B.A. with Honors, Chemistry (ACS approved), Drury University 2010
Ph.D. Chemistry, Bioinorganic, Washington University in St. Louis 2015
Postdoctoral Research: University of Missouri-Columbia and The Scripps Research Institute
The Majumder Lab employs ‘omics-guided biochemistry to study the mechanisms and consequences of microbial inorganic metabolisms on environmental and human health. We achieve this by investigating 1) Organismal response to perturbations in its environment, 2) Gene and metabolite function in situ, 3) Environmental applications of novel microbial chemistries.
Theme 1: Plastics
We recognize that one of the primary limitations of the big data era to understand complex biological systems is the reliance on incomplete genome annotations. Current genome annotations do not account for the multiplicity of functions one gene may perform nor do they include atypical metabolic pathways. We use genomics, enzymology and metabolomics-based technologies to determine the roles for specific genes, enzymes and metabolites in different conditions such as (1) bioplastic-precursor synthesis from waste carbon sources, (2) degradation of recalcitrant plastics in the environment, and (3) the ecology of the interactions between microplastics and Harmful Algal Blooms.
Theme 2: Metals
As more and more organisms are sequenced and cultured, we are learning about an increasing range of microbial metabolisms and chemistries, especially from anaerobic bacteria. After determining these novel chemical capabilities, we can apply this knowledge and these microbes for greener solutions. Initial efforts in this theme are focused on the electron transfer and bioinorganic synthetic capabilities of anaerobes towards (1) enhanced wastewater treatment and (2) electricity production in photobioelectrochemical calls.
Theme 3: Non-metal (Sulfur, Nitrogen, Phosphorus) Metabolites
One of the primary lessons the scientific community learned from the human genome and earth microbiome projects is that genome information alone cannot sufficiently explain observed phenotypes and that environmental factors are a significant, if not the main driving factor affecting phenotype presentation. Using a systems-level combination of genomics, metabolomics and biochemistry, we aim to elucidate the mechanisms of how environmental factors cause changes to an organism’s metabolism and result in the observed phenotype. Potential projects focused on this theme are (1) investigating the contributions of metabolism to prognosis in tick-borne diseases like Lyme’s, and (2) the effect of metal contamination on sulfur metabolism of the gut microbiome.
Connections across themes:
We anticipate cross-talk between the themes as genetic tools, mass spectrometry methods, and data analysis pipelines developed in one theme will be applicable in others.
In this study, a mild two-stage hydrothermal pretreatment was employed to optimally valorize industrial hemp (Cannabis sativa sp.) fibrous waste into sugars for Poly(3-hydroxybuyrate) (PHB) production using recombinant Escherichia coli LSBJ. Biomass was pretreated using hot water at 160, 180, and 200 °C for 5 and 10 min (15% solids), followed by disk refining. The sugar yields during enzymatic hydrolysis were found to improve with increasing temperature and the yields for hot water-disk refining pretreatment (HWDM) were higher compared to only hot water pretreatment at all conditions. The maximum glucose (56 g/L) and cellulose conversion (92%) were achieved for HWDM at 200 °C for 10 min. The hydrolysate obtained was fermented at a sugar concentration of 20 g/L. The PHB inclusion and concentration of 48% and 1.8 g/L, respectively, were similar to those from pure sugars. A pH-controlled fermentation resulted in a near bi-fold increase in PHB yield (3.46 g/L).
