Unit 22: Physiological Tests for Characterization and Identification of Bacteria - Biology

Unit 22: Physiological Tests for Characterization and Identification of Bacteria - Biology

We are searching data for your request:

Forums and discussions:
Manuals and reference books:
Data from registers:
Wait the end of the search in all databases.
Upon completion, a link will appear to access the found materials.

Unit 22: Physiological Tests for Characterization and Identification of Bacteria

Chapter 12 Staphylococcus

Clinical Manifestations

Staphylococci can cause many forms of infection. (1) S aureus causes superficial skin lesions (boils, styes) and localized abscesses in other sites. (2) S aureus causes deep-seated infections, such as osteomyelitis and endocarditis and more serious skin infections (furunculosis). (3) S aureus is a major cause of hospital acquired (nosocomial) infection of surgical wounds and, with S epidermidis, causes infections associated with indwelling medical devices. (4) S aureus causes food poisoning by releasing enterotoxins into food. (5) S aureus causes toxic shock syndrome by release of superantigens into the blood stream. (6) S saprophiticus causes urinary tract infections, especially in girls. (7) Other species of staphylococci (S lugdunensis, S haemolyticus, S warneri, S schleiferi, S intermedius) are infrequent pathogens.


Staphylococci are Gram-positive cocci 1μm in diameter. They form clumps.


S aureus and S intermedius are coagulase positive. All other staphylococci are coagulase negative. They are salt tolerant and often hemolytic. Identification requires biotype analysis.

Natural Habitat

S aureus colonizes the nasal passage and axillae. S epidermidis is a common human skin commensal. Other species of staphylococci are infrequent human commensals. Some are commensals of other animals.


S aureus expresses many potential virulence factors. (1) Surface proteins that promote colonization of host tissues. (2) Factors that probably inhibit phagocytosis (capsule, immunoglobulin binding protein A). (3) Toxins that damage host tissues and cause disease symptoms. Coagulase-negative staphylococci are normally less virulent and express fewer virulence factors. S epidermidis readily colonizes implanted devices.

Host Defenses

Phagocytosis is the major mechanism for combatting staphylococcal infection. Antibodies are produced which neutralize toxins and promote opsonization. The capsule and protein A may interfere with phagocytosis. Biofilm growth on implants is impervious to phagocytosis.


Infections acquired outside hospitals can usually be treated with penicillinase-resistant β-lactams. Hospital acquired infection is often caused by antibiotic resistant strains and can only be treated with vancomycin.

Antibiotic Resistance

Multiple antibiotic resistance is increasingly common in S aureus and S epidermidis. Methicillin resistance is indicative of multiple resistance. Methicillin-resistant S aureus (MRSA) causes outbreaks in hospitals and can be epidemic.


Epidemiological tracing of S aureus is traditionally performed by phage typing, but has limitations. Molecular typing methods are being tested experimentally.


Diagnosis is based on performing tests with colonies. Tests for clumping factor, coagulase, hemolysins and thermostable deoxyribonuclease are routinely used to identify S aureus. Commercial latex agglutination tests are available. Identification of S epidermidis is confirmed by commercial biotyping kits.


Patients and staff carrying epidemic strains, particularly MRSA, should be isolated. Patients may be given disinfectant baths or treated with a topical antibiotic to eradicate carriage of MRSA. Infection control programs are used in most hospitals.


Unidentified bacteria or isolates with ambiguous profiles.

One of the most attractive potential uses of 16S rRNA gene sequence informatics is to provide genus and species identification for isolates that do not fit any recognized biochemical profiles, for strains generating only a “low likelihood” or �ptable” identification according to commercial systems, or for taxa that are rarely associated with human infectious diseases. The cumulative results from a limited number of studies to date suggest that 16S rRNA gene sequencing provides genus identification in most cases (㺐%) but less so with regard to species (65 to 83%), with from 1 to 14% of the isolates remaining unidentified after testing (5, 11, 17). Difficulties encountered in obtaining a genus and species identification include the recognition of novel taxa, too few sequences deposited in nucleotide databases, species sharing similar and/or identical 16S rRNA sequences, or nomenclature problems arising from multiple genomovars assigned to single species or complexes.

Isolation and characterization of bacteria associated with the rhizosphere of halophytes (Salsola stocksii and Atriplex amnicola) for production of hydrolytic enzymes

Microbes from hypersaline environments are useful in biotechnology as sources of novel enzymes and proteins. The current study aimed to characterize halophilic bacteria from the rhizosphere of halophytes (Salsola stocksii and Atriplex amnicola), non-rhizospheric, and brine lake-bank soils collected from Khewra Salt Mine and screening of these bacterial strains for industrially important enzymes. A total of 45 bacterial isolates from the rhizosphere of Salsola, 38 isolates from Atriplex, 24 isolates from non-rhizospheric, and 25 isolates from lake-bank soils were identified by using 16S rRNA gene analysis. Phylogenetic analysis showed that bacterial strains belonging to Bacillus, Halobacillus, and Kocuria were dominant in the rhizosphere of halophytes (Salsola and Atriplex), and Halobacillus and Halomonas were dominating genera from non-rhizospheric and lake-bank soils. Mostly identified strains were moderately halophilic bacteria with optimum growth at 1.5–3.0 M salt concentrations. Most of the bacterial exhibited lipase, protease, cellulase, amylase, gelatinase, and catalase activities. Halophilic and halotolerant Bacilli (AT2RP4, HL1RS13, NRS4HaP9, and LK3HaP7) identified in this study showed optimum lipase, protease, cellulase, and amylase activities at 1.0–1.5 M NaCl concentration, pH 7–8, and temperature 37 °C. These results indicated that halophilic and halotolerant bacteria can be used for bioconversion of organic compounds to useful products under extreme conditions.


Plasmid preparation

Plasmid-containing strains were grown in Mueller Hinton broth at 37 ଌ overnight. Plasmid DNA was prepared from the over-night culture with the Qiagen Mini and Midi kits according to the manufacturer’s description for low-copy plasmids. Eluated DNA was precipitated with isopropanol, washed with 70% ethanol and resuspended in 10 mM Tris-HCl, 1 mM EDTA before analysis. The final concentration of eluated DNA was measured for each preparation using a NanoDrop.

Sample preparation

For nanofluidics experiments, DNA was stained with YOYO-1 (YOYO, Invitrogen) in a molar ratio of 1:5 to the total number of basepairs in the sample, and with netropsin (Sigma-Aldrich) in a molar ratio of 150:1 with respect to YOYO. The samples were initially mixed in 5x TBE (Tris-Borate-EDTA, Medicago, diluted with ultrapure water from 10x tablets) and left to equilibrate at room temperature for about 20 minutes. As an example, 2 μL of plasmid DNA (100 μM, bp) was mixed with 2 μL λ-DNA (100 μM, bp), 2 μL of YOYO-1 (40 μM) and 3 μL of Netropsin (4000 μM). 5x TBE was then added in order to obtain a final volume of 10 μL. Subsequently, the samples were diluted to 0.05x TBE with ultrapure water to a final concentration of typically 0.4 μM of DNA (bp). The mixing in high ionic strength was performed to enable rapid equilibration of YOYO on DNA 35 . Beta-mercaptoethanol (BME, Sigma-Aldrich) was added in 2% (v/v) to suppress excessive photonicking of the plasmids. λ-DNA (48502 bp, New England Biolabs) was used as standard for measurements of the sizes of the plasmids and was measured in the same conditions as for the plasmids.

