Information

Why was Achromobacter xerosis removed from the NCBI taxonomy?


The Global Catalogue of Microorganisms lists a bacterium called Achromobacter xerosis which is mentioned in several papers and patents. It once existed in the NCBI taxonomy database, with ID 216898. However, it is no longer there - going to https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?id=216898 just yields a "No result found" page.

Why was this species removed from the NCBI's taxonomy? I haven't managed to find any documentation on why the NCBI would purge a species from their database, nor can I see any obvious reason to do so.


For whatever it's worth, I queried NCBI support about this and got a reply:

To: [email protected]
Subject: Reason for removal of a species from the taxonomy?

Hi,

I notice that the species Achromobacter xerosis used to be listed in the NCBI taxonomy at https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?id=216898 but no longer is. I haven't been able to track down any documentation hinting at why a species would ever be removed from the taxonomy. Is there any such documentation that I've missed, and are you able to explain why this species in particular was removed?

Thanks in advance for your time,
Mark

Reply:

Dear Mark Amery:
The NCBI Taxonomy database records exist to support data that are in other NCBI databases. A review of the Taxonomy database must have identified that we have no records for which Achromobacter xerosis is designated as the organism name.

regards,
Bonnie L. Maidak, Ph.D.
NCBI Help Desk
DHHS/NIH/NLM/NCBI


Achromobacter xerosis was proposed in 1954 by Groupé et al. Currently, the genus Achromobacter (revived by Yabuuchi & Yano, 1981) comprises 21 species, but A. xerosis is not among them. It seems that A. xerosis has never been validly published.


Nitrate Reducing Bacterium

All samples for determination of nitrate must be kept as sterile as possible, because the presence of nitrate-reducing bacteria or moulds can result in the rapid conversion of large amounts of nitrate to other nitrogen-containing compounds before the start of the assay. Normally deproteinization of biological samples is not necessary. Samples with high salt concentration must be diluted to allow full activity of the FNR. Samples which are strongly acid or alkaline must be neutralized before the addition of the enzyme.

Stability of sample: Samples should be analysed as soon as possible. If storage is unavoidable this should be in the dark at 0–5 °C under aseptic conditions.


S. epidermidis – the species

Staphylococci are common bacterial colonizers of the skin and mucous membranes of humans and other mammals 4 . S. epidermidis in particular is the most frequently isolated species from human epithelia. It colonizes predominantly the axillae, head, and nares 5 . Analysis of the S. epidermidis genome indicated that the species is well equipped with genes assumed to provide protection from the harsh conditions encountered in its natural habitat 9 , 10 . For example, to cope with extremes of salt concentration and osmotic pressure, S. epidermidis has eight sodium ion/proton exchangers and six transport systems for osmoprotectants 9 .

S. epidermidis belongs to the group of coagulase-negative staphylococci (CoNS), which is distinguished from coagulase-positive staphylococci such as S. aureus by lacking the enzyme coagulase. The species shows a high degree of diversity with 74 identified sequence types (STs) 6 . Most isolates belong to clonal complex (CC) 2, which comprises the most frequently isolated ST2. Possibly, the successful spread of ST2 may be due to the fact that all ST2 isolates contain IS256 insertion sequences and ica genes 7 , two factors found correlated with S. epidermidis invasiveness 13 – 16 . In addition, most ST2 isolates show in vitro capacity to form biofilms 7 . Genome information is available for two strains of S. epidermidis: the biofilm-negative ATCC12228 8 and the biofilm-positive clinical isolate RP62A 9 . Of note, no genome sequence is available yet for an isolate of the most frequently found and potentially most invasive ST2.


2. ACETYLCHOLINESTERASE INHIBITORS

AChE inhibitors or anti-cholinesterases inhibit the cholinesterase enzyme from breaking down ACh, increasing both the level and duration of the neurotransmitter action. According to the mode of action, AChE inhibitors can be divided into two groups: irreversible and reversible. Reversible inhibitors, competitive or noncompetitive, mostly have therapeutic applications, while toxic effects are associated with irreversible AChE activity modulators.

2.1. Reversible Acetylcholinesterase Inhibitors

Reversible AChE inhibitors play an important role in pharmacological manipulation of the enzyme activity. These inhibitors include compounds with different functional groups (carbamate, quaternary or tertiary ammonium group), and have been applied in the diagnostic and/or treatment of various diseases such as: myasthenia gravis, AD, post-operative ileus, bladder distention, glaucoma, as well as antidote to anticholinergic overdose.

2.1.1. Reversible Acetylcholinesterase Inhibitors in Alzheimer’s Disease Treatment

AD is a progressive neurological disorder, the most common form of dementia, characterized by memory loss and other intellectual abilities serious enough to interfere with daily life [25]. The disease is associated with loss of cholinergic neurons in the brain and the decreased level of ACh [26]. The major therapeutic target in the AD treatment strategies is the inhibition of brain AChE [26, 27]. There is no cure for AD, and reversible AChE inhibitors, employed in the therapy, treat symptoms related to memory, thinking, language, judgment and other thought processes. Actually, different physiological processes related to AD damage or destroy cells that produce and use ACh, thereby reducing the amount available to deliver messages to other cells. Cholinesterase inhibitor drugs, inhibiting AChE activity, maintain ACh level by decreasing its breakdown rate. Therefore, they boost cholinergic neurotransmission in forebrain regions and compensate for the loss of functioning brain cells. No drug has an indication for delaying or halting the progression of the disease [28]. Medications currently approved by regulatory agencies such as the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) to treat the cognitive manifestations of AD and improve life quality of the patients are: donepezil, rivastigmine and galantamine as reversible AChE inhibitors, and memantine as an NMDA receptor antagonist [29, 30, 31]. Tacrine was the first of the AChE inhibitors approved for the AD treatment in 1993, but its use has been abandoned because of a high incidence of side effects including hepatotoxicity [32, 33].

Donepezil

(Fig. ​ 4 4 ) is a selective, reversible AChE inhibitor that binds to the peripheral anionic site exerting not only symptomatic effects in the AD treatment, but also causative ones delaying the deposition of amyloid plaque [34, 35]. The drug is produced by pharmaceutical companies Eisai and Pfizer under the trade name Aricept. Although its principal therapeutic use is in the palliative treatment of mild to moderate AD, some clinical studies state that donepezil improves cognitive function in patients with severe AD symptoms as well [36]. It is available as disintegrating tablet and oral solution, being 100% oral bioavailability with ease crossing the blood-brain barrier and slow excretion. As it has a half-life of about 70 hours, it can be taken once a day. The drug is available in 5 and 10 mg dose strengths, and treatment is usually initiated at 5 mg per day, and increased after several weeks to 10 mg per day. Maximum daily dose is 23 mg once daily [37]. Patients receiving the higher dose showed mild improvement in cognitive functions, and no improvement on overall functioning. On the other hand, the higher drug dose induced the increased incidence of cholinergic side effects in patients, which limited its wider use [38]. Common donepezil adverse effects include gastrointestinal anomalies-nausea, diarrhea, anorexia, abdominal pain, as well as increase in cardiac vagal tone causing bradycardia [39]. Additionally, recent studies have suggested donepezil ability to improve speech in children with autism, while its indication in other cognitive disorders such as Lewy body dementia, schizophrenia and vascular dementia is not currently approved [40-42].

Selected reversible AChE inhibitors in pharmacotherapy of AD.

Rivastigmine

(Fig. ​ 4 4 ) (sold under the trade name Exelon) is a powerful, slow-reversible carbamate inhibitor that blocks cholinesterase activity through binding at the esteratic part of the active site. Unlike donepezil that selectively inhibits AChE, rivastigmine inhibits both BuChE and AChE. It has received approval for the treatment of mild-to-moderate AD in 60 countries including all member states of the European Union and the USA [43]. The drug is administered orally as capsules or liquid formulations, with good absorption and bioavailability of about 40% in the 3 mg dose. It is eliminated through the urine and has relatively few drug-drug interactions. The treatment is initiated at 1.5 mg twice daily, and is increased gradually over weeks to 6 mg twice daily the increment is 3 mg per day every 2 to 4 weeks. Early and continued treatment of AD with rivastigmine maximizes the observed beneficial effects in the rate of decline of cognitive function, activities of daily living, and severity of dementia with daily doses of 6 to 12 mg. Adverse events are consistent with the cholinergic actions of the drug, and include nausea, vomiting, diarrhea, anorexia, headache, syncope, abdominal pain and dizziness [32, 44]. The side effects can be reduced using transdermal patch delivering rivastigmine. The target dose of 9.5 mg/day delivered by patch provides similar clinical effects (improved memory and thinking, activities of daily living, concentration) as the highest recommended doses of rivastigmine capsules, but with three times fewer reports of nausea and vomiting [45]. In addition to AD, rivastigmine can be applied in the treatment of Lewy bodies and Parkinson’s disease dementia [43, 46].

Galantamine

(trade name Razadyne, Nivalin) is an alkaloid (Fig. ​ 4 4 ) isolated from the plant Galanthus woronowii being applied for the treatment of mild to moderate AD. It is a selective, competitive, rapidly-reversible AChE inhibitor that interacts with the anionic subsite, as well as with the aromatic gorge [47-49]. Besides, the drug is an allosteric ligand at nicotinic cholinergic receptors inducing their modulation. It interacts with the nicotinic receptor at binding sites separate from those for ACh and nicotinic agonists, and acts specifically to enhance the activity (sensitize) of nicotinic receptors in the presence of ACh [50, 51]. As the severity of cognitive impairment in AD correlates with loss of nicotinic receptors, this effect appears to be beneficial for the disorder treatment [52]. Galantamine absorption is rapid and complete, with absolute oral bioavailability between 80 and 100% and seven hours half-life. The treatment is usually initiated at 4 mg twice daily, and can be increased gradually up to 12 mg twice daily [39]. The drug side effects are similar to those of other AChE inhibitors, mainly with gastrointestinal symptoms. Galantamine seems to be less tolerated compared with the other AD drugs. However, a careful and gradual titration over more than three months may improve long-term tolerability [29]. Since galantamine has allosteric potentiating effects at nicotinic receptors, it affects not only cholinergic transmission but also other neurotransmitter systems such as monoamines, glutamate, and γ-aminobutyric acid (GABA) through its allosteric mechanism. These effects may result in more beneficial effects, and improve cognitive dysfunction and psychiatric illness in schizophrenia, major depression, bipolar disorder and alcohol abuse [53].