Sulfate-reducing bacteria (SRB) are obligate anaerobes that can couple their growth to the reduction of sulfate. Despite the importance of SRB to global nutrient cycles and their damage to the petroleum industry, our molecular understanding of their physiology remains limited. To systematically provide new insights into SRB biology, we generated a randomly barcoded transposon mutant library in the model SRB Desulfovibrio vulgaris Hildenborough (DvH) and used this genome-wide resource to assay the importance of its genes under a range of metabolic and stress conditions. In addition to defining the essential gene set of DvH, we identified a conditional phenotype for 1,137 non-essential genes. Through examination of these conditional phenotypes, we were able to make a number of novel insights into our molecular understanding of DvH, including how this bacterium synthesizes vitamins. For example, we identified DVU0867 as an atypical L-aspartate decarboxylase required for the synthesis of pantothenic acid, provided the first experimental evidence that biotin synthesis in DvH occurs via a specialized acyl carrier protein and without methyl esters, and demonstrated that the uncharacterized dehydrogenase DVU0826:DVU0827 is necessary for the synthesis of pyridoxal phosphate. In addition, we used the mutant fitness data to identify genes involved in the assimilation of diverse nitrogen sources and gained insights into the mechanism of inhibition of chlorate and molybdate. Our large-scale fitness dataset and RB-TnSeq mutant library are community-wide resources that can be used to generate further testable hypotheses into the gene functions of this environmentally and industrially important group of bacteria.
To uncover metal toxicity targets and defense mechanisms of the facultative anaerobe Pantoea sp. strain MT58 (MT58), we used a multiomic strategy combining two global techniques, random bar code transposon site sequencing (RB-TnSeq) and activity-based metabolomics. MT58 is a metal-tolerant Oak Ridge Reservation (ORR) environmental isolate that was enriched in the presence of metals at concentrations measured in contaminated groundwater at an ORR nuclear waste site. The effects of three chemically different metals found at elevated concentrations in the ORR contaminated environment were investigated: the cation Al, the oxyanion CrO, and the oxycation UO. Both global techniques were applied using all three metals under both aerobic and anaerobic conditions to elucidate metal interactions mediated through the activity of metabolites and key genes/proteins. These revealed that Al binds intracellular arginine, CrO enters the cell through sulfate transporters and oxidizes intracellular reduced thiols, and membrane-bound lipopolysaccharides protect the cell from UO toxicity. In addition, the Tol outer membrane system contributed to the protection of cellular integrity from the toxic effects of all three metals. Likewise, we found evidence of regulation of lipid content in membranes under metal stress. Individually, RB-TnSeq and metabolomics are powerful tools to explore the impact various stresses have on biological systems. Here, we show that together they can be used synergistically to identify the molecular actors and mechanisms of these pertubations to an organism, furthering our understanding of how living systems interact with their environment. IMPORTANCE Studying microbial interactions with their environment can lead to a deeper understanding of biological molecular mechanisms. In this study, two global techniques, RB-TnSeq and activity metabolomics, were successfully used to probe the interactions between a metal-resistant microorganism, Pantoea sp. strain MT58, and metals contaminating a site where the organism can be located. A number of novel metal-microbe interactions were uncovered, including Al toxicity targeting arginine synthesis, which could lead to a deeper understanding of the impact Al contamination has on microbial communities as well as its impact on higher-level organisms, including plants for whom Al contamination is an issue. Using multiomic approaches like the one described here is a way to further our understanding of microbial interactions and their impacts on the environment overall.
Over the last century, leaps in technology for imaging, sampling, detection, high-throughput sequencing, and -omics analyses have revolutionized microbial ecology to enable rapid acquisition of extensive datasets for microbial communities across the ever-increasing temporal and spatial scales. The present challenge is capitalizing on our enhanced abilities of observation and integrating diverse data types from different scales, resolutions, and disciplines to reach a causal and mechanistic understanding of how microbial communities transform and respond to perturbations in the environment. This type of causal and mechanistic understanding will make predictions of microbial community behavior more robust and actionable in addressing microbially mediated global problems. To discern drivers of microbial community assembly and function, we recognize the need for a conceptual, quantitative framework that connects measurements of genomic potential, the environment, and ecological and physical forces to rates of microbial growth at specific locations. We describe the Framework for Integrated, Conceptual, and Systematic Microbial Ecology (FICSME), an experimental design framework for conducting process-focused microbial ecology studies that incorporates biological, chemical, and physical drivers of a microbial system into a conceptual model. Through iterative cycles that advance our understanding of the coupling across scales and processes, we can reliably predict how perturbations to microbial systems impact ecosystem-scale processes or vice versa. We describe an approach and potential applications for using the FICSME to elucidate the mechanisms of globally important ecological and physical processes, toward attaining the goal of predicting the structure and function of microbial communities in chemically complex natural environments.