Experimental procedure

Nanofluidic chips were fabricated in fused silica with standard methods as described elsewhere 15 . The nanofluidic chip consists of four loading wells connected two and two by microchannels, which in turn are spanned by nanochannels. The dimensions of the nanochannels were 100 ×� nm 2 or 100 ×� nm 2 , both with a length of 500 μm. The channels were pre-wetted with a mixture of 0.05x TBE and 2% (v/v) BME. For each sample, a volume of 10 μL was loaded into the chip and the DNA was forced into the nanochannels by pressure-driven flow with nitrogen gas. All plasmids were inserted in their circular form, which was ensured by visual inspection of the plasmids in the microchannel, (see Fig. 1 ). The manual selection of intact circles ensures that all DNA molecules analyzed contain the entire sequence of the plasmid, and discards fragmented plasmids and remaining chromosomal DNA, which appear as linear pieces 24 . It should also be noted that nicking of the YOYO-labeled DNA will occur rapidly by irradiation with light and hence any supercoiled plasmids will be relaxed well before insertion into the nanochannels. Circular plasmids were unfolded to their linear form, while enclosed in the nanochannels, by spontaneous photonicking caused by irradiation with the excitation light of the microscope until a double-stranded break occurs 20 ,36 . The fact that all plasmids studied were linearized inside the nanochannel further guarantees that all plasmids analyzed are intact. After unfolding, the light was switched off and the molecules were allowed to relax into their equilibrium extension. Imaging was performed with an inverted fluorescence microscope (Zeiss AxioObserver.Z1) using a 100x oil immersion objective (Zeiss, NA =𠂑.46). Using an EMCCD camera (Photometrix Evolve) and an exposure time of 100 ms, a series of about 200 images was recorded for each molecule.

Image analysis

Within our workflow, experimental barcodes are subject to three processing steps. First, raw kymographs (see Fig. 1 ) are aligned in order to reduce effects due to local conformational changes and center-of-mass diffusion of DNA during imaging, using the WPAlign method 37 . Once the kymograph has been aligned, we obtain the time-averaged barcode. Second, we remove the background from the signal region in the time-averaged barcode. To that end, we fit the sum of two sigmoidals to the data 25 . The estimated lengths, L, of the molecules are then obtained, and the barcode contains only the “signal region”. Third, we reduce end effects: in the end regions of experimental barcodes, the background and signal are intermixed due to the finite width of the microscope’s optical point spread function. To deal with this, and other end effects, we use a bit weighting scheme (Supplementary Methods 2) that effectively removes any effect due to the ends, yet retaining information about the true length, L, of the barcode.

Generating consensus barcodes from experimental barcodes

As a result of stochasticity (for instance, due to the staining process) in individual experimental barcodes, time-averaged barcodes from DNA molecules with identical sequences differ slightly from one another. In order to reduce such molecule-to-molecule fluctuations, we present a new method for averaging M barcodes from multiple molecules with the same DNA sequence (but circularly shifted and possibly flipped) into 𠇌onsensus” barcodes (Supplementary Methods 5). In brief, we first identify the two most similar barcodes out of all barcode pairs using a cross-correlation measure. These two barcodes are merged leaving us with M-1 barcodes. The procedure is then repeated until the set of barcodes has been exhausted, resulting in a consensus barcode. In our procedure, a bit weighting method (see Supplementary Methods 2) reduces end effects and results in consensus barcodes which, in all cases considered, are truly circular (no edges).

Theoretical barcodes

For comparisons with experimental data, we created a database of theoretical barcodes with sizes of 20 kbp and upwards. The sequences were retrieved from NCBI (, June 2015) and filtered to remove all duplicates, and all sequences where more than 0.01% of the bases in the sequence are undefined, resulting in a database of 3127 sequences. The theoretical barcodes are created using the DNA sequence as input and are calculated using the known statistical physics framework for competitive binding of ligands 22 . The theoretical barcodes obtained from this framework have basepair resolution and are therefore convolved with the known point spread function of the microscope in order to mimic experimental conditions.

Identifiability of plasmids

To address the question whether a particular plasmid can be experimentally identified in the database, we apply a two-step strategy: (I) First, if the correct plasmid’s length differs more than 20 percent in size from another plasmid, these two plasmids are deemed different. (II) For barcodes in the database of similar length to the correct plasmid, see criteria (I), we introduce a separability score (details below). If this score is smaller than a predetermined threshold then the correct plasmid is said to be identifiable. To calculate the separability score we first quantify the similarity of the barcode of the correct plasmid (experimental or theoretical) with all K theoretical barcodes of similar lengths, using best Pearson correlation coefficient, Ĉk, k =𠂑,….K. The term �st” here refers to the fact that, due to the circular nature of plasmids, it is necessary to slide one barcode across the other, and possibly to flip it. The sliding generates a set of cross-correlation values out of which only the best one is stored. Note that in general cross-correlation values are in the range of -1 to 1. However, the �st” cross correlation is generally in the range of 0 to 1. In all our studied cases all Ĉ-values are indeed positive. For large enough K, these best cross-correlation values are expected to follow the Gumbel probability density function (PDF), Φ(Ĉ) (probability density for the best, with two parameters) 38 and we therefore fit the histogram of Ĉk to the Gumbel PDF. In the Results section we find that a match between an experimental plasmid barcode and its 𠇌orrect” theoretical barcode has Ĉ ≈ Ĉmatch =𠂐.9. Therefore, once the two Gumbel parameters have been estimated we define a separability score, which is similar to the usual p-value definition, i.e., the separability score equals . By construction, the separability score is in the range 0 to 1 and has an average score of 0.5. We deem a plasmid identifiable if the separability score is smaller than a set threshold. In the Results section we use a conservative threshold of 0.001 and a less restrictive choice, with a threshold of 0.01, and demonstrate that the plasmid identifiability is insensitive to this choice for long enough plasmids (㹰 kbp). Details are found in Supplementary Methods 6.

Challenges in biofilm mechanics

A biofilm is not a straightforward material: it is a complex, highly heterogeneous living medium, and so is constantly evolving. Such properties yield high variability in results that have to be taken into account. This section draws attention to the high variability in the mechanical responses of biofilms and the difficulty to have repeatable results, which are among the main obstacles when dealing with the standardization of the biofilm mechanical characterization. The measurement of mechanical properties needs to reach an agreement in the community in order to validate protocols such as screening of molecules, cleaning procedures or adjusting operational parameters in industry impacted by biofilms presence.

A biofilm is a living material

Because a biofilm is a living structure, its shape and mechanical characteristics change over time. Biofilm structure and mechanical properties depend strongly on the growing environment. 105,106 The temperature, surface energy and hydrophobicity of the substratum, 95 pH, 32 flow rate in fluid conditions, 107 and nutrient and oxygen availability are some parameters liable to affect the differentiation of bacteria and biofilm formation. Changes in the growth conditions may impact the physical properties of a biofilm, which in turn are linked to its mechanical properties. 108 Overall, the growth conditions act on the biological and physical behavior of bacteria according to their survival potential. Bacterial specificity can change during the life of a biofilm due to the properties of bacteria as living organisms. This is why, it is nearly impossible to have a standardized sample shape like a test specimen of an engineering material and inter- testing method variability are expected. Sample configuration and experiment conditions should affect the result of the mechanical test and the comparison between two experiments. Biofilms are fragile living tissues, and are liable to be reshaped in the course of handling. This raises the possibility that the biofilm self-adapts under a mechanical load, and alters its mechanical properties to persist. Moreover, reported plastic deformations, 109 i.e., permanent deformations, emphasize the need to grow biofilm in situ. The sample is thus not damaged before testing. In vitro studies are often used for greater convenience and minimal changes in biofilm matter. However, in vitro biofilms differ from in vivo biofilms 110 in their structure. The results of in vitro tests are only an approximation of the reality for which assumptions have to be clarified. Moreover, due to the viscoelasticity of biofilms, flow conditions may infer modification in biofilm shape. In irrigation conditions, biofilm can roll or turn into streamers. 39,40 In that case, the experimentalist has to adapt the way he identifies the mechanical properties in terms of force applied on the biofilm and deformation of the sample. The living property of biofilm forms a significant hurdle towards the standardization of mechanical tests. It challenges the inter-experience comparison and the accuracy of the mechanical identification.