Considering the clinical effects of donepezil, rivastigmine and galantamine, there is no evidence that any of these medications is superior in terms of efficacy. However, donepezil has been found to be better tolerated, with less gastrointestinal side effects than rivastigmine or galantamine [39]. Beside the described drugs approved by FDA and EMA for the AD symptomatic treatment, new AChE inhibitors have been synthesized and tested. So, the derivative of hepatotoxic tacrine (Fig. ​ 4 4 ) - the potent inhibitor of AChE anionic active site, 7-methoxytacrine was widely studied as a suitable substitute to tacrine. In vitro and in vivo tests indicated both its less toxic effects and stronger inactivating power against AChE related to tacrine [54]. Furthermore, natural alkaloid huperzine A (Fig. ​ 4 4 ) is originated from the firmoss Huperzia serrata, and can be synthesized as well. The target of this AChE inhibitor is the peripheral anionic site, which makes the AD drug able to affect the symptoms as well as the cause of the disorder [55, 49] (see about donepezil above). The drug is a more potent AChE inhibitor than tacrine, galantamine and rivastigmine, while donepezil exhibits higher anti-AChE activity. Compared to other AChE inhibitors, huperzine A demonstrated better penetration through blood-brain barrier, higher oral bioavailability and longer AChE inhibition. Clinical trials with this AChE inhibitor revealed cognitive and functional impairments at patients with AD, schizophrenia and vascular dementia, and memory improvement of elder people [56]. In addition, protoberbrine alkaloids (berberine, palmatine, jatrorrhizine, epiberberine), as natural robust AChE inhibitors, are contemplated promising symptomatic therapeutic agents for AD [57]. It is necessary to emphasize that the pharmacological profile of the eutomer (bioactive enantiomer or enantiomer having higher pharmacological activity) and distomer (opposite to eutomer) of the chiral drugs (e.g. donepezil, rivastigmine, galantamine) is differential.

Looking for potent and selective AChE inhibitors as potential anti-Alzheimer drugs, new compounds have recently been designed, synthesized and tested. Novel donepezil-tacrine and oxoisoaporphine-tacrine congeners hybrid related derivatives, coumarin and huperzine A derivatives have exhibited high AChE inhibitory activity with IC50 values in the nanomolar range, and ability to bind simultaneously to both peripheral and catalytic sites of the enzyme. For the reason, these dual binding site inhibitors are promising compounds for developing disease-modifying drugs for the future treatment of AD [58-62], Additionally, new synthesized symmetrical bispyridinium and carbamate anti-AChE compounds inhibit the enzyme in micromolar concentrations, making them the potential candidates for the treatment of AD [63, 64].

Generally, the disadvantage of the AChE inhibitors in AD treatment is modest and temporary benefits lasting for a maximum 12-24 months. Actually, these drugs do not reduce the rate of decline in cognitive or functional capacities over the long term [65, 66]. Despite this fact, reversible AChE inhibitors provide meaningful symptomatic benefits, thereby remaining the mainstay of pharmacotherapy in AD. Moreover, their use is standard and supported by evidence [39].

2.1.2. Carbamates

Carbamates are organic compounds derived from carbamic acid (NH2COOH). The structure of biologically active carbamates is displayed in Fig. ( ​ 5 5 ), where X can be oxygen or sulphur (thiocarbamate), R1 and R2 are usually organic or alkyl substituents, but R1 or R2 may also be hydrogen, and R3 is mostly an organic substituent or sometimes a metal. In addition to their use as therapeutic drugs in human medicine (AD, myasthenia gravis, glaucoma, Lewy bodies, Parkinson’s disease), these reversible AChE inhibitors have been applied as pesticides, then as parasiticides in veterinary medicine, and in prophylaxis of organophosphorus compounds (OPs) poisoning as well [67].

General chemical structure of biologically active carbamates.

Since carbamates, as well as OPs, are AChE inhibitors, both compounds cause similar toxic acute effects and symptoms derived from poisoning. The principal difference between OP and carbamate induced inhibitory action is in the stability of the AChE-OP/carbamate complex. Actually, OPs are able to phosphorylate serine residues of AChE in non-reversible way (Fig. ​ 6 6 ), whereas the carbamylated serine residue is less stable and the carbamyl moiety can be split from the enzyme by spontaneous hydrolysis (decarbamylation time is 30-40 minutes) [68, 69]. Therefore, carbamates are considered reversible AChE inhibitors. Furthermore, carbamates, analogously to OPs, reversibly inhibit neuropathy target esterase, but, unlike OPs, are not able to dealkylate i.e. age the inhibited enzyme. So, carbamates are not delayed neuropathy inducers (see more about OP induced neuropathy below) [70, 71]. Moreover, they exhibit protective effects when applied before OPs induced neuropathy. Thereupon, the neuropathic OPs are not able to inhibit and age neuropathy target esterase previously reversibly inhibited by carbamates. On the other hand, carbamates stimulate delayed neuropathy or make it more severe, when they are dosed after applying OP neuropathic doses, inducing promotion of delayed neuropathy [72].

Mechanism of AChE inhibition induced by OPs reactivation, spontaneous hydrolysis, and aging of the phosphorylated enzyme.

Carbamate compounds are applied as fungicides, insecticides and herbicides in agriculture, and belong to the second group of pesticides inhibiting cholinesterases. Carbamates containing hydrogen and methyl group in the place of R2 and R1 (Fig. ​ 5 5 ), respectively, exert the insecticide activity. Carbamate insecticides include aldicarb, carbofuran, carbaryl, fenobucarb, propoxur (Fig. ​ 7 7 ). Their insecticide killing action is based on reversible AChE inactivation. Carbamates are considered to be safer than OP insecticides that irreversibly inhibit AChE causing more severe cholinergic poisoning [67, 73-75]. It was found that stress conditions can improve carbamates diffusion into the central nervous system, while the blood brain barrier penetration in the healthy body is prevented [76].

Selected carbamate insecticides.

Some carbamate compounds are used as herbicides such as ferbam, mancozeb, thiram (Fig. ​ 8 8 ). In addition, carbamates exhibit fungicide activity as well – butylate, pebulate, metham, molinate, cycloate, vernolate (Fig. ​ 8 8 ). It is generally thought that their acute toxicity to humans is low, but they may irritate skin, eyes and throat causing sneezing and coughing [67].

Selected carbamates being applied as herbicides and fungicides.

Carbamates, due to their reversible AChE inhibitory action, found an important application in human medicine as pharmacologically active compounds. Natural carbamate derivate physostigmine (Fig. ​ 9 9 ), the secondary metabolite in the plant Physostigma venenosum, is widely used in the treatment of myasthenia gravis. As a potent AChE inhibitor, this therapeutic agent reduces ACh hydrolysis rate, and thereby increases its level in damaged neurosynaptic clefts improving nerve impulse transmission. Besides, pyridostigmine (Fig. ​ 9 9 ) is capable to prevent the irreversible binding of OP to AChE. Consequently, it is applied as a prophylactic against nerve agent intoxication [77-79]. Furthermore, rivastigmine (Fig. ​ 4 4 ) is a carbamate with probably the most meaningful pharmacological application, being validated in the symptomatic treatment of AD (see above).

Structural formula of physostigmine and pyridostigmine.

2.2. Irreversible Acetylcholinesterase Inhibitors – Organophosphorus Compounds

OPs are esters or thiols derived from phosphoric, phosphonic, phosphinic or phosphoramidic acid (Fig. ​ 10 10 ).

General structural formula of OPs.

R1 and R2 are aryl or alkyl groups that are bonded to the phosphorus atom either directly (forming phosphinates), or through an oxygen or sulphur atom (forming phosphates or phosphothioates). In some cases, R1 is directly bonded to the phosphorus atom, and R2 is bonded to an oxygen or sulphur atom (forming phosphonates or thiophosphonates). In phosphoramidates, at least one of these groups is –NH2 (un-, mono- or bi-substituted), and the atom double-bonded with phosphorus is either oxygen or sulphur. The –X group, also binding to the phosphorus atom through oxygen or sulphur atom, may belong to a wide range of halogen, aliphatic, aromatic or heterocyclic groups. This ″leaving group″ is released from the phosphorus atom when the OP is hydrolyzed by phosphotriesterases or upon interaction with protein targets. In medicine and agriculture, the word ″organophosphates″ refers to a group of insecticides and nerve agents that inhibit AChE [52, 71, 80].

The OPs exert their main toxicological effects through non-reversible phosphorylation of esterases in the central nervous system [81, 82]. The acute toxic effects are related to irreversible inactivation of AChE [82]. Actually, OPs are substrate analogues to ACh, and like natural substrate enter the active site covalently binding to serine –OH group. As in acetylation, OP is split and the enzyme is phosphorylated (Fig. ​ 6 6 ). While the acyl enzyme is quickly hydrolyzed to regenerate the free enzyme, dephosphorylation is very slow (on the order of days), and phosphorylated enzyme cannot hydrolyze the neurotransmitter [83]. The inhibition of the enzyme leads to accumulation of ACh in the synaptic cleft resulting in over-stimulation of nicotinic and muscarinic ACh receptors and impeded neurotransmission. The typical symptoms of acute poisoning are agitation, muscle weakness, muscle fasciculations, miosis, hypersalivation, sweating. Severe poisonings may cause respiratory failure, unconsciousness, confusion, convulsions and/or death [82, 84-86].

Mechanism of OPs induced AChE inhibition is presented using the reaction scheme:

where, E𠄾nzyme, PX–OP, E * PX–reversible enzyme-OP complex, EP–phosphorylated enzyme, X–OP leaving group [87].

Irreversible inhibition occurs in two steps the first one is fast, short term reversible enzyme inactivation, and its influence is dominant in the begining of the inhibition. The next step is slow irreversible inhibition producing a very stable enzyme-inhibitor complex (phosphorylated enzyme)-inhibitor is covalently bonded to the enzyme [88]. Time dependent irreversible inhibition can be described by the equation:

where, E/Eo–remaining enzyme activity related to initial enzyme activity (control) (Eo), KI𠄽issociation constant for enzyme-inhibitor complex E * PX, k3–the first rate constant for the conversion of the reversible enzyme-inhibitor complex to phosphorylated enzyme, EP, (I)–inhibitor (OP) concentration, t–time interval after the enzyme and inhibitor mixing. If (I) » (Eo), the reciprocal slope value of linear dependence ln(E/Eo) - t (Fig. 11a ) can be presented in the form:

(a). Progressive development of inhibition produced by reaction of AChE with different concentrations of diazoxon plotted as semi logarithmic curve in accordance with Equation (1). Diazoxon concentrations (in mol/l): (1) 2 × 10 -8 , (2) 3 × 10 -8 , (3) 5 × 10 -8 , (4) 7.5 × 10 -8 , (5) 1 × 10 -7 , and (6) 2 × 10 -7 . Reproduced from [90]. (b). The dependence of kapp upon the concentration of diazoxon (1), chlorpyrifos-oxon (2) and chlorpyrifos ((3), inset) plotted as reciprocals in accordance with Equation (2). Reproduced from [90].

The values of inhibition parameters, KI and k3, are calculated from the slope and intersection of 1/kapp - 1/(I) linear dependence (Fig. 11b ) [89,90].

Effective OPs have the following structural features: a terminal oxygen connected to phosphorus by a double bond (oxo form), two lipophilic groups (–R1, –R2) bonded to the phosphorus, and a good leaving group (–X) bonded to the phosphorus (Fig. ​ 10 10 ) [91].