Competition between nitrate-reducing bacteria (NRB) and sulfate-reducing bacteria (SRB) for resources in anoxic environments is generally thought to be governed largely by thermodynamics. It is now recognized that intermediates of nitrogen and sulfur cycling (e.g., hydrogen sulfide, nitrite, etc.) can also directly impact NRB and SRB activities in freshwater, wastewater, and sediment and therefore may play important roles in competitive interactions. Here, through comparative transcriptomic and metabolomic analyses, we have uncovered mechanisms of hydrogen sulfide- and cysteine-mediated inhibition of nitrate respiratory growth for the NRB Intrasporangium calvum C5. Specifically, the systems analysis predicted that cysteine and hydrogen sulfide inhibit growth of I. calvum C5 by disrupting distinct steps across multiple pathways, including branched-chain amino acid (BCAA) biosynthesis, utilization of specific carbon sources, and cofactor metabolism. We have validated these predictions by demonstrating that complementation with BCAAs and specific carbon sources relieves the growth inhibitory effects of cysteine and hydrogen sulfide. We discuss how these mechanistic insights give new context to the interplay and stratification of NRB and SRB in diverse environments. IMPORTANCE Nitrate-reducing bacteria (NRB) and sulfate-reducing bacteria (SRB) colonize diverse anoxic environments, including soil subsurface, groundwater, and wastewater. NRB and SRB compete for resources, and their interplay has major implications on the global cycling of nitrogen and sulfur species, with undesirable outcomes in some contexts. For instance, the removal of reactive nitrogen species by NRB is desirable for wastewater treatment, but in agricultural soils, NRB can drive the conversion of nitrates from fertilizers into nitrous oxide, a potent greenhouse gas. Similarly, the hydrogen sulfide produced by SRB can help sequester and immobilize toxic heavy metals but is undesirable in oil wells where competition between SRB and NRB has been exploited to suppress hydrogen sulfide production. By characterizing how reduced sulfur compounds inhibit growth and activity of NRB, we have gained systems-level and mechanistic insight into the interplay of these two important groups of organisms and drivers of their stratification in diverse environments.
Polystyrene (PS) is one of the main polymer types of plastic wastes and is known to be resistant to biodegradation, resulting in PS waste persistence in the environment. Although previous studies have reported that some microorganisms can degrade PS, enzymes and mechanisms of microorganism PS biodegradation are still unknown. In this study, we summarized microbial species that have been identified to degrade PS. By screening the available genome information of microorganisms that have been reported to degrade PS for enzymes with functional potential to depolymerize PS, we predicted target PS-degrading enzymes. We found that cytochrome P4500s, alkane hydroxylases and monooxygenases ranked as the top potential enzyme classes that can degrade PS since they can break C-C bonds. Ring-hydroxylating dioxygenases may be able to break the side-chain of PS and oxidize the aromatic ring compounds generated from the decomposition of PS. These target enzymes were distributed in Proteobacteria, Actinobacteria, Bacteroidetes, and Firmicutes, suggesting a broad potential for PS biodegradation in various earth environments and microbiomes. Our results provide insight into the enzymatic degradation of PS and suggestions for realizing the biodegradation of this recalcitrant plastic.
Cognitive computing is revolutionizing the way big data are processed and integrated, with artificial intelligence (AI) natural language processing (NLP) platforms helping researchers to efficiently search and digest the vast scientific literature. Most available platforms have been developed for biomedical researchers, but new NLP tools are emerging for biologists in other fields and an important example is metabolomics. NLP provides literature-based contextualization of metabolic features that decreases the time and expert-level subject knowledge required during the prioritization, identification and interpretation steps in the metabolomics data analysis pipeline. Here, we describe and demonstrate four workflows that combine metabolomics data with NLP-based literature searches of scientific databases to aid in the analysis of metabolomics data and their biological interpretation. The four procedures can be used in isolation or consecutively, depending on the research questions. The first, used for initial metabolite annotation and prioritization, creates a list of metabolites that would be interesting for follow-up. The second workflow finds literature evidence of the activity of metabolites and metabolic pathways in governing the biological condition on a systems biology level. The third is used to identify candidate biomarkers, and the fourth looks for metabolic conditions or drug-repurposing targets that the two diseases have in common. The protocol can take 1-4 h or more to complete, depending on the processing time of the various software used.