A biofilm is a heterogeneous material

A biofilm is a multiscale composite material. Depending on the point of view, the biofilm material can be seen as an homogeneous entity or as an highly heterogeneous and complex material formed of cells embedded in an extracellular matrix. As composite materials, biofilms are made of reinforcements, here the bacteria, and of a matrix, here the EPS. Moreover, biofilms is a porous matter since the EPS matrix is crossed by pores and channels through which fluid charged with nutriments can move. Fig. 1 represents the complex structure of a biofilm. Structural heterogeneity of biofilm is increased by permeability, nutrients and oxygen gradients. 111 These gradients result in shifts in metabolic activity and impact EPS production. This stratification is accompanied by corresponding physical properties. The heterogeneity of its mechanical properties is directly related to the local microstructure of the biofilm. There is a correlation between microscale morphology, macroscale configuration and mechanical behavior of a biofilm. For example, the Young’s modulus evolves with microcolony size and biofilm shape 112 in P.aeruginosa biofilms. This phenomenon is due to the increase in polymer secretion at later stages of development. The Young’s modulus of the matrix polymers influences the way the biofilm develops. The mechanical parameters differ along the directions of the biofilm sample. 80 Beyond the heterogeneity of the EPS matrix, the variation of cell density within the biofilm infers particular mechanical behavior of the biofilm. Homogenization techniques have been used 113 to study the influence of the biofilm spatial structure on the mechanical parameters. Homogenization techniques showed that the stiffness of a biofilm is linked to the density of the bacteria embedded in the matrix. They also proved that the detachment phenomenon in a biofilm obtained with wall shear stress is directly correlated with the heterogeneities in stiffness within the biofilm. The volume fraction of cells embedded in the EPS matrix is then a significant factor in the biofilm integrity. Actually, the bacteria within the EPS matrix constitutes solid pores which can influence the matrix strength according to the fraction of cells. Moreover, the water content in the biofilm impacts the physical properties of the matter. 114

Physical heterogeneities of biofilms: biofilms are heterogeneous in their composition. a Biofilms are made of reinforcements (bacteria) surrounded with matrix (EPS). The influence of the scale of the mechanical study is not insignificant. Moreover, metabolic gradients (oxygen, nutrient, physical stress, etc.) result in heterogeneity in mechanical parameters. b A focus on the internal composition reveals that EPS matrix is made of many components. Entanglements of molecules within the EPS matrix have a key role in the biofilm behavior

The biofilm characteristics discussed above make mechanical study challenging. Most experimentalists consider biofilm to be a homogeneous material, and so analyze mechanical responses averaged over the whole sample material. However, almost all biofilm characteristic properties such as biomass, concentration of chemicals or gene expression, measured at the micron scale, have been shown to exhibit strongly heterogeneous spatial distributions. Averaging biofilm at the macroscale smooths out its heterogeneity. Bulk parameters may be significantly different than microscale parameters. 102 Thus the choice of the scale of the mechanical study is important.

Implications in mechanical characterization

Any practitioner in biofilm mechanics has to be aware of the variability in the results of the measurements. Different setup lead to different result. Actually, medium used, planktonic model, flow situation, temperature and sampling point are various parameters which can vary the results. Moreover, in mechanics of materials, a test sample is assumed to be homogeneous or structurally determined, although a test result is always disturbed by noise during measurement, and so there is some variance in the results. By contrast, in biofilm mechanics, the sample is always different between two successive tests because of the inherent variability of the biofilm: in addition to the variance of the measurements, there is thus a further variability in the values which are reported. In the literature, deviations among reported values for the mechanical parameters may be due in part to differences in sample origin: bacterial EPS production or physical and chemical origin. However, it is likely that the difficulties met in conducting any mechanical study of biofilms include non-biological sources of variation. The complexity of this material makes any mechanical study of biofilms challenging, and identifying mechanical parameters especially difficult. This following section aims to call the reader’s attention to the intricacies of biofilm study, when several microbiological targets need reliable quantitative indicators to draw up and compare protocols for improved control of biofilms.


It is generally known that the metabolism of drugs and other xenobiotics may proceed through two phases, with the Phase I reactions involving the generation of functional groups that may subsequently be used in the Phase II conjugations [1]. Prominent among the Phase II conjugation reactions are methylation, sulfonation, acetylation, glucuronidation, and glutathione conjugation [2]. In addition to their involvement in drug metabolism, some of the Phase II conjugation reactions such as methylation, sulfonation, and glucuronidation have been reported to also play a role in the metabolism of key endogenous compounds such as catecholamines and steroid/thyroid hormones [3𠄷].

Enzymatic methylation of catecholamines was first discovered by Axelrod et al. [8, 9]. The responsible enzyme was identified as the catechol O-methyltransferase (COMT) which catalyzes the methylation reactions using S-adenosyl-L-methionine as the methyl group donor [2, 10, 11]. The enzyme has since been shown to be capable of methylating not only the endogenous catecholamines and catecholestrogens, but also many catechol drugs such as levodopa, carbidopa, benserazide, apomorphine, dobutamine, isoprenaline (isoproterenol), rimiterol, inamrinone, and isoetharine [10�]. In humans, there exist two isoforms of COMT, membrane-bound and soluble, that are encoded by mRNAs derived from the same gene through differential transcription/translation [13, 14]. Both the membrane-bound and soluble COMTs have been detected in various human tissues, albeit at different ratios [15]. The major physiological function of COMT-mediated methylation is generally thought to be for the deactivation of biologically-active or chemically-reactive endogenous as well as xenobiotic catechols [6, 10, 11, 16]. For catechol drugs, COMT therefore plays a dual role in undesirably lowering the efficacy of catechol drugs, as in the case of levodopa used in treating Parkinson’s disease, and in furnishing a protection mechanism against their potential adverse effects.

Zebrafish has in recent years emerged as a popular animal model for a wide range of studies [17�]. Its advantages, compared with mouse, rat, or other vertebrate animal models, include the small size, availability of a relatively large number of eggs, rapid external development of virtually transparent embryos, and short generation time. These unique characteristics make the zebrafish an excellent model for a systematic investigation of the developmental stage-dependent and cell type/tissue/organ-specific expression, as well as physiological involvement of the COMT particularly with regard to the homeostasis of catecholamines and catecholestrogens and the detoxification of xenobiotic catechols including catechol drugs. A prerequisite for using the zebrafish in these studies, however, is the identification and functional characterization of the zebrafish COMT.

We report in this communication the identification of a COMT from zebrafish. The enzymatic activities toward a variety of endogenous and xenobiotic catecholic compounds were examined. To gain insight into its involvement in vivo, the developmental stage-dependent expression of the zebrafish COMT was investigated.


Kahn, F. Man in Structure & Function (A. A. Knopf, 1943).

Crick, F. Central dogma of molecular biology. Nature 227, 561–563 (1970).

Johnson, C. H., Ivanisevic, J. & Siuzdak, G. Metabolomics: beyond biomarkers and towards mechanisms. Nat. Rev. Mol. Cell Biol. 17, 451–459 (2016).

Jacob, F. & Monod, J. Genetic regulatory mechanisms in the synthesis of proteins. J. Mol. Biol. 3, 318–356 (1961).

Yang, Q., Vijayakumar, A. & Kahn, B. B. Metabolites as regulators of insulin sensitivity and metabolism. Nat. Rev. Mol. Cell Biol. 19, 654–672 (2018).

Zoncu, R., Efeyan, A. & Sabatini, D. M. mTOR: from growth signal integration to cancer, diabetes and ageing. Nat. Rev. Mol. Cell Biol. 12, 21–35 (2011).

Katsyuba, E. & Auwerx, J. Modulating NAD + metabolism, from bench to bedside. EMBO J. 36, 2670–2683 (2017).

Magistretti, P. J. & Allaman, I. Lactate in the brain: from metabolic end-product to signalling molecule. Nat. Rev. Neurosci. 19, 235–249 (2018).

Liu, P.-S. et al. α-Ketoglutarate orchestrates macrophage activation through metabolic and epigenetic reprogramming. Nat. Immunol. 18, 985–994 (2017).

Knobloch, M. et al. Metabolic control of adult neural stem cell activity by Fasn-dependent lipogenesis. Nature 493, 226–230 (2013).

Branco Dos Santos, F. et al. Probing the genome-scale metabolic landscape of Bordetella pertussis, the causative agent of whooping cough. Appl. Environ. Microbiol. (2017). This study identifies nitrogen sinks, using metabolomics based on a computational prediction, with the aim of enhancing vaccine production.