OPs can produce delayed neurotoxic effect in humans and chickens, called OP induced delayed neuropathy. It is associated with phosphorylation and further dealkylation (aging) (Fig. ​ 6 6 ) of a protein in neurons called neuropathy target esterase, subsequently leading to this syndrome. The symptoms of this neuropathy are paralysis and ataxia, and appear between 14 and 24 days after the poisoning [67, 70, 71].

2.2.1. Organophosphorus Insecticides

The majority of OPs have been commonly used as nonspecific insecticides for over fifty years, to control a variety of insects in agriculture and the household environment. The synthesis of OP pesticides in large quantities started after World War II, and parathion was among the first marketed, followed by malathion and azinphosmethyl. Commonly used OP insecticides have included ethyl parathion, malathion, methyl parathion, chlorpyrifos, diazinon, dichlorvos, phosmet, fenitrothion, tetrachlorvinphos, azinphos methyl, pirimiphos-methyl, dimethoate, phosalone (Fig. ​ 12 12 ) [74, 75, 91-93]. In the 1970s organochlorine insecticides (DDT, dieldrin, aheptachlor) were banned because of their persistence and accumulation in the environment, and replaced by more degradable OPs.

Actually, OP insecticides in the environment undergo the natural degradation pathway including mainly homogeneous and heterogeneous hydrolysis (especially at high pH) enhanced by the presence of dissolved metals, humic substances, microorganisms and other compounds present in soil [94-96]. OP degradation processes also occur in chemical treatments for purification of polluted waters, generally referred as advanced oxidation processes, as well as throughout the enzymatic reactions in birds, fish, insects and mammals. Degradation studies revealed different kinetics, mechanisms and transformation products, suggesting complete mineralization of the starting compound (usually thio form), but forming toxic break down products as well [89, 97-100]. Actually, oxidation and isomerisation reaction products were reported as much more potent AChE inhibitors compared to the starting thio OPs, while hydrolysis products do not noticeably affect the enzyme activity. Inhibition parameters, IC50 and Ki, for diazinon, malathion, chlorpyrifos and their transformation products are given in Table ​ 1 1 , indicating even several hundred times lower IC50 values for oxo and iso forms related to the thio compounds, and non-inhibiting hydrolysis products [89, 90, 101].

Table 1

IC50 and KI Values for Irreversible Inhibition of AChE Activity by Diazinon, Chlorpyrifos, Malathion, and their Transformation Products

CompoundIC50 (20 min), mol/L [75]Ki, mol/L [75]CompoundIC50 (5 min), mol/L [86]Ki, mol/L [74]
Diazinon> 2.0 × 10 -4 / ** Malathion3.2 × 10 -5 1.3 × 10 -4
Diazoxon5.1 × 10 -8 7.9 × 10 -7 Malaoxon4.7 × 10 -7 5.6 × 10 -6
IMP * / *** / *** Isomalathion6.0 × 10 -7 7.2 × 10 -6
Chlorpyrifos4.3 × 10 -6 9.6 × 10 -6 Diethylmaleate6.0 × 10 -2 / ***
Chlorpyrifos-oxon3.0 × 10 -8 4.3 × 10 -7 O,O-dimethyl thiophosphate/ *** / ***
3,5,6,-trichloro-2-pyridinol/ *** / ***

Although OPs insecticides degrade rapidly, that made them an attractive alternative to the organochloride pesticides, they have greater acute toxicity, posing risks to people who may be exposed to large amounts - workers employed in the manufacture and application of these pesticides. OPs are one of the most common causes of poisoning worldwide occurring as a result of agricultural use, suicide or accidental exposure. OP pesticides can be absorbed by all routes, including inhalation, ingestion, and dermal absorption [102]. Their toxicity is not limited to the acute phase, but chronic effects have long been noted. Actually, repeated or prolonged exposure to OPs may result in the same effects as acute exposure including the delayed symptoms. The effects, reported in workers repeatedly exposed, include impaired memory and concentration, disorientation, severe depressions, irritability, confusion, headache, speech difficulties, delayed reaction times, nightmares, sleepwalking and drowsiness or insomnia. Influenza-like condition with headache, nausea, weakness, loss of appetite, and malaise has also been reported [103]. Neurotransmitters such as ACh are profoundly important in the brain's development, and many OPs have neurotoxic effects on developing organisms, even from low levels of exposure, causing various diseases of nervous and immune system [85, 104].

Oxo forms of OP insecticides,are highly, approximately equally toxic to warm-blooded as well as cold-blooded organisms. On the other hand, thio forms are converted into the oxo forms by mixed function oxidases. The activation proceeds in cold-blooded organisms but this is not common in warm-blooded organisms where dealkylation into non toxic compounds takes place [51, 105]. Thus, numerous derivatives of highly toxic insecticides have been prepared to reduce the toxicity towards warm-blooded organisms and retain toxicity to insects, thereby enhancing their specificity. The examples of effective, commonly used OP insecticides, and relative safe for warm-blooded organisms are: malathion, chlorpyrifos, fenitrothion, pirimiphos-methyl, dimethoate, phosalone [51].

Nowadays, the common use of OP insecticides results in their accumulation, environmental pollution and acute and chronic poisoning events [106]. For this reason, the use of OP insecticides has to be strictly controlled and restricted. Accordingly, the majority of countries have strong regulations on the application of pesticides e.g. in the European Union it is regulated by the directive 91/41/EHS [51]. Also, the applied insecticides and their by-products in the environment, water and food are monitored applying different bioanalytical techniques [107].

2.2.2. Organophosphorus Nerve Agents/Gases

Nerve agents of OP group include tabun, sarin, soman, cyclosarin and VX. Sarin, soman and cyclosarin are phosphonofluoridates, and VX is a phosphonothioate (Fig. ​ 13 13 ). Soman has four, while sarin and VX have two isoforms, which significantly differ in toxicity and irreversible AChE inactivation rate. Based on the acute toxicity, VX is the most toxic compound among all the nerve agents [67]. The developing and production of these extremely toxic nerve agents started in the 1930s, and later used in wars and by terrorists on several occasions. As chemical weapons, they are classified as weapons of mass destruction by the United Nations, and their production and stockpiling was outlawed by the Chemical Weapons Convention.

Acute poisoning by a nerve agent leads to contraction of pupils, profuse salivation, convulsions, involuntary urination and defecation, and eventual death by asphyxiation as control is lost over respiratory muscles. Some nerve agents are readily vaporized or aerosolized and the primary portal of entry into the body is the respiratory system. Nerve agents can also be absorbed through the skin, requiring that those exposed to such agents wear a full body suit in addition to a respirator [108]. Moreover, the effects of nerve agents are very long lasting and cumulative (increased by successive exposures), and survivors of nerve agent poisoning usually suffer chronic neurological damage that can lead to continuing psychiatric effects [109].

2.2.3. Irreversible Acetylcholinesterase Inhibitors as Therapeutic Agents

OPs, except their use as toxic compounds, have been applied in ophthalmology as therapeutic agents in the treatment of chronic glaucoma, an eye disease in which the optic nerve is damaged in a characteristic pattern. The disease is associated with increased fluid pressure in the eye, and can permanently damage vision in the affected eye(s) and lead to blindness if left untreated [110]. These medical useful OPs include diisopropyl fluorophosphate and echothiophate.

Diisopropyl fluorophosphate (DFP, DIFP, diisopropyl phosphorofluoridate) (Fig. ​ 14 14 ) is a parasympathomimetic drug, irreversible anti-cholinesterase, and has been used locally in the oily eye drops form as a miotic agent in the glaucoma treatment. It is known as fluostigmine and dyflos in such uses. It exerts ocular side effects mainly associated with its AChE inhibitory properties, and ability to induce delayed peripheral neuropathy [67, 111].

Pharmacologically important OPs.

Echothiophate (phospholine) (Fig. ​ 14 14 ) is a parasympathomimetic phosphorothioate, irreversible AChE inhibitor. It is used as an ocular antihypertensive in the treatment of chronic glaucoma and, in some cases, accommodative esotropia. Its application is local (eye drops), and the effects can last a week or more. The drug is available under several trade names such as phospholine iodide. Adverse effects include muscle spasm and other systemic effects [112].

OP compounds may be used in the therapy of neurological damages such as AD and Parkinson's disease. The example is trichlorfon (metrifonate) (Fig. ​ 14 14 ) that used to be applied as a pesticide, and has medicine implementation analogous to the carbamate rivastigmine (described above) [113].

The described reversible and irreversible AChE inhibitors are summarized in Table ​ 2 2 .

Table 2

Commonly used Reversible and Irreversible AChE Inhibitors and their Application

CompoundChemical StructureMode of AChE InhibitionApplication
Donepezil Piperidine derivative ReversibleAlzheimer's disease
Autism
Rivastigmine Carbamate ReversibleAlzheimer's disease
Lewy bodies
Parkinson's disease
Galantamine Alkaloid ReversibleAlzheimer's disease
Tacrine Pyridine derivative ReversibleAlzheimer's disease
7-methoxytacrine Pyridine derivative ReversibleAlzheimer's disease
Huperzine A Alkaloid ReversibleAlzheimer's disease
Aldicarb Carbamate Reversible Insecticide
Carbofuran
Carbofuran
Carbaryl
Propoxur
Ferbam Carbamate Reversible Herbicide
Mancozeb
Thiram
Butylate Carbamate Reversible Fungicide
Pebulate
Metham
Molinate
Cycloate
Vernolate
PhysostigmineCarbamateReversibleMyasthenia gravis
PyridostigmineCarbamateReversibleProphylactic against nerve agent intoxication
Ethyl parathion IrreversibleInsecticide
MalathionOrganophosphorus compound
Methyl parathion
Chlorpyrifos
Diazinon
Dichlorvos
Phosmet
Fenitrothion
Tetrachlorvinphos
Azinphos methyl
Pirimiphos methyl
Dimethoate
Phosalone
TabunOrganophosphorus compoundIrreversibleNerve agent
Sarin
Soman
Cyclosarin
VX
Diisopropyl FluorophosphateOrganophosphorus compoundIrreversibleGlaucoma
Diisopropyl FluorophosphateOrganophosphorus compoundIrreversibleGlaucoma
EchothiophateOrganophosphorus compoundIrreversibleGlaucoma
Accommodative esotropia
TrichlorfonOrganophosphorus compoundIrreversibleAlzheimer's disease
Parkinson’s disease

2.2.4. Nonspecific Toxic Effects of Organophosphates

The primary target of OP action is AChE, and the main mechanism of toxicity in acute OP exposure involves the specific irreversible inhibition of this enzyme activity in the nervous system and blood, manifesting as a cholinergic crisis with excessive glandular secretions and weakness, miosis and fasciculation of muscle, which may lead to death [106, 114, 115]. Additionally, many studies suggest that both acute and chronic intoxication disturb the redox processes changing the activities of antioxidative enzymes and causing enhancement of lipid peroxidation in many organs, and there is little correlation between organ damage and the degree of OP induced AChE inhibition [116-118]. Indeed, in acute, and rather subchronic or chronic OP exposition, induction of oxidative stress has been reported as the main mechanism of its toxicity [119]. Oxidative stress is defined as an imbalance between the production of free radicals – reactive oxygen species (ROS) and the antioxidant defense system – enzymatic and non-enzymatic. The ROS may be generated as the result of the metabolism of OPs by cytochrome P450s, monooxygenases that catalyze oxidation by addition of one atom of molecular oxygen into the substrate (OP) by electron transport pathway [120]. This disorder is manifested by changes in the activity of antioxidative enzymes (catalase, superoxide dismutase, glutathione peroxidase, glutathione reductase), increased malondialdehyde concentration and/or altered levels of non-enzymatic antioxidants (reduced glutathione, vitamin C, vitamin E, beta-carotene), as oxidative stress parameters and the marker of ROS increased level and lipid peroxidation [99, 121, 122]. There is some evidence that OPs may affect liver, kidney, muscles, immune, and hematological system, causing many human body disorders [123-125]. Also, some findings indicate oxidative stress as an important pathomechanism of neurological disorders such as AD and Parkinson's disease, as well as of cardiovascular diseases [122, 126, 127].