Calorie restriction (CR) enhances health span (the length of time that an organism remains healthy) and increases longevity across species. In mice, these beneficial effects are partly mediated by the lowering of core body temperature that occurs during CR. Conversely, the favorable effects of CR on health span are mitigated by elevating ambient temperature to thermoneutrality (30°C), a condition in which hypothermia is blunted. In this study, we compared the global metabolic response to CR of mice housed at 22°C (the standard housing temperature) or at 30°C and found that thermoneutrality reverted 39 and 78% of total systemic or hypothalamic metabolic variations caused by CR, respectively. Systemic changes included pathways that control fuel use and energy expenditure during CR. Cognitive computing-assisted analysis of these metabolomics results helped to prioritize potential active metabolites that modulated the hypothermic response to CR. Last, we demonstrated with pharmacological approaches that nitric oxide (NO) produced through the citrulline-NO pathway promotes CR-triggered hypothermia and that leucine enkephalin directly controls core body temperature when exogenously injected into the hypothalamus. Because thermoneutrality counteracts CR-enhanced health span, the multiple metabolites and pathways altered by thermoneutrality may represent targets for mimicking CR-associated effects.
The processing of sediment to accurately characterize the spatially-resolved depth profiles of geophysical and geochemical properties along with signatures of microbial density and activity remains a challenge especially in complex contaminated areas. This study processed cores from two sediment boreholes from background and contaminated core sediments and surrounding groundwater. Fresh core sediments were compared by depth to capture the changes in sediment structure, sediment minerals, biomass, and pore water geochemistry in terms of major and trace elements including pollutants, cations, anions, and organic acids. Soil porewater samples were matched to groundwater level, flow rate, and preferential flows and compared to homogenized groundwater-only samples from neighboring monitoring wells. Groundwater analysis of nearby wells only revealed high sulfate and nitrate concentrations while the same analysis using sediment pore water samples with depth was able to suggest areas high in sulfate- and nitrate-reducing bacteria based on their decreased concentration and production of reduced by-products that could not be seen in the groundwater samples. Positive correlations among porewater content, total organic carbon, trace metals and clay minerals revealed a more complicated relationship among contaminant, sediment texture, groundwater table, and biomass. The fluctuating capillary interface had high concentrations of Fe and Mn-oxides combined with trace elements including U, Th, Sr, Ba, Cu, and Co. This suggests the mobility of potentially hazardous elements, sediment structure, and biogeochemical factors are all linked together to impact microbial communities, emphasizing that solid interfaces play an important role in determining the abundance of bacteria in the sediments.
G20 biofilms were cultivated on 316 steel, 1018 steel, or borosilicate glass under steady-state conditions in electron-acceptor limiting (EAL) and electron-donor limiting (EDL) conditions with lactate and sulfate in a defined medium. Increased corrosion was observed on 1018 steel under EDL conditions compared to 316 steel, and biofilms on 1018 carbon steel under the EDL condition had at least twofold higher corrosion rates compared to the EAL condition. Protecting the 1018 metal coupon from biofilm colonization significantly reduced corrosion, suggesting that the corrosion mechanism was enhanced through attachment between the material and the biofilm. Metabolomic mass spectrometry analyses demonstrated an increase in a flavin-like molecule under the 1018 EDL condition and sulfonates under the 1018 EAL condition. These data indicate the importance of S-cycling under the EAL condition, and that the EDL is associated with increased biocorrosion indirect extracellular electron transfer mediated by endogenously produced flavin-like molecules.