Giera, M., Branco Dos Santos, F. & Siuzdak, G. Metabolite-induced protein expression guided by metabolomics and systems biology. Cell Metab. 27, 270–272 (2018).

Yang, M., Soga, T. & Pollard, P. J. Oncometabolites: linking altered metabolism with cancer. J. Clin. Invest. 123, 3652–3658 (2013).

Guijas, C., Montenegro-Burke, J. R., Warth, B., Spilker, M. E. & Siuzdak, G. Metabolomics activity screening for identifying metabolites that modulate phenotype. Nat. Biotechnol. 36, 316–320 (2018).

Metallo, C. M. & Vander Heiden, M. G. Understanding metabolic regulation and its influence on cell physiology. Mol. Cell 49, 388–398 (2013).

Rabinowitz, J. D. & Silhavy, T. J. Systems biology: metabolite turns master regulator. Nature 500, 283–284 (2013).

Choudhary, C., Weinert, B. T., Nishida, Y., Verdin, E. & Mann, M. The growing landscape of lysine acetylation links metabolism and cell signalling. Nat. Rev. Mol. Cell Biol. 15, 536–550 (2014).

Weinert, B. T. et al. Time-resolved analysis reveals rapid dynamics and broad scope of the CBP/p300 acetylome. Cell 174, 231–244 (2018).

Rana, M. S. et al. Fatty acyl recognition and transfer by an integral membrane S-acyltransferase. Science 359, eaao6326 (2018).

James, A. M. et al. The causes and consequences of nonenzymatic protein acylation. Trends Biochem. Sci. 43, 921–932 (2018).

Weinert, B. T., Moustafa, T., Iesmantavicius, V., Zechner, R. & Choudhary, C. Analysis of acetylation stoichiometry suggests that SIRT3 repairs nonenzymatic acetylation lesions. EMBO J. 34, 2620–2632 (2015).

Dennis, J. W. & Brewer, C. F. Density-dependent lectin-glycan interactions as a paradigm for conditional regulation by posttranslational modifications. Mol. Cell. Proteomics 12, 913–920 (2013).

Hart, G. W., Slawson, C., Ramirez-Correa, G. & Lagerlof, O. Cross talk between O-GlcNAcylation and phosphorylation: roles in signaling, transcription, and chronic disease. Annu. Rev. Biochem. 80, 825–858 (2011).

Mills, E. L. et al. Itaconate is an anti-inflammatory metabolite that activates Nrf2 via alkylation of KEAP1. Nature 556, 113–117 (2018). This paper describes a new active metabolite, itaconate, that mediates inflammatory responses by protein modification.

Bambouskova, M. et al. Electrophilic properties of itaconate and derivatives regulate the IκBζ-ATF3 inflammatory axis. Nature 556, 556–504 (2018).

Cho-Park, P. F. & Steller, H. Proteasome regulation by ADP-ribosylation. Cell 153, 614–627 (2013).

Tan, M. et al. Lysine glutarylation is a protein posttranslational modification regulated by SIRT5. Cell Metab. 19, 605–617 (2014).

Masri, S. & Sassone-Corsi, P. The circadian clock: a framework linking metabolism, epigenetics and neuronal function. Nat. Rev. Neurosci. 14, 69–75 (2013).

Warth, B. et al. Metabolomics reveals that dietary xenoestrogens alter cellular metabolism induced by palbociclib/letrozole combination cancer therapy. Cell Chem. Biol. 25, 291–300 (2018).

Roadmap Epigenomics Consortium et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

Petkovich, D. A. et al. Using DNA methylation profiling to evaluate biological age and longevity interventions. Cell Metab. 25, 954–960 (2017).

Schaefer, M. et al. RNA methylation by Dnmt2 protects transfer RNAs against stress-induced cleavage. Genes Dev. 24, 1590–1595 (2010).

Helm, M. & Alfonzo, J. D. Posttranscriptional RNA modifications: playing metabolic games in a cell’s chemical Legoland. Chem. Biol. 21, 174–185 (2014).

Hu, X.-L., Wang, Y. & Shen, Q. Epigenetic control on cell fate choice in neural stem cells. Protein Cell 3, 278–290 (2012).

Watanabe, A., Yamada, Y. & Yamanaka, S. Epigenetic regulation in pluripotent stem cells: a key to breaking the epigenetic barrier. Phil. Trans. R. Soc B 368, 20120292 (2013).

Serganov, A. & Patel, D. J. Molecular recognition and function of riboswitches. Curr. Opin. Struct. Biol. 22, 279–286 (2012).

Husted, A. S., Trauelsen, M., Rudenko, O., Hjorth, S. A. & Schwartz, T. W. GPCR-mediated signaling of metabolites. Cell Metab. 25, 777–796 (2017).

Toma, I. et al. Succinate receptor GPR91 provides a direct link between high glucose levels and renin release in murine and rabbit kidney. J. Clin. Invest. 118, 2526–2534 (2008).

Syed, I. et al. Palmitic acid hydroxystearic acids activate GPR40, which is involved in their beneficial effects on glucose homeostasis. Cell Metab. 27, 419–427 (2018). This study shows how active metabolites, such as a novel identified class of endogenous lipids (PAHSAs), can signal to cells via GPCRs.

Yore, M. M. et al. Discovery of a class of endogenous mammalian lipids with anti-diabetic and anti-inflammatory effects. Cell 159, 318–332 (2014).

Jones, C. P. & Ferré-D’Amaré, A. R. Long-range interactions in riboswitch control of gene expression. Annu. Rev. Biophys. 46, 455–481 (2017).

Rajniak, J. et al. Biosynthesis of redox-active metabolites in response to iron deficiency in plants. Nat. Chem. Biol. 14, 442–450 (2018).

Steinbusch, L., Labouèbe, G. & Thorens, B. Brain glucose sensing in homeostatic and hedonic regulation. Trends Endocrinol. Metab. 26, 455–466 (2015).

Beyer, B. A. et al. Metabolomics-based discovery of a metabolite that enhances oligodendrocyte maturation. Nat. Chem. Biol. 14, 22–28 (2018). This study demonstrates the metabolomics-based identification of taurine as an enhancer of oligodendrocyte differentiation from stem cells.

Yang, M., Su, H., Soga, T., Kranc, K. R. & Pollard, P. J. Prolyl hydroxylase domain enzymes: important regulators of cancer metabolism. Hypoxia (Auckl.) 2, 127–142 (2014).

Xiao, M. et al. Inhibition of α-KG-dependent histone and DNA demethylases by fumarate and succinate that are accumulated in mutations of FH and SDH tumor suppressors. Genes Dev. 26, 1326–1338 (2012).

Adam, J. et al. Renal cyst formation in Fh1-deficient mice is independent of the Hif/Phd pathway: roles for fumarate in KEAP1 succination and Nrf2 signaling. Cancer Cell 20, 524–537 (2011).

Yang, M. et al. The succinated proteome of FH-mutant tumours. Metabolites 4, 640–654 (2014).

Zheng, L. et al. Fumarate induces redox-dependent senescence by modifying glutathione metabolism. Nat. Commun. 6, 6001 (2015).

McBrayer, S. K. et al. Transaminase inhibition by 2-hydroxyglutarate impairs glutamate biosynthesis and redox homeostasis in glioma. Cell 175, 101–116 (2018).

Wishart, D. S. Is cancer a genetic disease or a metabolic disease? EBioMedicine 2, 478–479 (2015).

Boroughs, L. K. & DeBerardinis, R. J. Metabolic pathways promoting cancer cell survival and growth. Nat. Cell Biol. 17, 351–359 (2015).

Lee, C. K., Klopp, R. G., Weindruch, R. & Prolla, T. A. Gene expression profile of aging and its retardation by caloric restriction. Science 285, 1390–1393 (1999).

Schvartzman, J. M., Thompson, C. B. & Finley, L. W. S. Metabolic regulation of chromatin modifications and gene expression. J. Cell Biol. 217, 2247–2259 (2018).