Furthermore, OP induced ROS attack lipids, proteins and DNA, causing oxidation and membrane damage, enzyme inactivation, DNA damage and cell death [128-130]. The highly reactive free radicals attack DNA resulting in single and double strand breaks, as well as oxidative damage to sugar and base residues that can later be converted to strand breaks [131]. On the other hand, phosphorus moiety in the OPs appears to be a good substrate for nucleophilic attack leading to phosphorylation of DNA which is an instance of DNA damage [132]. Some reported studies indicate the increase in chromosomal aberrations (CA), micronuclei (MN) and sister chromatid exchanges (SCE), as the markers of cytogenetic damage, in cultured lymphocytes isolated from peripheral blood taken from exposed individuals. Thus, cytogenetic damage in circulating lymphocytes has been widely used as a biomarker of exposure and effects of pesticides [133, 134]. It has been reported that AChE non-inhibiting OP decomposition products exert stronger genotoxic potency compared to the parent compound [99], suggesting that the risk of genotoxicity from some insecticides might be appreciably greater than that predicted from standard toxicity tests [135]. Moreover, DNA damage leads to genomic instability that may result in mutagenesis and carcinogenesis [136]. Some epidemiological studies demonstrate cancer risk due to pesticides exposure [137-139], while The United States Environmental Protection Agency lists parathion as a possible human carcinogen [140].


Results

Molecular identification of bacterium

The bacterium (Achromobacter sp.) was identified based on 16S rDNA gene sequencing. PCR amplification yielded

1500 bp amplicon. BLAST analysis recorded 99% similarity to Achromobacter sp. sequence available in the NCBI Genbank database and thus, the test bacterium was identified as Achromobacter sp. The 16S rDNA gene sequence data have been deposited in the NCBI nucleotide database (Ac. No. <"type":"entrez-nucleotide","attrs":<"text":"HQ200410","term_id":"306410503","term_text":"HQ200410">> HQ200410). The phylogenetic tree clearly portrayed the relationships of the isolates used in the analysis. Thus, the Achromobacter sp. was successfully grouped along with other Achromobacter sp. isolates obtained from the NCBI GenBank database confirming the complete authenticity of our strain (Figure ​ (Figure1). 1 ). This Achromobacter sp. was presently deposited in culture collection center of IMTECH (Institute of Microbial Technology, Chandigarh, India).

Phylogenic relationships of Achromobacter sp. strain isolated from Rhabditis (Oscheius) sp. and known bacterial relatives based on 16S rDNA gene sequences (neighbor-joining method).

Isolation, purification, and characterization of bioactive compounds

The crude ethyl acetate extract recorded promising antibacterial activity against S. aureus (indicator microorganism). Column chromatographic purification of ethyl acetate extract yielded three compounds. The eluted solvent system in column chromatography and yield of each compound were revealed in Table ​ Table1. 1 . The three compounds were further purified by crystallization using hexane and benzene to yield white crystals. Antibacterial activity of these crystals was again confirmed by testing against S. aureus. TLC profile of these purified crystal compounds recorded single spots, and RF-value is presented in Table ​ Table1. 1 . In HPLC analysis, the compounds were eluted as single peaks, which confirm its purity (Figure ​ (Figure2). 2 ). The purity of the compounds recorded more than 98%, according to the peak area from the chromatogram.

Table 1

Information regarding the isolated compounds.

S.no.CompoundsColumn solventYield (mg)Melting point (ଌ)Optical rotation (c, 0.02, MeOH)
1Cyclo(D-Leu-D-Arg)30% DCM in hexane19202.51�.55[a]D − 118
2Cyclo(L-Trp-L-Arg)75% DCM in hexane16265.1�.34[a]D + 145
3Cyclo(D-Trp-D-Arg)15% ethyl acetate in DCM21262.23�.58[a]D − 167

HPLC profile of CDPs on a reversed-phase C18 HPLC column. Cyclo(D-Leu-D-Arg) (A), Cyclo(L-Trp-L-Arg) (B), and Cyclo(D-Trp-D-Arg) (C).

Identification of cyclic dipeptides as antimicrobial compounds

The three crystal compounds were subjected to various spectroscopic analyses for elucidating the chemical structure, i.e., UV, HR-MS, and NMR ( 1 H and 13 C NMR). Based on spectral data the compounds were identified as three different cyclic dipeptides (CDPs) or diketopiperazines. The CDPs identified are cyclo(D-Leu-D-Arg) (1), cyclo(L-Trp-L-Arg) (2), and cyclo(D-Trp-D-Arg) (3), respectively (Figure ​ (Figure3 3 ).

Chemical structure of cyclic dipeptides. Cyclo(D-Leu-D-Arg) (1), Cyclo(L-Trp-L-Arg) (2), and Cyclo(D-Trp-D-Arg) (3).

CDP 1: Cyclo(D-Leu-D-Arg) (1-<3-[(2R,5R)-5-(2-methylpropyl)-3,6-dioxopiperazin-2-yl]propyl>guanidine): 1 H NMR (DMSO-d6, 500 MHz) δ 7.91 (1H, br s, Arg-NH), δ 4.13(1H, dd, J = 8.2, 8.1 Hz, Arg-H2), δ 4.04 (1H, dd, J = 6.8, 5.7 Hz, Leu-H2), δ 3.47 (1H, m, Arg-H5), δ 3.26 (1H, m, Arg-H5), δ 2.18 (1H, m, Arg-H3), δ 1.99 (1H, m, Arg-H3), δ 1.94 (2H, m, Leu-H4), δ 1.75 (2H, m, Arg-H4), δ 1.75 (1H, m, Leu-H3), δ 1.32 (1H, m, Leu-H3), δ 0.83 (3H, d, J = 7.0 Hz, Leu-H5), δ 0.81 (3H, d, J = 7.0 Hz, Leu-H50) 13 C NMR (DMSO-d6, 125 MHz) 170.1, 167.4, 58.7, 53.9, 45.9, 38.1, 28.3, 24.1, 24.0, 23.1, and 22.9. The molecular formula of this compound was determined to be C12H24O2N5 by HR-ESI-MS at m/z 270.22351 [M+H].

CDP 2: Cyclo(L-Trp-L-Arg) (1-<3-[(2S,5S)-5-(1H-indol-3-ylmethyl)-3,6-dioxopiperazin-2-yl]propyl>guanidine): 1 H NMR (DMSO-d6, 500 MHz) 10.77d (d, J = 2.3), 8.17 (d, J = 2.4), 8.11 (d, J = 2.3), 7.69 (dd, J = 8.0, 1.0), 7.41 (d, J = 8.1), 7.28 d (t, J = 5.7), 7.15 (ddd, J = 8.0, 7.0, 1.1), 7.12 (s), 7.01 (ddd, J = 8.0, 7.0, 1.0), 4.37 (ddd, J = 4.8, 3.7, 1.3), 3.74 (ddd, J = 7.7, 5.2, 1.5), 3.51 (dd, J = 14.8, 3.6), 3.19 (dd, J = 14.7, 4.6), 2.72 (d, J = 7.0, 2.0), 0.81 (m), 0.79 (m), 0.54 (m). 13 C NMR (DMSO-d6, 125 MHz) 169.81, 169.28, 158.54, 137.85, 129.41, 126.17, 122.44, 120.35, 120.29, 112.32, 109.60, 57.21, 55.24, 41.68, 32.16, 30.52, and 24.59. The molecular formula of this compound was determined to be C17H23O2N6 by HR-ESI-MS at m/z 343.39558 [M+H].

CDP 3: Cyclo(D-Trp-D-Arg) (1-<3-[(2R,5R)-5-(1H-indol-3-ylmethyl)-3,6-dioxopiperazin-2-yl]propyl>guanidine): 1 H NMR (DMSO-d6, 500 MHz) 10.91d (d, J = 2.3), 8.21 (d, J = 2.4), 8.24 (d, J = 2.3), 7.77 (dd, J = 8.0, 1.0), 7.43 (d, J = 8.1), 7.34 d (t, J = 5.7), 7.21 (ddd, J = 8.0, 7.0, 1.1), 7.22 (s), 7.01 (ddd, J = 8.0, 7.0, 1.0), 4.43 (ddd, J = 4.8, 3.7, 1.3), 3.88 (ddd, J = 7.7, 5.2, 1.5), 3.61 (dd, J = 14.8, 3.6), 3.33 (dd, J = 14.7, 4.6), 2.77 (d, J = 7.0, 2.0), 0.88 (m), 0.79 (m), 0.61 (m). 13 C NMR (DMSO-d6, 125 MHz) 170.11, 170.01, 158.79, 138.22, 130.28, 127.17, 123.14, 121.38, 121.32, 113.28, 109.66, 59.01, 54.67, 41.99, 32.86, 31.48, and 25.5. The molecular formula of this compound was determined to be C17H23O2N6 by HR-ESI-MS at m/z 343.37431 [M+H].

Absolute configuration determination of cyclic dipeptides

The advanced Marfey's analysis was effectively employed for determining the absolute configuration of CDPs. Regarding the absolute configuration of compounds, the CDP 1 and 3 contain D-amino acids, whereas CDP 2 contains L-amino acids (Data not shown). The three derivatives obtained by the acid hydrolysis of the CDPs were compared with the HPLC retention times of the derivatized standard D and L-amino acids.

Antibacterial activity of cyclic dipeptides

The pure CDPs were tested for antibacterial activity against 10 wound associated bacterial pathogens using CLSI protocol. MIC and MBC values of CDPs were recorded and are presented in Table ​ Table2. 2 . The test pathogen that exhibited the highest sensitivity toward CDP 1 was B. subtilis. CDP 2 was active against all the test bacteria only at higher concentration and best activity of this compound was recorded against S. epidermidis (32 μg/ml), followed by S. aureus (64 μg/ml). Interestingly, CDP 3 recorded good activity against all test pathogens in impressively low concentration, and best activity was recorded against S. aureus and P. aeruginosa (0.5 μg/ml). The activity of the test compounds was better than the ampicillin, standard antimicrobial drug. The disc diffusion assay of the CDPs against test bacteria was shown in Table ​ Table3 3 .