Computational metabolite annotation in untargeted profiling aims at uncovering neutral molecular masses of underlying metabolites and assign those with putative identities. Existing annotation strategies rely on the observation and annotation of adducts to determine metabolite neutral masses. However, a significant fraction of features usually detected in untargeted experiments remains unannotated, which limits our ability to determine neutral molecular masses. Despite the availability of tools to annotate, relatively few of them benefit from the inherent presence of in-source fragments in liquid chromatography-electrospray ionization-mass spectrometry. In this study, we introduce a strategy to annotate in-source fragments in untargeted data using low-energy tandem mass spectrometry (MS) spectra from the METLIN library. Our algorithm, MISA (METLIN-guided in-source annotation), compares detected features against low-energy fragments from MS/MS spectra, enabling robust annotation and putative identification of metabolic features based on low-energy spectral matching. The algorithm was evaluated through an annotation analysis of a total of 140 metabolites across three different sets of biological samples analyzed with liquid chromatography-mass spectrometry. Results showed that, in cases where adducts were not formed or detected, MISA was able to uncover neutral molecular masses by in-source fragment matching. MISA was also able to provide putative metabolite identities via two annotation scores. These scores take into account the number of in-source fragments matched and the relative intensity similarity between the experimental data and the reference low-energy MS/MS spectra. Overall, results showed that in-source fragmentation is a highly frequent phenomena that should be considered for comprehensive feature annotation. Thus, combined with adduct annotation, this strategy adds a complementary annotation layer, enabling in-source fragments to be annotated and increasing putative identification confidence. The algorithm is integrated into the XCMS Online platform and is freely available at http://xcmsonline.scripps.edu .
Anthropogenic nitrate contamination is a serious problem in many natural environments. Nitrate removal by microbial action is dependent on the metal molybdenum (Mo), which is required by nitrate reductase for denitrification and dissimilatory nitrate reduction to ammonium. The soluble form of Mo, molybdate (MoO ), is incorporated into and adsorbed by iron (Fe) and aluminium (Al) (oxy) hydroxide minerals. Herein we used Oak Ridge Reservation (ORR) as a model nitrate-contaminated acidic environment to investigate whether the formation of Fe- and Al-precipitates could impede microbial nitrate removal by depleting Mo. We demonstrate that Fe and Al mineral formation that occurs as the pH of acidic synthetic groundwater is increased, decreases soluble Mo to low picomolar concentrations, a process proposed to mimic environmental diffusion of acidic contaminated groundwater. Analysis of ORR sediments revealed recalcitrant Mo in the contaminated core that co-occurred with Fe and Al, consistent with Mo scavenging by Fe/Al precipitates. Nitrate removal by ORR isolate Pseudomonas fluorescens N2A2 is virtually abolished by Fe/Al precipitate-induced Mo depletion. The depletion of naturally occurring Mo in nitrate- and Fe/Al-contaminated acidic environments like ORR or acid mine drainage sites has the potential to impede microbial-based nitrate reduction thereby extending the duration of nitrate in the environment.
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The photosynthetic membranes of the filamentous anoxygenic phototroph Roseiflexus castenholzii have been studied with electron microscopy, atomic force microscopy, and biochemistry. Electron microscopy of the light-harvesting reaction center complex produced a 3D model that aligns with the solved crystal structure of the RC-LH1 from Thermochromatium tepidum with the H subunit removed. Atomic force microscopy of the whole membranes yielded a picture of the supramolecular organization of the major proteins in the photosynthetic electron transport chain. The results point to a loosely packed membrane without accessory antenna proteins or higher order structure.
A lack of X-ray or nuclear magnetic resonance structures of proteins inhibits their further study and characterization, motivating the development of new ways of analyzing structural information without crystal structures. The combination of hydrogen-deuterium exchange mass spectrometry (HDX-MS) data in conjunction with homology modeling can provide improved structure and mechanistic predictions. Here a unique diheme cytochrome c (DHCC) protein from Heliobacterium modesticaldum is studied with both HDX and homology modeling to bring some definition of the structure of the protein and its role. Specifically, HDX data were used to guide the homology modeling to yield a more functionally relevant structural model of DHCC.