Jha, A. K. et al. Network integration of parallel metabolic and transcriptional data reveals metabolic modules that regulate macrophage polarization. Immunity 42, 419–430 (2015).

Piazza, I. et al. A map of protein-metabolite interactions reveals principles of chemical communication. Cell 172, 358–372 (2018). This paper presents a comprehensive study using structural proteomics and metabolomics to investigate the effects of small molecules on protein structure and complex assembly.

Li, X., Gianoulis, T. A., Yip, K. Y., Gerstein, M. & Snyder, M. Extensive in vivo metabolite-protein interactions revealed by large-scale systematic analyses. Cell 143, 639–650 (2010).

Alam, M. T. et al. The metabolic background is a global player in Saccharomyces gene expression epistasis. Nat. Microbiol. 1, 15030 (2016).

Buescher, J. M. et al. A roadmap for interpreting 13 C metabolite labeling patterns from cells. Curr. Opin. Biotechnol. 34, 189–201 (2015).

Jang, C., Chen, L. & Rabinowitz, J. D. Metabolomics and isotope tracing. Cell 173, 822–837 (2018). This review describes the state of the art of isotope tracing of metabolites.

Domingo-Almenara, X., Montenegro-Burke, J. R., Benton, H. P. & Siuzdak, G. Annotation: a computational solution for streamlining metabolomics analysis. Anal. Chem. 90, 480–489 (2018).

Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R. & Siuzdak, G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 78, 779–787 (2006).

Pluskal, T., Castillo, S., Villar-Briones, A. & Orešič, M. MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics 11, 395 (2010).

Pfeuffer, J. et al. OpenMS - a platform for reproducible analysis of mass spectrometry data. J. Biotechnol. 261, 142–148 (2017).

Tsugawa, H. et al. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat. Methods 12, 523–526 (2015).

Vinaixa, M. et al. Mass spectral databases for LC/MS- and GC/MS-based metabolomics: state of the field and future prospects. Trends Analyt. Chem. 78, 23–35 (2016).

Wishart, D. S. et al. HMDB 4.0: The Human Metabolome Database for 2018. Nucleic Acids Res. 46, D608–D617 (2018).

Wishart, D. S. et al. HMDB 3.0—The Human Metabolome Database in 2013. Nucleic Acids Res. 41, D801–D807 (2013).

Guijas, C. et al. METLIN: a technology platform for identifying knowns and unknowns. Anal. Chem. 90, 3156–3164 (2018).

Ludwig, C. et al. Birmingham Metabolite Library: a publicly accessible database of 1D 1H and 2D 1H J-resolved NMR spectra of authentic metabolite standards (BML-NMR). Metabolomics 8, 8–18 (2012).

King, Z. A. et al. BiGG models: a platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res. 44, D515–D522 (2016).

Horai, H. et al. MassBank: a public repository for sharing mass spectral data for life sciences. J. Mass Spectrom. 45, 703–714 (2010).

Fahy, E. et al. Update of the LIPID MAPS comprehensive classification system for lipids. J. Lipid Res. 50 (Suppl.), S9–S14 (2009).

Lai, Z. et al. Identifying metabolites by integrating metabolome databases with mass spectrometry cheminformatics. Nat. Methods 15, 53–56 (2018).

Hummel, J., Selbig, J., Walther, D. & Kopka, J. in Metabolomics: A Powerful Tool in Systems Biology (eds Nielsen, J. & Jewett, M. C.) 75–95 (Springer Berlin Heidelberg, 2007).

Vinaixa, M. et al. A guideline to univariate statistical analysis for LC/MS-based untargeted metabolomics-derived data. Metabolites 2, 775–795 (2012).

Huan, T. et al. Systems biology guided by XCMS Online metabolomics. Nat. Methods 14, 461–462 (2017).

Cottret, L. et al. MetExplore: collaborative edition and exploration of metabolic networks. Nucleic Acids Res. 46, W495–W502 (2018).

Boekel, J. et al. Multi-omic data analysis using Galaxy. Nat. Biotechnol. 33, 137–139 (2015).

Kuo, T.-C., Tian, T.-F. & Tseng, Y. J. 3Omics: a web-based systems biology tool for analysis, integration and visualization of human transcriptomic, proteomic and metabolomic data. BMC Syst. Biol. 7, 64 (2013).

Karnovsky, A. et al. Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data. Bioinformatics 28, 373–380 (2012).

Zhu, H. et al. Global analysis of protein activities using proteome chips. Science 293, 2101–2105 (2001).

Roelofs, K. G., Wang, J., Sintim, H. O. & Lee, V. T. Differential radial capillary action of ligand assay for high-throughput detection of protein-metabolite interactions. Proc. Natl Acad. Sci. USA 108, 15528–15533 (2011).

Savitski, M. M. et al. Tracking cancer drugs in living cells by thermal profiling of the proteome. Science 346, 1255784 (2014).

Diether, M. & Sauer, U. Towards detecting regulatory protein-metabolite interactions. Curr. Opin. Microbiol. 39, 16–23 (2017).

Tran, D. T., Adhikari, J. & Fitzgerald, M. C. StableIsotope labeling with amino acids in cell culture (SILAC)-based strategy for proteome-wide thermodynamic analysis of protein-ligand binding interactions. Mol. Cell. Proteomics 13, 1800–1813 (2014).

Feng, Y. et al. Global analysis of protein structural changes in complex proteomes. Nat. Biotechnol. 32, 1036–1044 (2014).

Gaulton, A. et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 40, D1100–D1107 (2012).

Kirchmair, J. et al. Predicting drug metabolism: experiment and/or computation? Nat. Rev. Drug Discov. 14, 387–404 (2015).

Warth, B. et al. Exposome-scale investigations guided by global metabolomics, pathway analysis, and cognitive computing. Anal. Chem. 89, 11505–11513 (2017).

Ge, H., Walhout, A. J. M. & Vidal, M. Integrating ‘omic’ information: a bridge between genomics and systems biology. Trends Genet. 19, 551–560 (2003).

Hakimi, A. A. et al. An integrated metabolic atlas of clear cell renal cell carcinoma. Cancer Cell 29, 104–116 (2016).

Davidson, R. L., Weber, R. J. M., Liu, H., Sharma-Oates, A. & Viant, M. R. Galaxy-M: a Galaxy workflow for processing and analyzing direct infusion and liquid chromatography mass spectrometry-based metabolomics data. GigaScience 5, 10 (2016).

Villiers, F. et al. Investigating the plant response to cadmium exposure by proteomic and metabolomic approaches. Proteomics 11, 1650–1663 (2011).

Zhang, W., Li, F. & Nie, L. Integrating multiple ‘omics’ analysis for microbial biology: application and methodologies. Microbiology 156, 287–301 (2010).

Haas, R. et al. Designing and interpreting ‘multi-omic’ experiments that may change our understanding of biology. Curr. Opin. Syst. Biol. 6, 37–45 (2017).

Yugi, K., Kubota, H., Hatano, A. & Kuroda, S. Trans-omics: how to reconstruct biochemical networks across multiple ‘omic’ layers. Trends Biotechnol. 34, 276–290 (2016).

Jewison, T. et al. SMPDB 2.0: big improvements to the small molecule pathway database. Nucleic Acids Res. 42, D478–D484 (2014).

Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res. 42, D459–D471 (2014).

Xia, J. & Wishart, D. S. MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data. Nucleic Acids Res. 38, W71–W77 (2010).

Fabregat, A. et al. The Reactome pathway knowledgebase. Nucleic Acids Res. 46, D649–D655 (2018).

Swainston, N. et al. Recon 2.2: from reconstruction to model of human metabolism. Metabolomics 12, 109 (2016).

Barupal, D. K. & Fiehn, O. Chemical similarity enrichment analysis (ChemRICH) as alternative to biochemical pathway mapping for metabolomic datasets. Sci. Rep. 7, 14567 (2017).

Kanehisa, M., Goto, S., Sato, Y., Furumichi, M. & Tanabe, M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40, D109–D114 (2012).

Haug, K. et al. MetaboLights — an open-access general-purpose repository for metabolomics studies and associated meta-data. Nucleic Acids Res. 41, D781–D786 (2013).