Table 2

MIC and MBC of cyclic dipeptides against wound associated bacterial pathogens.

Test bacteriaMIC (μg/ml)
CDP 1CDP 2CDP 3Ampicillin
MICMBCMICMBCMICMBCMICMBC
B. subtilis81612512548816
S. aureus163264640.50.544
S. epidermidis1616326481648
S. faecalis3264500100048816
E. faecium16322502502424
P. aeruginosa1252502505000.50.522
P. vulgaris500500100010004824
P. mirabilis125125100020006412848
K. pneumonia2505001252502448
S. typhi641251252503264816

Table 3

Antimicrobial activity of cyclic dipeptides against wound associated bacteria.

Test bacteriaInhibition zone (dia. in mm)
CDP 1CDP 2CDP 3Ampicillin
B. subtilis18 ± 117 ± 024 ± 026 ± 0
S. aureus20 ± 118 ± 0.5735 ± 0.5728 ± 1
S. epidermidis21 ± 1.5215 ± 027 ± 1.1530 ± 1.15
S. faecalis17 ± 115 ± 0.5730 ± 1.1529 ± 0
E. faecium2312 ± 0.5731 ± 1.1526 ± 1
P. aeruginosa2111 ± 034 ± 125 ± 0.57
P. vulgaris2013 ± 0.5729 ± 1.7330 ± 0
P. mirabilis16 ± 013 ± 0.5730 ± 024 ± 1.52
K. pneumonia18 ± 1.210 ± 02727 ± 0.57
S. typhi219 ± 1.223 ± 130 ± 1.52

Values represent mean of three replications.

Cyclic dipeptides synergistically enhance the activity of ampicillin against wound pathogens

The combined activities of CDPs with ampicillin from the in vitro checkerboard assay against wound associated bacteria are shown in Table ​ Table4. 4 . FIC, FBC, FIC index, FBC index and interpretations for the activities of CDPs, and ampicillin against the test bacteria predominantly recorded the synergistic interaction, i.e., significant enhancement in the bioactivity. But CDP 1 with ampicillin against P. aeruginosa and CDP 2 with ampicillin against K. pneumonia recorded additive. Antagonism and indifference were not recorded for the combinations. When CDP 3 was combined with ampicillin for the inhibition of P. aeruginosa, an important synergistic effect (FIC = 0.09) was observed and the MIC values of CDP 3 and ampicillin were reduced to more than five times below their individual MIC values, respectively (Table ​ (Table4). 4 ). From the checkerboard assay, it is clearly evident that the CDPs enhance the activity of ampicillin against wound bacterial pathogen tested and in most combinations the MIC level has reduced many folds. This data clearly indicated that the combination is more effective than the individual compounds.

Table 4

Synergistic effects of the cyclic dipeptides with ampicillin against wound associated bacteria.

Test bacteriaAgentMIC/MBC (μg/ml)FIC/FFCFICI b /FFCI cOutcome
AloneCombination a
B. subtilisCDP 18/161/20.13𢅐.130.16𢅐.16Synergistic/synergistic
Ampicillin8/160.25/0.50.03𢅐.03
CDP 2125/12516/160.13𢅐.130.26𢅐.26Synergistic/synergistic
Ampicillin8/161/20.13𢅐.13
CDP 34/81/10.25𢅐.060.31𢅐.12Synergistic/synergistic
Ampicillin8/160.25/0.50.06𢅐.06
S. aureusCDP 116/322/40.13𢅐.130.26𢅐.26Synergistic/synergistic
Ampicillin4/40.5/0.50.13𢅐.13
CDP 264/648/160.13𢅐.250.38𢅐.5Synergistic/synergistic
Ampicillin4/41/10.25𢅐.25
CDP 30.5/0.50.03/0.060.06𢅐.120.18𢅐.37Synergistic/synergistic
Ampicillin4/40.5/10.12𢅐.25
S. epidermidisCDP 116/161/10.06𢅐.060.19𢅐.12Synergistic/synergistic
Ampicillin4/80.5/0.50.13𢅐.06
CDP 232/644/40.13𢅐.060.26𢅐.19Synergistic/synergistic
Ampicillin4/80.5/10.13𢅐.13
CDP 38/161/10.13𢅐.060.25𢅐.12Synergistic/synergistic
Ampicillin4/80.24/0.240.12𢅐.06
S. faecalisCDP 132/644/80.13𢅐.130.19𢅐.19Synergistic/synergistic
Ampicillin8/161/20.06𢅐.06
CDP 2500/100032/320.06𢅐.060.09𢅐.12Synergistic/synergistic
Ampicillin8/164/80.03𢅐.06
CDP 34/81/10.13𢅐.060.15𢅐.08Synergistic/synergistic
Ampicillin8/160.12/0.250.02𢅐.02
E. faeciumCDP 116/324/80.02𢅐.020.14𢅐.15Synergistic/synergistic
Ampicillin2/40.25/0.50.12𢅐.13
CDP 2250/25032/640.13𢅐.260.38𢅐.51Synergistic/additive
Ampicillin2/40.5/10.25𢅐.25
CDP 32/40.5/10.25𢅐.250.31𢅐.31Synergistic/synergistic
Ampicillin2/40.12/0.250.06𢅐.06
P. aeruginosaCDP 164/12516/160.25𢅐.130.31𢅐.19Synergistic/synergistic
Ampicillin8/160.5/10.06𢅐.06
CDP 2125/2508/160.06𢅐.060.19𢅐.19Synergistic/synergistic
Ampicillin8/161/20.13𢅐.13
CDP 30.5/10.03/0.030.06𢅐.030.09𢅐.06Synergistic/synergistic
Ampicillin8/160.25/0.50.03𢅐.03
P. vulgarisCDP 1500/50064/1250.13𢅐.250.38𢅐.5Synergistic/synergistic
Ampicillin2/40.5/10.25𢅐.25
CDP 21000/1000125/1250.13𢅐.130.38𢅐.38Synergistic/synergistic
Ampicillin2/40.5/10.25𢅐.25
CDP 34/80.24/0.50.06𢅐.060.07𢅐.07Synergistic/synergistic
Ampicillin2/40.03/0.060.01𢅐.01
P. mirabilisCDP 1125/12516/160.13𢅐.130.19𢅐.19Synergistic/synergistic
Ampicillin4/80.25/0.50.06𢅐.06
CDP 21000/2000125/2500.13𢅐.130.38𢅐.26Synergistic/synergistic
Ampicillin4/81/10.25𢅐.13
CDP 364/12516/320.25𢅐.250.38𢅐.38Synergistic/synergistic
Ampicillin4/80.5/10.13𢅐.13
K. pneumoniaCDP 1250/50032/640.13𢅐.130.26𢅐.26Synergistic/synergistic
Ampicillin4/80.5/10.13𢅐.13
CDP 2125/25032/640.26𢅐.260.51𢅐.51Additive/additive
Ampicillin4/81/20.25𢅐.25
CDP 32/40.12/0.240.06𢅐.060.12𢅐.12Synergistic/synergistic
Ampicillin4/80.25/0.50.06𢅐.06
S. typhiCDP 1125/25032/640.26𢅐.260.51𢅐.51Additive/additive
Ampicillin2/20.5/0.50.25𢅐.25
CDP 2250/50032/320.12𢅐.060.37𢅐.31Synergistic/synergistic
Ampicillin2/20.5/0.50.25𢅐.25
CDP 332/644/80.12𢅐.120.18𢅐.18Synergistic/synergistic
Ampicillin2/20.12/0.120.06𢅐.06

Combination of cyclic dipeptides and ampicillin significantly increases the inhibition of biofilms formation

The effects of the combination of CDPs and ampicillin on the inhibition of wound associated bacterial biofilm formation were shown in Figure ​ Figure4. 4 . The combination of CDPs and ampicillin recorded significant inhibition in the biofilm formation by the bacterial pathogens when compared to the effect of individual compounds. Significant inhibition in the biofilms was recorded by CDP 3 in combination with ampicillin when compared to the other CDPs.

Effects of the synergistic combination of cyclic dipeptides and ampicillin against biofilm formation by wound associated bacterial pathogens. Error bars indicate the standard deviations of three measurements. Different letters in the superscript were significantly different according to Duncan's multiple range test (p < 0.05).

Immunomodulatory actions of cyclic dipeptides

The abilities of CDPs to modulate the production of cytokines are shown in Figure ​ Figure5. 5 . Incubation with the CDPs significantly enhanced the production of cytokine IL-10 by unstimulated cells and also boosted the production in cells stimulated with concanavalin A (standard T cell mitogen activator) (Figure ​ (Figure5A). 5A ). Production of IL-4 by unstimulated cells was significantly enhanced in the presence of 10 μg/ml CDPs, but the compound was without significant effect on concanavalin A-stimulated cells (Figure ​ (Figure5B). 5B ). Production of the pro-inflammatory cytokine TNF-α was not affected by incubation with the CDPs in the presence or absence of concanavalin A (Figure ​ (Figure5C 5C ).

Effects of cyclic dipeptides on the production of (A) IL-10, (B) IL-4, and (C) IFN-α by unstimulated and concanavalin A stimulated human peripheral blood mononuclear cells. Error bars indicate the standard deviations of three measurements. Different letters in the superscript were significantly different according to Duncan's multiple range test (p < 0.05).

Cyclic dipeptides recorded significant toxicity toward the intracellular S. aureus

Infected murine J774 cells were treated with 1X and 2X MIC concentrations of CDPs. Significant decreases in bacterial loads were consistently observed for the treatment of CDPs (Figure ​ (Figure6), 6 ), whereas none was observed with the solvent control (Figure ​ (Figure6). 6 ). However, CDP 3 treatments significantly reduced the number of viable intracellular S. aureus at their respective 0.5X, 1X, and 2X MIC concentrations in J774 macrophages. Interestingly, cytotoxicity was not recorded for the uninfected macrophages by CDPs (data not shown).

Intracellular efficacy of rifampicin in J774 murine macrophage. (A) CDP 1, (B) CDP 2, and (C) CDP 3. Experiments were performed three times. Error bars indicate the standard deviations of three measurements. Different letters in the superscript were significantly different according to Duncan's multiple range test (p < 0.05).

Cyclic dipeptides recorded no cytotoxicity toward normal cell lines

Cytotoxicity activity of CDPs against three normal cell lines after 72 h of treatment was recorded by MTT assay and was presented in Figure ​ Figure7. 7 . When exposed to CDPs in the range, 5� μg/ml, CDPs recorded no cytotoxicity against FS, VERO, and L231 cell lines (Figure ​ (Figure7 7 ).