The filamentous anoxygenic phototrophic bacterium Chloroflexus aurantiacus possesses an unusual electron transfer complex called Alternative Complex III instead of the cytochrome bc or bf type complex found in nearly all other known groups of phototrophs. Earlier work has confirmed that Alternative Complex III behaves as a menaquinol:auracyanin oxidoreductase in the photosynthetic electron transfer chain. In this work, we focus on elucidating the contribution of individual subunits to the overall function of Alternative Complex III. The monoheme subunit ActE has been expressed and characterized in Escherichia coli. A partially dissociated Alternative Complex III missing subunit ActE and subunit ActG was obtained by treatment with the chaotropic agent KSCN, and was then reconstituted with the expressed ActE. Enzymatic activity of the partially dissociated Alternative Complex III was greatly reduced and was largely restored in the reconstituted complex. The redox potential of the heme in the recombinant ActE was +385mV vs. NHE, similar to the highest potential heme in the intact complex. The results strongly suggest that the monoheme subunit, ActE, is the terminal electron carrier for Alternative Complex III.
Alternative Complex III (ACIII) is a multisubunit integral membrane protein electron transfer complex that is proposed to be an energy-conserving functional replacement for the bacterial cytochrome bc1 or b6f complexes. Clues to the structure and function of this novel complex come from its relation to other bacterial enzyme families. The ACIII complex has menaquinone: electron acceptor oxidoreductase activity and contains protein subunits with multiple Fe-S centers and c-type hemes. ACIII is found in a diverse group of bacteria, including both phototrophic and nonphototrophic taxa. In the phototrophic filamentous anoxygenic phototrophs, the electron acceptor is the small blue copper protein auracyanin instead of a soluble cytochrome. Recent work on ACIII and the copper protein auracyanin is reviewed with focus on the photosynthetic systems and potential electron transfer pathways and mechanisms. Taken together, the ACIII complexes constitute a unique system for photosynthetic electron transfer and energy conservation. This article is part of a Special Issue entitled: Respiratory Complex III and related bc complexes.
Thermodynamic data for RNA 1 x 2 nucleotide internal loops are lacking. Thermodynamic data that are available for 1 x 2 loops, however, are for loops that rarely occur in nature. In order to identify the most frequently occurring 1 x 2 nucleotide internal loops, a database of 955 RNA secondary structures was compiled and searched. Twenty-four RNA duplexes containing the most common 1 x 2 nucleotide loops were optically melted, and the thermodynamic parameters DeltaH degrees , DeltaS degrees , DeltaG degrees 37, and TM for each duplex were determined. This data set more than doubles the number of 1 x 2 nucleotide loops previously studied. A table of experimental free energy contributions for frequently occurring 1 x 2 nucleotide loops (as opposed to a predictive model) is likely to result in better prediction of RNA secondary structure from sequence. In order to improve free energy calculations for duplexes containing 1 x 2 nucleotide loops that do not have experimental free energy contributions, the data collected here were combined with data from 21 previously studied 1 x 2 loops. Using linear regression, the entire dataset was used to derive nearest neighbor parameters that can be used to predict the thermodynamics of previously unmeasured 1 x 2 nucleotide loops. The DeltaG degrees 37,loop and DeltaH degrees loop nearest neighbor parameters derived here were compared to values that were published previously for 1 x 2 nucleotide loops but were derived from either a significantly smaller dataset of 1 x 2 nucleotide loops or from internal loops of various sizes [Lu, Z. J., Turner, D. H., and Mathews, D. H. (2006) Nucleic Acids Res. 34, 4912-4924]. Most of these values were found to be within experimental error, suggesting that previous approximations and assumptions associated with the derivation of those nearest neighbor parameters were valid. DeltaS degrees loop nearest neighbor parameters are also reported for 1 x 2 nucleotide loops. Both the experimental thermodynamics and the nearest neighbor parameters reported here can be used to improve secondary structure prediction from sequence.