Sud, M. et al. Metabolomics Workbench: an international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools. Nucleic Acids Res. 44, D463–D470 (2016).

Knepper, M. A. Proteomic pearl diving versus systems biology in cell physiology. Focus on “Proteomic mapping of proteins released during necrosis and apoptosis from cultured neonatal cardiac myocytes”. Am. J. Physiol. Cell Physiol. 306, C634–C635 (2014).

Nielsen, J. & Keasling, J. D. Engineering cellular metabolism. Cell 164, 1185–1197 (2016).

Chassagnole, C., Noisommit-Rizzi, N., Schmid, J. W., Mauch, K. & Reuss, M. Dynamic modeling of the central carbon metabolism of Escherichia coli. Biotechnol. Bioeng. 79, 53–73 (2002).

Covert, M. W., Xiao, N., Chen, T. J. & Karr, J. R. Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli. Bioinformatics 24, 2044–2050 (2008).

Zelezniak, A. et al. Machine learning predicts the yeast metabolome from the quantitative proteome of kinase knockouts. Cell Syst. 7, 269–283 (2018). This study shows how the yeast metabolome can be predicted from omics data sets, showing the wide applicability of machine learning approaches in multi-omics integration.

Kim, M., Rai, N., Zorraquino, V. & Tagkopoulos, I. Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli. Nat. Commun. 7, 13090 (2016). This study shows how cellular behaviour can be computationally predicted by mathematical modelling combined with omics integration.

Frainay, C. et al. Mind the gap: mapping mass spectral databases in genome-scale metabolic networks reveals poorly covered areas. Metabolites 8, 51 (2018).

Chae, Y. K., Kim, S. H. & Markley, J. L. Relationship between recombinant protein expression and host metabolome as determined by two-dimensional NMR spectroscopy. PLOS ONE 12, e0177233 (2017).

Human Microbiome Project Consortium. A framework for human microbiome research. Nature 486, 215–221 (2012).

Corrêa-Oliveira, R., Fachi, J. L., Vieira, A., Sato, F. T. & Vinolo, M. A. R. Regulation of immune cell function by short-chain fatty acids. Clin. Transl Immunol. 5, e73 (2016).

Li, Z. et al. Butyrate reduces appetite and activates brown adipose tissue via the gut-brain neural circuit. Gut 67, 1269–1279 (2017).

Levy, B. D., Clish, C. B., Schmidt, B., Gronert, K. & Serhan, C. N. Lipid mediator class switching during acute inflammation: signals in resolution. Nat. Immunol. 2, 612–619 (2001).

Funk, C. D. Prostaglandins and leukotrienes: advances in eicosanoid biology. Science 294, 1871–1875 (2001).

Kalinski, P. Regulation of immune responses by prostaglandin E2. J. Immunol. 188, 21–28 (2012).

Kaisar, M. M. M. et al. Dectin-1/2-induced autocrine PGE2 signaling licenses dendritic cells to prime Th2 responses. PLOS Biol. 16, e2005504 (2018). This study shows, starting from lipidomic data, the identification of prostaglandin E2 as a key modulator of T helper 2 immune cell responses.

Lipworth, B. J. Leukotriene-receptor antagonists. Lancet 353, 57–62 (1999).

Veselinovic, M. et al. Clinical benefits of n-3 PUFA and γ-linolenic acid in patients with rheumatoid arthritis. Nutrients 9, 325 (2017).

Bhatt, D. L. et al. Cardiovascular risk reduction with icosapent ethyl for hypertriglyceridemia. N. Engl. J. Med. 380, 11–22 (2019).

Siscovick, D. S. et al. Omega-3 polyunsaturated fatty acid (fish oil) supplementation and the prevention of clinical cardiovascular disease: a science advisory from the American Heart Association. Circulation 135, e867–e884 (2017).

Kris-Etherton, P. M., Harris, W. S. & Appel, L. J., American Heart Association Nutrition Committee. Fish consumption, fish oil, omega-3 fatty acids, and cardiovascular disease. Circulation 106, 2747–2757 (2002).

Hur, J. et al. Cerebrovascular β-amyloid deposition and associated microhemorrhages in a Tg2576 Alzheimer mouse model are reduced with a DHA-enriched diet. FASEB J. 32, 4972–4983 (2018).

Grandison, R. C., Piper, M. D. W. & Partridge, L. Amino-acid imbalance explains extension of lifespan by dietary restriction in Drosophila. Nature 462, 1061–1064 (2009).

Denzel, M. S. et al. Hexosamine pathway metabolites enhance protein quality control and prolong life. Cell 156, 1167–1178 (2014).

Serhan, C. N. Treating inflammation and infection in the 21st century: new hints from decoding resolution mediators and mechanisms. FASEB J. 31, 1273–1288 (2017).

Niihara, Y. et al. A phase 3 trial of l-glutamine in sickle cell disease. N. Engl. J. Med. 379, 226–235 (2018). This study shows that metabolic interventions with active metabolites can potentially have a large impact on human disease.

Morris, C. R. et al. Erythrocyte glutamine depletion, altered redox environment, and pulmonary hypertension in sickle cell disease. Blood 111, 402–410 (2008).

Doucette, C. D., Schwab, D. J., Wingreen, N. S. & Rabinowitz, J. D. α-Ketoglutarate coordinates carbon and nitrogen utilization via enzyme I inhibition. Nat. Chem. Biol. 7, 894–901 (2011).

Chin, R. M. et al. The metabolite α-ketoglutarate extends lifespan by inhibiting ATP synthase and TOR. Nature 510, 397–401 (2014).

Carey, B. W., Finley, L. W. S., Cross, J. R., Allis, C. D. & Thompson, C. B. Intracellular α-ketoglutarate maintains the pluripotency of embryonic stem cells. Nature 518, 413–416 (2015).

Klysz, D. et al. Glutamine-dependent α-ketoglutarate production regulates the balance between T helper 1 cell and regulatory T cell generation. Sci. Signal. 8, ra97 (2015).

Grimm, P. R. et al. Integrated compensatory network is activated in the absence of NCC phosphorylation. J. Clin. Invest. 125, 2136–2150 (2015).

Grimm, P. R. & Welling, P. A. α-Ketoglutarate drives electroneutral NaCl reabsorption in intercalated cells by activating a G-protein coupled receptor, Oxgr1. Curr. Opin. Nephrol. Hypertens. 26, 426–433 (2017).

Coudray-Lucas, C., Le Bever, H., Cynober, L., De Bandt, J. P. & Carsin, H. Ornithine alpha-ketoglutarate improves wound healing in severe burn patients: a prospective randomized double-blind trial versus isonitrogenous controls. Crit. Care Med. 28, 1772–1776 (2000).

Patti, G. J. et al. Meta-analysis of global metabolomic data identifies metabolites associated with life-span extension. Metabolomics 10, 737–743 (2014).

Kamburov, A., Cavill, R., Ebbels, T. M. D., Herwig, R. & Keun, H. C. Integrated pathway-level analysis of transcriptomics and metabolomics data with IMPaLA. Bioinformatics 27, 2917–2918 (2011).

Peters, K. et al. PhenoMeNal: processing and analysis of metabolomics data in the cloud. Preprint at bioRxiv (2018).

Cravatt, B. F. et al. Chemical characterization of a family of brain lipids that induce sleep. Science 268, 1506–1509 (1995).

Boneschansker, L., Yan, J., Wong, E., Briscoe, D. M. & Irimia, D. Microfluidic platform for the quantitative analysis of leukocyte migration signatures. Nat. Commun. 5, 4787 (2014).

Laan, L. C. et al. The whipworm (Trichuris suis) secretes prostaglandin E2 to suppress proinflammatory properties in human dendritic cells. FASEB J. 31, 719–731 (2016).

Shi, S.-Y. et al. Coupling HPLC to on-line, post-column (bio)chemical assays for high-resolution screening of bioactive compounds from complex mixtures. Trends Analyt. Chem. 28, 865–877 (2009).

