference between the numbers of root tips, root volume and total root length in scanned versus unscanned samples. In parallel, a soil microbe experiment scanning samples over a total of 24 weeks observed no significant difference between the scanned and unscanned microbial biomass values. We conclude from the literature review and our own experiments that X-ray CT does not impact plant growth or soil microbial populations when employing a low level of dose (<30 Gy). However, the call for higher throughput X-ray CT means that doses that biological samples receive are likely to increase and thus should be closely monitored

Multi-objective optimization of genome-scale metabolic models: the case of ethanol production

Ethanol is among the largest fermentation product used worldwide, accounting for more than 90% of all biofuel produced in the last decade. However current production methods of ethanol are unable to meet the requirements of increasing global demand, because of low yields on glucose sources. In this work, we present an in silico multi-objective optimization and analyses of eight genome-scale metabolic networks for the overproduction of ethanol within the engineered cell. We introduce MOME (multi-objective metabolic engineering) algorithm, that models both gene knockouts and enzymes up and down regulation using the Redirector framework. In a multi-step approach, MOME tackles the multi-objective optimization of biomass and ethanol production in the engineered strain; and performs genetic design and clustering analyses on the optimization results. We find in silico E. coli Pareto optimal strains with a knockout cost of 14 characterized by an ethanol production up to 19.74mmolgDW−1h−1 (+832.88% with respect to wild-type) and biomass production of 0.02h−1 (−98.06% ). The analyses on E. coli highlighted a single knockout strategy producing 16.49mmolgDW−1h−1 (+679.29% ) ethanol, with biomass equals to 0.23h−1 (−77.45% ). We also discuss results obtained by applying MOME to metabolic models of: (i) S. aureus; (ii) S. enterica; (iii) Y. pestis; (iv) S. cerevisiae; (v) C. reinhardtii; (vi) Y. lipolytica. We finally present a set of simulations in which constrains over essential genes and minimum allowable biomass were included. A bound over the maximum allowable biomass was also added, along with other settings representing rich media compositions. In the same conditions the maximum improvement in ethanol production is +195.24%

Genome sequencing and carrier testing: decisions on categorization and whether to disclose results of carrier testing

We are investigating the use of genome sequencing for preconception carrier testing. Genome sequencing could identify one or more of thousands of X-linked or autosomal recessive conditions that could be disclosed during preconception or prenatal counseling. Therefore, a framework that helps both clinicians and patients understand the possible range of findings is needed to respect patient preferences by ensuring that information about only the desired types of genetic conditions are provided to a given patient

Resectable pancreatic small cell carcinoma

Primary pancreatic small cell carcinoma (SCC) is rare, with just over 30 cases reported in the literature. Only 7 of these patients underwent surgical resection with a median survival of 6 months. Prognosis of SCC is therefore considered to be poor, and the role of adjuvant therapy is uncertain. Here we report two institutions' experience with resectable pancreatic SCC. Six patients with pancreatic SCC treated at the Johns Hopkins Hospital (4 patients) and the Mayo Clinic (2 patients) were identified from prospectively collected pancreatic cancer databases and re-reviewed by pathology. All six patients underwent a pancreaticoduodenectomy. Clinicopathologic data were analyzed, and the literature on pancreatic SCC was reviewed. Median age at diagnosis was 50 years (range 27–60). All six tumors arose in the head of the pancreas. Median tumor size was 3 cm, and all cases had positive lymph nodes except for one patient who only had five nodes sampled. There were no perioperative deaths and three patients had at least one postoperative complication. All six patients received adjuvant therapy, five of whom were given combined modality treatment with radiation, cisplatin, and etoposide. Median survival was 20 months with a range of 9–173 months. The patient who lived for 9 months received chemotherapy only, while the patient who lived for 173 months was given chemoradiation with cisplatin and etoposide and represents the longest reported survival time from pancreatic SCC to date. Pancreatic SCC is an extremely rare form of cancer with a poor prognosis. Patients in this surgical series showed favorable survival rates when compared to prior reports of both resected and unresectable SCC. Cisplatin and etoposide appears to be the preferred chemotherapy regimen, although its efficacy remains uncertain, as does the role of combined modality treatment with radiation

The Signaling Petri Net-Based Simulator: A Non-Parametric Strategy for Characterizing the Dynamics of Cell-Specific Signaling Networks

Reconstructing cellular signaling networks and understanding how they work are major endeavors in cell biology. The scale and complexity of these networks, however, render their analysis using experimental biology approaches alone very challenging. As a result, computational methods have been developed and combined with experimental biology approaches, producing powerful tools for the analysis of these networks. These computational methods mostly fall on either end of a spectrum of model parameterization. On one end is a class of structural network analysis methods; these typically use the network connectivity alone to generate hypotheses about global properties. On the other end is a class of dynamic network analysis methods; these use, in addition to the connectivity, kinetic parameters of the biochemical reactions to predict the network's dynamic behavior. These predictions provide detailed insights into the properties that determine aspects of the network's structure and behavior. However, the difficulty of obtaining numerical values of kinetic parameters is widely recognized to limit the applicability of this latter class of methods

TFEB regulates murine liver cell fate during development and regeneration

It is well established that pluripotent stem cells in fetal and postnatal liver (LPCs) can differentiate into both hepatocytes and cholangiocytes. However, the signaling pathways implicated in the differentiation of LPCs are still incompletely understood. Transcription Factor EB (TFEB), a master regulator of lysosomal biogenesis and autophagy, is known to be involved in osteoblast and myeloid differentiation, but its role in lineage commitment in the liver has not been investigated. Here we show that during development and upon regeneration TFEB drives the differentiation status of murine LPCs into the progenitor/cholangiocyte lineage while inhibiting hepatocyte differentiation. Genetic interaction studies show that Sox9, a marker of precursor and biliary cells, is a direct transcriptional target of TFEB and a primary mediator of its effects on liver cell fate. In summary, our findings identify an unexplored pathway that controls liver cell lineage commitment and whose dysregulation may play a role in biliary cancer

Comparative effects of whey and casein proteins on satiety in overweight and obese individuals: A randomized controlled trial

Background/Objective: Dairy protein seems to reduce appetite by increasing satiety and delaying the return of hunger and subsequently lowering energy intake compared with fat or carbohydrate. The aim of this study was to compare the effect of whey with that of casein proteins on satiety in overweight/obese individuals. Methods/Subjects: This was a randomized, parallel-design 12-week-long study. Seventy subjects with a body mass index between 25 and 40 kg/m2 and aged 18–65 years were randomized into one of three supplement groups: glucose control (n=25), casein (n=20) or whey (n=25) protein. Before commencing the study, at weeks 6 and 12 of the treatment, a Visual Analogue Scale (VAS) was used to measure subjective sensations of appetite before lunch and before dinner. Results: Rating for VAS (mm) at 6 and 12 weeks showed significantly higher satiety in the whey group compared with the casein (P=0.017 and P=0.025, respectively) or control (P=0.024 and P=0.032, respectively) groups when measured before lunch. Similarly, at 6 and 12 weeks, the score for fullness was also significantly higher in the whey group compared with both casein (P=0.038 and P=0.022, respectively) and control (P=0.020 and P=0.030, respectively) groups. However, these short-term effects on satiety from dairy whey proteins did not have any long-term effects on energy intake or body weight over 12 weeks compared with casein. Conclusions: Collectively, whey protein supplementation appears to have a positive and acute postprandial effect on satiety and fullness compared with casein and carbohydrate supplementation in overweight and obese individuals