Cytotoxicity of cyclic dipeptides against normal cell lines. (A) FS normal fibroblast, (B) VERO, and (C) L231 normal lung epithelial. Error bars indicate the standard deviations of three measurements. Different letters in the superscript were significantly different according to Duncan's multiple range test (p < 0.05).


Discussion

Phylogeny and Species Boundaries in Achromobacter

According to sequence typing database PubMLST ( Jolley 2016), which is by far the most comprehensive molecular database for this organism ( Spilker et al. 2013), there are over 18 Achromobacter species, only a subset of which have been fully genome sequenced. This study provides genome-wide evidence to support the classification of group 5, that is A. xylosoxidans, A. ruhlandii, A. dolens and A. insuavis. However, with respect to ANI and accessory genome content, A. ruhlandii and A. dolens should be part of the same species. Moreover, because species of group 5 are within the 93–95% ANI “grey zone” ( Konstantinidis and Tiedje 2005), it would be equally appropriate to classify them as subspecies. Achromobacter xylosoxidans is the most prevalent species of the genus among infected CF patients ( Spilker et al. 2013 Coward et al. 2016) as well as nonrespiratory clinical samples ( Amoureux et al. 2016), followed in frequency by the three other species of group 5. This explains the over-representation of these species in our data set, as there is a clear sampling bias favoring clinical isolates in both typing and sequence databases. Still, the prevalence of A. xylosoxidans, A. ruhlandii, A. dolens and A. insuavis among clinical isolates combined to their phylogenetic clustering suggest that they are better adapted to cause opportunistic infections. If this is the case, Achromobacter evolution would be reminiscent of that of its closest relative, Bordetella ( Li et al. 2013), a largely pathogenic genus involved in human respiratory infections ( Melvin et al. 2014) that evolved from a metabolically versatile, environmental ancestor ( Gross et al. 2008 Zelazny et al. 2013 Linz et al. 2016). As for the 18 genomes outside of group 5, our results suggest that they represent almost as many different species. More genome sequences would be required to confidently support taxonomic inference, but it is reasonable to assume that group 1 comprises A. spanius and A. piechaudii, and that A. arsenitoxydans is part of group 2.

Mechanisms of Adaptation to the Host

A central goal of this study was to identify genomic features implicated in pathogenicity, or more broadly speaking, in adaptation to the human host environment. Based on the evolutionary tree of the Achromobacter genus, this goal became intimately linked to understanding the evolution of group 5, which we propose to call “the clinical lineage.” In looking at both the core and the accessory genome, we found that this lineage of Achromobacter is likely to share adaptive mechanisms with other biological systems.

Identifying positively selected genes in the clinical lineage led us to find similarities with a study on within-host evolution of Achromobacter spp. in CF patients, where most mutated genes were involved in general metabolism, and some were related to virulence and antimicrobial resistance ( Ridderberg et al. 2015). Metabolic genes are often identified in screening approaches aimed at finding genes implicated in virulence, but, even though metabolism and virulence are known to be intimately linked, understanding of detailed mechanisms is extremely limited ( Rohmer et al. 2011 Fuchs et al. 2012). Metabolism is key to adapt to host conditions and effectively compete against resident microbiota ( Rohmer et al. 2011 Olive and Sassetti 2016), hence it may not be surprising that almost half of positively selected genes in the clinical lineage were implicated in metabolic processes. This analysis also led to the identification of three antibiotic resistance genes, which will be discussed later on.

Although Achromobacter and Pseudomonas are distantly related bacteria, both are obviously well equipped to thrive in the CF lung environment. We found two possible common features between them, the first one pertaining to the type III secretion system (T3SS), which pathogenic bacteria use to inject effectors into host cells ( Coburn et al. 2007). A previous comparative genomics analysis of Achromobacter using only three genomes, one from a CF patient and two from environmental isolates, showed that some virulence genes, mostly T3SS related, were only present in the CF isolate ( Li et al. 2013). Considering that we had a larger data set, coupled to a better understanding of the evolutionary history of the genus, we compiled virulence gene presence per phylogenetic lineage. As suspected based on our observation that accessory genome content is largely influenced by evolutionary history, patterns of gene presence/absence are not as clear cut as was previously suggested. T3SS genes were more common in the clinical lineage, but they were not systematically present, and some of the other species carried them as well. The T3SS anciently evolved from the flagellum machinery ( Abby and Rocha 2012) and is highly conserved among Bordetella and Achromobacter species. However, it may be absent in some isolates, which are generally, but not exclusively, nonpathogenic ( Li et al. 2013). Therefore, this virulence factor may have been lost during adaptation to environmental niches, or selected against in certain cases of chronic lung infection ( Ridderberg et al. 2015). This last mechanism is reminiscent of adaptation to the CF lung for P. aeruginosa, where virulence factor loss is thought to be advantageous to evade host defenses ( Nguyen and Singh 2006 Smith et al. 2006).

The second potential mechanism that we identified relates to survival with limited oxygen. It was demonstrated that the CF mucus is oxygen depleted, prompting P. aeruginosa to use denitrification for energy production ( Schobert and Jahn 2010). Moreover, there is evidence that molybdenum uptake, upon which denitrification depends, is essential for anaerobic proliferation and influences virulence in this pathogen ( Pederick et al. 2014 Perinet et al. 2016). Considering that most of the genomes of clinical origin used here are from CF patients, it was interesting to find two positively selected genes involved in nitrogen metabolism, suggesting that Achromobacter and P. aeruginosa may share this adaptive mechanism to the CF lung environment.

Flexible and Mobile Genomic Diversity

We have shown that accessory genome content generally matches evolutionary history more than it does ecological niche. These results are consistent with a study on opportunistic pathogen P. aeruginosa, where no correlation was found between genome content and infection type or environmental source ( Wolfgang et al. 2003). Nevertheless, using a discriminant analysis of principal components (DAPC), we attempted to identify genes that were specific to isolates of clinical or nonclinical origin. The 28 most discriminant genes identified (based on loading > 0.001, i.e. above background noise) are listed in supplementary file 9 , Supplementary Material online. It is noteworthy that many of them are consecutive (locus tags separated by increments of 5), and are likely part of prophages or genomic islands. Especially among genes that were more common among clinical isolates, we identified multiple hypothetical proteins, highlighting the fact that genes with unknown function may play very important roles. Among characteristic genes of nonclinical isolates, this analysis revealed an arsenic efflux pump protein. This is interesting, considering that arsenic resistance is a staple of contaminated soil isolate A. arsenitoxidans SY8 ( Li et al. 2012), although this result is influenced by the presence of multiple contaminated site isolates in our data set.

Compiling the number of genes found as a function of the number of genomes available showed that each new A. xylosoxidans genome results in gene discovery, which corresponds to the concept of an open pan-genome. It was suggested that this type of pan-genome reflects a need for high adaptability in the face of diverse environmental conditions, which may translate into high levels of lateral gene transfer among organisms ( Tettelin et al. 2008). This result motivated a systematic search for mobile genetic material in the Achromobacter genus. Using two independent approaches, we found putative mobile elements that support past exchange of genetic material between Achromobacter and bacterial genera that share the same ecological setting, without assumptions on the direction of this exchange. Considering that many of these other genera are also environmental organisms capable of causing opportunistic infections, determining whether gene transfer in the soil has predominated over gene transfer in host environments is far from being trivial, although it is probably reasonable to assume that this mechanism has played an important role in both cases. It is now widely recognized that horizontal gene transfer is key to rapid adaptation in the contexts of infectious disease, plant symbiosis and bioremediation ( Frost et al. 2005), all of which are relevant to the ecology of Achromobacter.

While they yielded a similar taxonomic breakdown of gene exchange, results of the two approaches used here to find evidence of horizontal transfer shared 338 proteins, representing only 6.6% of the plasmid-related proteins and 19.4% of the putative mobile element proteins. This is due to the fact that the first approach uses a less stringent identity cut-off, thus allowing the identification of more distantly related elements, while the second approach, with a 95% sequence identity cut-off, has a clear bias in favor of more recent transfer events. Thus, the two methods are complementary in this respect. It is also important to note that these results likely include false positives in the form of conserved regions inherited from a common ancestor, for example between Achromobacter and Bordetella. Within their overlapping results, there were 9 AMR genes: aadA25, acrF, ceoB, cmlA1, golS, mexQ, mexT, sul1 and sul2. Five of them (aadA25, cmlA1, golS, sul1 and sul2) were infrequent among Achromobacter isolates, which is what would be expected in cases of acquired resistance ( Hu et al. 2015).

Antibiotic Resistance

It has been known for some time that Achromobacter species have innate resistance against multiple antibiotics, namely cephalosporins (beta-lactam), aztreonam (beta-lactam), and aminoglycosides ( Glupczynski et al. 1988 Saiman et al. 2001 Almuzara et al. 2010), which include antibiotics relevant to CF lung infection treatment ( Tom et al. 2016). Most likely due to the selective pressures that they have undergone, our results show that isolates of the clinical lineage carry more resistance genes than other isolates, namely for resistance against aminoglycosides, beta-lactams, chloramphenicol and sulfonamides. These additional genes presumably contribute to acquired resistance. There are certain limitations to the approach used here to identify genes of the resistome. First, EmrA, while it was annotated as an AMR gene and found to be under positive selective pressures, was not detected in the analysis presented in figure 5. Second, even if a gene is present and reasonably similar to a known AMR gene, there is no guarantee that it is expressed, or even functional. These issues highlight the need for tools that are not database dependant, especially when it comes to organisms that remain to be well described like Achromobacter. Unfortunately, identifying the genetic basis of a trait de novo requires a large data set of both genotypes and phenotypes ( Bradley et al. 2015), which is simply not available at the moment for this organism.

Three genes encoding efflux pump components were under positive selection in the clinical lineage: (1) EmrA, a periplasmic fusion protein part of a major facilitator superfamily multidrug export complex (2) MacA, the periplasmic fusion part of an ABC efflux pump that exports macrolides, and (3) MexW, the multidrug transporter of a RND-type efflux pump. The periplasmic fusion protein is essential to anchor the inner membrane transporter and the outer membrane channel in tripartite efflux systems it can even play a regulatory role ( Lin et al. 2009 Modali and Zgurskaya 2011). Although efflux pumps are anciently evolved systems, substrate changes have been observed relatively frequently in prokaryotes ( Saier et al. 1998) and show a tendency to favor loss of specificity, which translates into multi-resistance ( Lewis 1994 Vargiu et al. 2016). Moreover, efflux pumps do not exclusively export antimicrobials, and studies on multiple pathogens suggest that they are implicated in bacterial virulence as well ( Alcalde-Rico et al. 2016). Hence, efflux pump components, which are generally part of the core genome, represent potent targets for adaptation to a pathogenic lifestyle.