Tammela, P., Wennberg, T., Vuorela, H. & Vuorela, P. HPLC micro-fractionation coupled to a cell-based assay for automated on-line primary screening of calcium antagonistic components in plant extracts. Anal. Bioanal. Chem. 380, 614–618 (2004).

Veyel, D. et al. PROMIS, global analysis of PROtein–metabolite interactions using size separation in Arabidopsis thaliana. J. Biol. Chem. 293, 12440–12453 (2018).

Annis, D. A., Nickbarg, E., Yang, X., Ziebell, M. R. & Whitehurst, C. E. Affinity selection-mass spectrometry screening techniques for small molecule drug discovery. Curr. Opin. Chem. Biol. 11, 518–526 (2007).

Huber, K. V. M. et al. Proteome-wide drug and metabolite interaction mapping by thermal-stability profiling. Nat. Methods 12, 1055–1057 (2015).

Sergushichev, A. A. et al. GAM: a web-service for integrated transcriptional and metabolic network analysis. Nucleic Acids Res. 44, W194–W200 (2016).

Orth, J. D., Thiele, I. & Palsson, B. Ø. What is flux balance analysis? Nat. Biotechnol. 28, 245–248 (2010).

Harcombe, W. R. et al. Metabolic resource allocation in individual microbes determines ecosystem interactions and spatial dynamics. Cell Rep. 7, 1104–1115 (2014).

Li, S. et al. Predicting network activity from high throughput metabolomics. PLOS Comput. Biol. 9, e1003123 (2013).

Pirhaji, L. et al. Revealing disease-associated pathways by network integration of untargeted metabolomics. Nat. Methods 13, 770–776 (2016).

Olivon, F. et al. Bioactive natural products prioritization using massive multi-informational molecular networks. ACS Chem. Biol. 12, 2644–2651 (2017).

Péresse, T. et al. Cytotoxic prenylated stilbenes isolated from Macaranga tanarius. J. Nat. Prod. 80, 2684–2691 (2017).


Two transcripts coding for an adenosine deaminase (ADA) were identified by sequencing a Phlebotomus duboscqi salivary gland cDNA library. Adenosine deaminase was previously reported in the saliva of the sand fly Lutzomyia longipalpis but it was not present in the saliva of the sand flies Phlebotomus papatasi, P. argentipes, P. perniciosus and P. ariasi, suggesting that this enzyme is only present in the saliva of sand flies from the genus Lutzomyia. In the present work, we tested the hypothesis that the salivary gland transcript coding for ADA in Phlebotomus duboscqi, a sister species of Phlebotomus papatasi, produces an active salivary ADA.

Salivary gland homogenates of P. duboscqi converted adenosine to inosine, suggesting the presence of ADA activity in the saliva of this species of sand fly furthermore, this enzymatic activity was significantly reduced when using either salivary glands of recently blood-fed sand flies or punctured salivary glands, suggesting that this enzyme is secreted in the saliva of this insect. This enzymatic activity was absent from the saliva of P. papatasi. In contrast to other Phlebotomus sand flies, we did not find AMP or adenosine in P. duboscqi salivary glands as measured by HPLC-photodiode array. To confirm that the transcript coding for ADA was responsible for the activity observed in the saliva of this sand fly,we cloned this transcript into a prokaryotic expression vector and produced a soluble and active recombinant protein of approximately 60 kDa that was able to convert adenosine to inosine. Extracts of bacteria transformed with control plasmids did not show this activity. These results suggest that P. duboscqi transcripts coding for ADA are responsible for the activity detected in the salivary glands of this sand fly and that P. duboscqiacquired this activity independently from other Phlebotomus sand flies. This is another example of a gene recruitment event in salivary genes of blood-feeding arthropods that may be relevant for blood feeding and,because of the role of ADA in immunity, it may also play a role in parasite transmission.

Isolation, Purification, and Characterization of Xylanase Produced by a New Species of Bacillus in Solid State Fermentation

A thermoalkalophilic new species of Bacillus, similar to Bacillus arseniciselenatis DSM 15340, produced extracellular xylanase under solid state fermentation when wheat bran is used as carbon source. The extracellular xylanase was isolated by ammonium sulfate (80%) precipitation and purified using ion exchange chromatography. The molecular weight of xylanase was

29.8 kDa. The optimum temperature and pH for the enzyme activity were 50°C and pH 8.0. The enzyme was active on birchwood xylan and little active on p-nitrophenyl xylopyranoside but not on Avicel, CMC, cellobiose, and starch, showing its absolute substrate specificity. For birchwood xylan, the enzyme gave a Km 5.26 mg/mL and Vmax 277.7 μmol/min/mg, respectively. In addition, the xylanase was also capable of producing high-quality xylo-oligosaccharides, which indicated its application potential not only in pulp biobleaching processes but also in the nutraceutical industry.

1. Introduction

Xylan is the most abundant noncellulosic polysaccharide present in both hardwoods and annual plants and accounts for 20–35% of the total dry weight in tropical plant biomass [1–3]. In temperate softwoods, xylans are less abundant and may comprise about 8% of the total dry weight [4]. Xylan is found mainly in the secondary cell wall and is considered to be forming an interphase between lignin and other polysaccharides. It is likely that xylan molecules covalently link with lignin phenolic residues and also interact with polysaccharides, such as pectin and glucan. In simplest forms, xylans are linear homopolymers that contain D-xylose monomers linked through β-1, 4–glycosyl bonds [5, 6]. Xylanase (E.C degrades β-1, 4 xylan by cleaving β-1, 4 glycosidic linkages randomly, and the products are xylose and xylo-oligosaccharides like xylobiose [7, 8]. Xylanases are of industrial importance, which can be used in paper manufacturing to bleach paper pulp, increasing the brightness of pulp and improving the digestibility of animal feed and for clarification of fruit juices. Applications of xylanase avoid the use of chemicals that are expensive and cause pollution [9]. Microorganisms are the rich sources of xylanases, produced by diverse genera and species of bacteria, actinomycetes, and fungi. Several species of Bacillus and filamentous fungi secrete high amounts of extracellular xylanases [10]. Xylanase secretion often associates with low or high amount of cellulases. To use xylanase for pulp treatment, it is preferable to use cellulose-free xylanases, since the cellulase may adversely affect the quality of the paper pulp [11–15]. The most practical approach is the screening for naturally occurring microbial strains capable of secreting cellulose-free xylanases under optimized fermentation conditions. To use xylanase prominently in bleaching process it should be stable at high temperature and alkaline pH [16, 17].

Industrial production of enzymes on large scale is associated mainly with substrate. The use of agriculture residues as low-cost substrates for the production of industrial enzymes is a significant way to reduce production cost. The technique of fermentation using solid state substrate has the great advantage over submerged fermentation due to absence or near absence of aqueous phase that provides natural habitat for growth of microorganisms, economy of the space, simplicity of the media, no complex machinery, equipments and control systems, greater compactness of the fermentation vessel owing to a lower water volume, greater product yields, reduced energy demand, lower capital and recurring expenditures in industry, easier scale-up of processes, lesser volume of solvent needed for product recovery, superior yields, absence of foam build-up, and easier control of contamination due to the low moisture level in the system [10, 18]. In consideration with these facts the present study aims to characterize extracellular alkalothermophilic xylanase produced by Bacillus arseniciselenatis DSM 15340 when grown in solid state fermentation. To our knowledge, this is the first report describing the production of thermoalkalophilic cellulase-free xylanase by Bacillus arseniciselenatis DSM 15340. In addition, this xylanase was found to be able to degrade xylan into xylo-oligosaccharides.

2. Materials and Methods

2.1. Screening of Xylanolytic Strains

Soil samples were collected from coastal areas of Mandovi, Goa, India. Enrichment was done using birchwood xylan (Sigma Chemicals, Germany) as a sole source of carbon. Twenty five bacterial cultures were screened for xylanolytic ability by adding dye-labelled substrate, for example, xylan-brilliant red 3BA in xylan agar medium [19].

2.2. Phenotypic Characteristics

Prominent selected isolate was identified on the basis of morphological, cultural, biochemical properties [20] and 16S rRNA sequencing. Culture was deposited at National Centre for Cell Sciences (NCCS), Pune, India.