Material and methods

Soil sampling and chemical properties

Two different agricultural soils were selected for our experiment. Soil 1 (S1) was sampled at INRA’s experimental station in Dijon (47° 30′ 22.1832″ N, 4° 10′ 26.4648″ E), France. While soil 2 (S2) was sampled at INRA’s experimental station in Montpellier (43° 37′ 04.7″ N, 3° 51′ 26.2″ E), France. S1 soil properties were 41.9% clay, 51.9% silt, 6.2% sand, 2.6 % of organic matter (OM), 5.6 pH, and 18.9 cmolc kg −1 cation exchange capacity (CEC). S2 soil properties were 28.8% clay, 35.2% silt, and 34.6% sand, 1.2% OM, pH of 8.6, and 11 cmolc kg −1 CEC. Samples were collected with an alcohol sterilized soil auger. Each soil was sieved (4 mm, alcohol sterilized) and stored at − 20 °C before downstream procedures.

Experimental design

To investigate the effect of soil phage inoculation on soil bacterial community composition and diversity as well as inorganic N pools, we performed experiments using a reciprocal transplant design under different community assembly scenarios. The different community assembly scenarios assessed whether colonizing, established, or natural bacterial communities are affected in a similar way by native or non-native phages. We incubated soil phage suspensions derived from soils S1 and S2 (namely PS1 and PS2) with their native or non-native communities in sterile soils or natural soils. Soil phage suspensions were obtained using a tangential filtration systems which will be explained in details further in this section. The retentates (i.e., bacterial suspensions, namely BS1 and BS2) were used for experiments in sterile soil. An additional treatment based on a mixture of previously isolated phages (namely phage cocktail PC) was included as an outgroup. The resulting experimental conditions were:

Phage and bacteria suspensions inoculated in sterile soil at the same time. Microcosms containing sterile soil were inoculated with BS1 or BS2 suspension and either (i) the phage suspensions from the same soil, (ii) the phage suspension from the other soil, or (iii) the cocktail of phage isolates.

Phage suspensions inoculated after bacteria inoculation in sterile soil. Microcosms containing sterile soil were inoculated with BS1 or BS2, and after 28 days, they were inoculated with either (i) the phage suspensions from the same soil, (ii) the phage suspension from the other soil, or (iii) the cocktail of phage isolates.

Phage suspensions inoculated in natural soils. Microcosms containing non-sterile soil (S1 or S2) were setup, and after 28 days of incubation, they were inoculated either by (i) the phage suspension from the same soil, (ii) the phage suspension from the other soil, or (iii) the cocktail of various phage isolates.

Overall, the experimental conditions tested included two soil bacterial communities (BS1 and BS2) × three phage sources (PS1, PS2, and PC) × two phage suspension status (natural and autoclaved as control) × three community assembly experiments (A: during colonization, B: after colonization, and C: in natural soils) × five replicates (i.e., n = 5), giving a total of 180 microcosms (Fig. 1). The IDs for each treatment hereinafter will be named as following for each community assembly experiment (A, B, or C): source of hosts (BS1 or BS2 for bacterial suspensions, and S1 or S2 for natural soils): phage origin (PS1, PS2, or PC) and phage status (ending by “a” if autoclaved), e.g., BS1PS2 or BS1PS2a.

Preparation of the phage and bacterial suspensions from soil S1 and S2

The TFF system is useful to filter large amounts of environmental samples more efficiently compared to conventional perpendicular filtration systems, in which soil particles block membrane pores more easily. We used two filters (MiniKros® Sampler polysulfone provided by Repligen) with different pore sizes: 0.2 μm and 100 kDa. They were assembled in parallel using a peristaltic pump (MiniPlus 3, Gilson) running at constant flux (10 rpm) adjusted to filter at minimum pressure. Filters were sanitized according to the manufacturer instructions using 0.1 M NaOH solution previously to each sample filtration.

Soil suspensions derived from 4 kg of soil (S1 or S2) washed with 4 L of phage buffer (68 mM NaCl, 10 mM MgSO4, Tris-Cl pH 7.5) [56], by dividing it into 500-mL bottles, that were manually shaken before centrifuging at 4500 G for 20 min at 4 °C, and then filtered with a TFF system. The retentates obtained (BS1 or BS2) were used in experiments A and B, and the filtrates (PS1 or PS2) were used in experiment A. After the incubation period (Fig. 1), this procedure was repeated and the filtrates obtained (PS1 or PS2) were used in experiments B and C. The final volumes of filtrates and retentates were 500 mL and 1.5 L, respectively. They were stored in 4 °C and inoculated in the different microcosms on the following day.

Preparation of the phage cocktail suspension

The phage cocktail (PC) was obtained by mixing three Pseudomonas phages, two of them isolated using Pseudomonas syringae pv. tomato as host (

10 7 plaque-forming units (PFUs) both) and one using Pseudomonas syringae pv. avii (

10 8 PFUs), one Xanthomonas phage (

10 9 PFUs), using Xanthomonas campestri pv. citri as host, and one Bacillus phage (

10 6 PFUs), using a Bacillus simplex as host. The latter was isolated from S1 by L.P.P. Braga. The other phages were isolated from plant decomposing material by W. Kot and L.H. Hansen in Denmark. Phage isolates were isolated by enrichment method using double-layer agar followed by plaque purification [57]. The PC inoculant was made of 400 μL of lysates of each phage, a total volume of 2 mL (i.e., five phages × 400 μL).

Microcosms setup

The experiments A (during colonization) and B (after colonization) were performed using microcosms containing 50 g of dry soil S1 sterilized by gamma-radiation (35 kGy Conservatome, Dagneux, France). The experiment C (natural soils) was performed using microcosms containing 50 g of soil S1 or S2. The volumes inoculated in the microcosms were 6 mL of PS1 or PS2 and 18 mL of BS1 or BS2. They were sampled from the final volumes obtained with TFF (retentate or filtrate). The bottle was vigorously shaken prior to each sampling. Autoclaved phage suspension PS1, PS2, or PC were included as controls. All microcosms were incubated at room temperature in sterile conditions for 34 to 35 days after phage inoculation, and moisture was maintained at 80 % of field capacity by regular addition of sterile water.

Transmission electron microscopy

In order to qualitative confirmation of the presence and integrity of phage particles in soil-filtered suspensions, microscopy images were obtained by TEM performed at the Center of Microscopy, INRA, Agroecology (Dijon). Five microliters sampled direct from phage suspensions (PS1 or PS2) were adhered to cooper EM grid overlaid with a collodion carbon film for 1 min, excess solution wicked of with a filter paper. The grid was stained with 2% uranyl acetate for 1 min and airdried. Grids were observed with a Hitachi H7500 (Hitachi Scientific Instruments Co., Tokyo, Japan) transmission electron microscope operating at 80 kV and equipped with an AMT camera.

DNA extraction and sequencing

DNA was extracted from samples of PS1 and PS2 that were collected in duplicates. Briefly, 1 mL of a given phage suspension was sampled and 15 U of DNase I was added to 900 μl of filtrate before incubating for 30 min at 37 °C to remove residual DNA. Then SDS (final concentration of 0.2%) and 30 μl of proteinase K (20 mg/ml) were added, followed by incubation for 1 h at 55 °C. Phage DNA was purified using Clean & Concentrator-10 kit (Zymo Research, CA, USA) according to the manufacturers’ protocol. The sequencing libraries were built using Nextera XT kit (Illumina, CA, USA) and sequenced on a NextSeq platform using 300 cycle MID kit v.2 which gives paired-ended reads of 150 bp. Each sample was sequenced four times to enable phage genome recovery from the dataset.

Soil DNA extraction was performed using DNeasy PowerSoil HTP 96 Kit (Qiagen, Hilden, Germany) with 0.3 g of soil from each soil microcosm. Samples from the initial non-treated soils (S1 and S2) were also included (n = 5). DNA quantification after extraction was performed with picogreen. 16S rRNA gene amplicons were generated in two steps according to Berry et al. [58]. In the first step, the bacterial 16S rRNA gene V3-V4 hypervariable region was amplified by polymerase chain reaction (PCR) using the fusion primers U341F (5′-CCTACGGGRSGCAGCAG-3′) and 805R (5′-GACTACCAGGGTATCTAAT-3′) (Takahashi et al. 2014), with overhang adapters (forward: TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG, adapter: GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG) to allow the subsequent addition of multiplexing index-sequences. PCR was carried out in duplicate 15 μL reactions containing 7.5 μL Phusion High-Fidelity PCR Master Mix (Thermo Fisher Scientific, MA, USA), 0.25 μM of each primer, 250 ng T4 gp32 (MPBio), and 1 ng template DNA. Thermal cycling conditions were 98 °C for 3 min followed by 25 cycles of 98 °C for 30s, 55 °C for 30s, and 72 °C for 30s, with a final extension at 72 °C for 10 min. Duplicated first step PCR products were pooled then used as template for the second step PCR. In the second step, PCR amplification added multiplexing index-sequences to the overhang adapters using a unique multiplex primer pair combination for each sample. The reaction was carried out in duplicate 30 μL volumes containing 15 μL Phusion High-Fidelity PCR Master Mix (Thermo Fisher Scientific, MA, USA), 1 μM of one forward and one reverse multiplex primer, and 6 μL of first step PCR product. Thermal cycling conditions were 98 °C for 3 min followed by 8 cycles of 98 °C for 30s, 55 °C for 30s, and 72 °C for 30s, with a final extension at 72 °C for 10 min. Duplicate second step PCR products were pooled then visualized in 2% agarose gel to verify amplification and size of amplicons (around 470 bp). The amplicons were cleaned-up and pooled using sequalPrep TM Normalization plate kit 96-well (Invitrogen). Sequencing was performed on MiSeq (Illumina, CA, USA 2 × 250 bp) using the MiSeq reagent kit v2 (500 cycles). Demultiplexing and trimming of Illumina adaptors and barcodes was done with Illumina MiSeq Reporter software (version 2.5.1.3).

Soil inorganic nitrogen determination

Mineral nitrogen pools (NO3 - and NH4 + ) present in the soil were quantified according to the ISO standard 14256-2. Quantification was performed using three blanks in each series by colorimetry in a BPC global 240 photometer. Statistical test for detecting differences in levels of NH4 + and NO3 - across soil samples was performed in R environment using ANOVAs (aov function) followed by Tukey’s honestly significant difference (HSD) test, both from the stats package.

Computational and statistical analyses

Metagenomic sequences of phage suspensions from S1 and S2 were assembled with MetaSPADES separately [59]. Assembled sequences were mapped using BWA [60] and SAMTOOLS [61] was used to process the mapped data. Binning was performed with MetaBAT2 [62], MaxBIN [63], and CONCOCT [64] using MetaWRAP [65]. The bins found were dereplicated with DREP [66]. Phage bins were identified using a machine learning method implemented by MARVEL [67], because it uses bins to make predictions and demonstrated higher recall rates compared with other available tools. Next, relative abundance of phage bins was calculated with MetaWRAP [65] by the function quant_bins using Salmon [68] that quantifies the metagenomic reads directly against the bins. The mapping procedure is based on an auxiliary k-mer hash and was performed according to the default parameters, considering k-mers of length 31 as the minimum acceptable length for a valid match. The MetaWRAP function calculates the relative abundance normalized according to the size of the sequences and the portion of mapped reads in the dataset [65, 68]. Statistical test for detecting differences in phage bin abundances across soil samples was performed using the python function scipy.stats.ttest_ind. Taxonomic classification of phage bins was investigated with vConTACT2 using the ProkaryoticViralRefSeq88 database [69]. Open reading frames (ORFs) in phage bins were identified with MetaProdigal [70]. Contigs containing bacterial genomic sequence were considered, and bins were analyzed with CheckM [71] for further checking possible contamination.