2.3. Phylogenic Analysis

The partial 16S rRNA sequences were retrieved on NCBI server ( using BLAST tool. Top 10 similar sequences were downloaded in FASTA format. Multiple alignment of sequences and calculations of levels of sequence similarity were performed by using ClustalW2 program. A phylogenetic tree obtained was analyzed for closely related organism. The evolutionary history was inferred using the neighbor-joining method [14].

2.4. Growth Conditions of Culture

The bacterial isolate was maintained in liquid medium as well as solid medium in basal salt solution (BSS) containing 0.5% xylan having pH 8.0 at 45°C and stored at 4°C.

2.5. Xylanase Production in Solid State Fermentation (SSF)

The selected strain was further tested for their abilities to produce extracellular xylanase under solid state fermentation. Wheat bran was used as the substrate. For this the strain was cultured in Erlenmeyer flasks (250 mL) containing 10 g of wheat bran moistened with 18 mL of the basal salt solution (BSS: substrate-to-moisture ratio 1 : 1. 8). After 48 h of fermentation spent solid substrate was removed and suspended in 50 mM phosphate buffer (pH 8.0), vortexed thoroughly to extract the xylanase. The sample was centrifuged at 5000 ×g for 10 minutes at 4°C. Centrifugation will remove xylanase from substrate. Supernatant was filtered through Whatman No. 1 filter paper and the clear filtrate was used as crude xylanase preparation. Prior to centrifugation, the samples were withdrawn for determining viable number of cells by standard viable plate count technique.

2.6. Xylanase Assay

Xylanase activity was measured according to Bailey et al. [21]. A 900 μL of 1% solubilised birchwood xylan solution was added with 100 μL enzyme solution in a test tube. 1.5 mL DNS reagent was added and incubated at 50°C for 5 min in water bath [22]. The absorbance was measured at 540 nm. The reaction was terminated at zero time in the control tubes. The standard graph was prepared using 0–500 μg xylose. An autozero was set in UV-VIS spectrophotometer (Hitachi, Japan) using buffer solution. One unit of xylanase activity was defined as the amount of enzyme that liberates 1 micromole of reducing sugars equivalent to xylose per minute under the assay conditions described. Solubilised xylan was prepared by stirring birchwood xylan with 1 M NaOH for six hours at room temperature followed by centrifugation and freeze drying the supernatant after neutralising the alkali with 1 M HCl.

2.7. Cellulase Assay

Cellulase activity was measured according to Ghose with necessary modifications [23]. A 900 μL 1% carboxy methyl cellulose solution was added with 100 μL enzyme in a test tube. 1.5 mL DNS reagent was added and incubated at 50°C for 5 min in water bath. The absorbance was measured at 540 nm. The reaction was terminated at zero time in control tubes. A standard graph was prepared using 0–500 μg glucose. An autozero was set in spectrophotometer using buffer solution. One unit of cellulase activity was defined as the amount of enzyme that liberates 1 micromole of glucose equivalents per minute under the assay conditions.

2.8. 1,4-β-xylosidase Assay

1,4-β-xylosidase activity was measured according to Lachke [24]. A 900 μL p-nitrophenyl β-xyloside (ρ-NPX) solution was added with 100 μL of appropriately diluted enzyme solution in a test tube. The mixture was incubated at 50°C for 30 min. Then 1 mL of 2 M sodium carbonate solution was added. The absorbance was measured at 410 nm. The reaction was terminated at zero time in control tubes. One unit of 1,4-β-xylosidase activity was defined as the amount of enzyme that catalyzes the formation of 1 micromole of ρ-nitrophenol per minute under assay conditions.

2.9. Determination of Total Protein Content

Total soluble protein was measured according to Lowry et al. [25]. Protein concentration was determined using bovine serum albumin (BSA) as a standard. The protein content of the chromatographic eluant was measured by monitoring the optical density at 280 nm.

2.10. Ammonium Sulphate Precipitation

Protein precipitation by salting out technique using ammonium sulphate (NH4(SO4)2) was carried out with constant gentle stirring [26]. This was left overnight and the precipitate was collected by centrifugation at 10,000 ×g for 10 min. The precipitate obtained was dissolved in phosphate buffer (50 mM, pH 8.0) and dialyzed against the same buffer for 24 h. Dialysis was carried out using cellulose tubing (molecular weight cut-off 13,000 kDa, Himedia LA393-10 MT).

2.11. Ion Exchange Chromatography

Dialyzed enzyme (2 mL) was loaded onto a anion exchange DEAE Cellulose (Sigma-Aldrich Co., USA) column. The column was packed with activated DEAE-cellulose equilibrated with 50 mM phosphate buffer (pH 8.0). The height of column was 20 cm with the 2.5 cm diameter. The protein was eluted with the 0.0 to 0.5 M NaCl gradient. The 50 fractions were collected having 5 mL volume of each fraction with the flow rate of 1 mL/min. All the steps were carried out at 4 to 8°C.

2.12. Molecular Mass Determination by SDS-PAGE

SDS-PAGE of partially purified xylanase was performed in a 12.5% acrylamide gel Laemmli [27]. Coomassie brilliant blue R-250 was used to stain the gel. The protein molecular weight markers used were of medium range containing 14.4 kDa to 94.0 kDa obtained from Bangalore GeNei, India.

2.13. Substrate Specificity

Substrate specificity of the xylanase was found by using 1% xylan, cellobiose, starch, carboxy methyl cellulose (CMC), and p-nitrophenyl xylopyranoside and Avicel as substrates.

2.14. Kinetic Parameters

Initial reaction rates using birchwood and oat spelt xylan as substrate were determined at substrate concentrations of 0.5–10 mg/mL in 50 mM phosphate buffer (pH 7.0) at 45°C. The kinetic constants, Km and Vmax, were estimated using the linear regression method of Lineweaver and Burk [28].

2.15. Identification of Hydrolysis Products

To 50 mL of birchwood xylan suspension (1% of birchwood xylan in 50 mM Phosphate buffer pH 7.0), 40 μg of xylanase enzyme was added and incubated at 45°C. Hydrolysis products were detected by thin layer chromatography (TLC) [29]. TLC (TLC plates, 0.25 mm layers of silica gel F 254, Merck, India) was performed using the mixture of n-butanol : ethanol : H2O (5 : 3 : 2 by vol) as a solvent system. Compounds were detected by spraying with 50% sulphuric acid in ethanol followed by heating at 150°C for 5 min. D-xylose (X1), xylobiose (X2), xylotriose (X3), and xylotetraose (X4) were applied as standard.

2.16. Effect of Temperature on Activity and Stability

The optimum temperature for maximum xylanase activity was determined by varying the reaction temperature from 30 to 80°C. To evaluate thermal stability, 0.5 mL of the enzyme solution was incubated at 30–80°C temperatures for up to 4 h. The relative enzyme activity was recorded at 1 h interval during period of 4 h.

2.17. Effect of pH on Activity and Stability

The effect of pH on enzyme activity was determined by incubating xylanase at various pH ranging from 6.0 to 11.0. The various buffers used were 50 mM sodium phosphate (pH 6, 7), 50 mM Tris HCl (pH 8, 9), 50 mM carbonate bicarbonate buffer (pH 10), and 50 mM glycine-NaOH buffer (pH 11). To evaluate the stability of the enzyme at each pH, the purified enzyme was incubated into the respective buffer over a pH range of 6.0–11.0 for up to 4 h at optimum temperature. The relative enzyme activity was determined at 1 h interval during the 4 h period of incubation.

3. Results and Discussion

3.1. Isolation and Identification of Bacteria

About 25 bacterial strains, which formed clear halos around their colonies on xylan agar plates, were picked up for further studies, isolated from soil collected at selected study site. The strain that showed 33 mm zone of clearance around the colony proved its xylanolytic ability (Figure 1). It was identified on the basis of various morphological and biochemical characteristics as shown in Table 1.

Watch the video: Μηχανισμός Ειδικής Άμυνας (August 2022).