16S rRNA gene amplicon analysis was performed in QIIME2 [72] environment. Sequences were filtered with Trimmomatics [73] and Cutadapt [74] for removal of illumina artificial sequences and low-quality sequences. The data set was imported into QIIME2, and paired-end reads were joined with VSEARCH [75] following q-score-joined method. Construction of the feature table based on amplicon sequence variants (ASVs) was performed using Deblur pipeline [76], which removes reads presenting more than two probable erroneous base calls, denoises, dereplicates, and filter chimeras. The average number of reads per sample after denoising was 6018 (± 2821). Rarefaction thresholds were determined for each pairwise within-group comparison separately, i.e., autoclaved vs active. For an optimal rarefaction threshold, one or two replicates had to be discarded in the following treatments: BS2PC (n = 4), from During Colonization Experiment BS1PS2 (n = 3), BS2PS2 (n = 4), BS2PS2a (n = 4), BS2PS1 (n = 4), BS2PS1a (n = 4), BS2PCa (n = 4), from After Colonization Experiment and BS1PS2 (n = 4), S2PS2 (n = 3), S2PS1 (n = 3), BS1PC (n = 4), and BS2PC (n = 4), from the Natural Soils Experiment. Tree for phylogenetic diversity analysis was built using the methods implemented in the align-to-tree-mafft-fasttree pipeline, with MAFFT [77] and FastTree [78]. Taxonomy was assigned by a classifier trained on V3–V4 region, including the set of primers used, based on greengenes database (v.05/2013 [79]). Next, diversity analyzes were performed according to the methods implemented by the core-metrics-phylogenetic pipeline following PERMANOVA and Kruskal-Wallis pairwise tests for assessing statistical significance on beta and alpha diversity tests, respectively. Significant differences in community diversity (p value ≤0.05) were further investigated with the gneiss pipeline to assess relevant microbial taxon contributing to the changes. Weighted and unweighted UniFrac distance values from the matrices that were obtained for within-group comparison were extracted in QIIME1 [80] to enable a between-group comparison across the experiments. The bar plots representing these values were obtained in PAST3 [81], and the statistical tests were performed using multcomp Tukey’s test in R environment.

Network models were constructed to investigate possible phage-derived indirect effect on microbial groups that could be expected due to elimination of competitors, nutrient release, generalized transduction, or AMGs inputs. The models were constructed with r package PLNmodels [82] sparse inverse covariance estimation was calculated using default parameters the best model was extracted with the function getBestModel and analyzed in igraph [83] using ASVs count tables pooling S1 and S2 samples separately to compare autoclaved vs non-autoclaved conditions.


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Materials and methods

Soil sample collection

Soil samples of sod-podzolic (SP) and chernozem (CZ) soil were collected in summer 2017 during expeditions to the Pskov (Pskov Research Institute of Agriculture, 57°50'44.2"N, 28°12'03.7"E) and Voronezh (Kamennaya Steppe reserve, 51°01'41.6"N, 40°43 '39.3"E) regions, respectively (S1 Fig). The director of Federal State Budget Scientific Institution “Kamennaya Steppe Experimental Forest District” and the director of the Federal State Budget Scientific Institution Pskov Research Institute of Agriculture gave permission for the sample collection. Soil samples were taken from the territories of the formerly sown areas from 10 different equidistant points from the upper soil layer (approximately 10 cm from the top of the soil profile). Finally, the selected samples were mixed and transported for laboratory research. Six replicates for each type of soil were formed.

Bacterial growth on nutritional medium

For cultivation, solid Hutchinson medium [14] with cellulose filters was used (grams/L: NaNO3: 2.5, FeCl3: 0.01, К2НРО4: 1.0, MgSO4·7H2O: 0.3, NaCl: 0.1 and CaCl2: 0.1 рН 7.2). The analysis was performed in six replicates for each type of soil. For both soils, the active growth of various types of bacteria was detected. After two weeks of cultivation, Petri dishes were washed out with sterile water, centrifuged, and subjected to DNA isolation. These samples were named gSP and gCZ accordingly.

DNA extraction and sequencing

The DNA was extracted from 0.2 g of soil using the PowerSoil DNA Isolation Kit (Mobio Laboratories, Solana Beach, CA, USA), which included a bead-beating step, according to the manufacturer’s specifications. Samples were homogenised with a Precellys 24 (Bertin Corp., USA) at 6.5 m/sec, twice for 30 s. The purity and quantity of DNA were tested by electrophoresis in 1% agarose in 0.5 × TAE buffer. DNA concentrations were measured at 260 nm using a SPECTROStar Nano (BMG LABTECH, Ortenberg, Germany).

The same DNA extraction procedure was applied to the culture plates. Microbial colonies were removed and solubilised in the extraction buffer, and DNA was extracted according to the manufacturer’s instructions. The average DNA yield was 2–5 μg DNA, with concentrations between 30 and 50 ng/μl. The purified DNA templates were amplified with the universal multiplex primers F515 5′-GTGCCAGCMGCCGCGGTAA-3′ and R806 5 ′-GGACTACVSGGGTATCTAAT-3′ [15] targeting the variable region V4 of bacterial and archaeal 16S rRNA genes, flanking an approximately 300-bp fragment of the gene, extended with service sequences containing linkers and barcodes according to Illumina technology. The PCR reactions were assembled in a 15-μl mix containing 1 U of Phusion Hot Start II High-Fidelity polymerase and 1X Phusion buffer (Thermo Fisher Scientific, USA), 5 pM of both primers, 10 ng of DNA, and 2 nM of each dNTP (Life Technologies, USA). The PCR thermal profile used was 94°С for 30 s, 50°С for 30 s, and 72°С for 30 s for 29 cycles. A final extension was performed at 72°С for 3 min. PCR products were purified and size selected with AM Pure XP (Beckman Coulter, USA). Further library preparation was done according to the manufacturer’s protocol with the MiSeq Reagent Kit Preparation Guide (Illumina, USA). Libraries were sequenced on an Illumina Miseq with a MiSeq ® Reagent Kit v3 (2x300b) sequencing kit.

Data processing

Amplicon libraries of the 16S rRNA gene were processed using packages in R [16] and QIIME2 [17] software environments. RStudio [18] was used as the development environment for R. Raw sequence reads were trimmed and grouped into amplicon sequence variants (phylotypes) by use of the 'dada2' package [16]. The RDP classifier [19] based on Silva 132 [20] was used to classify assign taxonomic ranks to the phylotypes. The phylogenetic tree was built in the QIIME2 software environment in the SEPP package [21]. Data were normalised by a rarefaction algorithm according to the sample with the smallest number of readings for alpha and beta-diversity analysis. For differential analysis of phylotypes and quantitative metrics, the normalisation was performed by a variance stabilisation algorithm through the ‘DEseq2’ package [22]. To estimate the significance of differences between phylotypes previously normalised data were processed using the Wald test, with Benjamin-Hochberg false discovery rate (FDR) correction in the ‘DEseq2’ package [23]. The UniFrac, unweighted UniFrac [24], Bray-Curtis and MPD [25] algorithms were used as metrics for beta diversity. Beta-diversity data was graphically reproduced using PCoA [26]. Statistical analysis of beta-diversity was done by PERMANOVA [27] in the form of the adonis2 function (‘vegan’ package) [28]. The formula by Apostol and Mnatsakanian [29] in package ‘usedist’ [30] was used as an additional statistical approach to calculate the distance between the centres of mass (centroids) of the sample groups in the beta-diversity space. The function cophenetic.phylo from the ‘ape’ [31] package was used to agglomerate closely related taxa using single-linkage clustering. The reliability of the dependence of the representation of phylotypes in soil and culturomes was obtained through the Fisher test for the generalised linear model (‘glm’) [32]. The R packages ‘phyloseq’ [33], ‘ggpubr’ [34], ‘picante’ [35], ‘ggforce’ [36], ‘tidyverse’ [37], ‘ggtree’ [38], ‘ampvis2’ [39] and ‘rnaturalearth’ [40] were used for post-processing and visualisation of the obtained data.

Data deposition

All sequences were deposited to the SRA (NCBI) within the dataset: Submission ID: SUB5714186 and BioProject ID: PRJNA549392.


Methods

Culture conditions and DNA extraction

Five type strains of the genus Leucobacter were selected for comparative studies based on 16S rRNA gene pairwise similarity to strain GP (Table 1) [26] and purchased from DMSZ (Germany). These strains were grown in Brain-Heart Infusion (BHI, Sigma) for 15 h. All incubations were carried out in the dark at 30 °C under continuous shaking at 120 rpm. The two-member consortium [26] consisting of Achromobacter denitrificans PR1 (LMG 30905) and strain GP was incubated for 7 d in mineral medium with 0.2 g/l yeast extract, 4 mM ammonium sulfate, 700 mM succinate, 0.6 mM SMX, and 2.5 g/L 2-phenylethanol (Sigma) as an inhibitor of Gram-negative cells (MMSY-SMX-PE). Further attempts to isolate strain GP were performed by incubating the consortium in 25% BHI agar plates (v/v) with 0.6 mM SMX, heme or heme precursors (10 μg/l, coproporphyrin III, coproporphyrin III tetramethylester, coproporphyrin I dihydrochloride) [26], putrescine (9 μg/l) and catalase (500 U) from Sigma, respectively. Genomic DNA extraction of the Leucobacter spp. type strains and the two-member consortium was performed from 2 × 10 10 cells with GenElute Bacterial Genomic DNA Kit (Sigma) as previously described [26].

Physiological characterization of the consortium

The effect of environmental parameters on the abundance of each strain of the consortium was investigated. The effect of temperature was examined by incubating the culture in MMSY at 22 °C, 30 °C and 37 °C. The influence of pH was tested at 30 °C in diluted Lysogeny broth medium (DLB, 25% w/v) with 12 mM of MES (pH 5.5), 12 mM phosphate buffer (pH 7.2) or 12 mM of CAPS (pH 9.5) at 30 °C. The tolerance to NaCl was examined in DLB supplemented with NaCl at final concentrations of 2, 4, 6, 8 or 10% (w/v) at 30 °C. To determine the influence of different standard media in the growth of strain GP, the consortium was incubated in unbuffered R2A, TSA and different dilutions of BHI (5, 25, 50, 75 and 100%). Cultures under all these conditions were incubated at 30 °C for 15 h and carried out in triplicate and in parallel to an abiotic control. The abundance of each strain in the consortium was assessed by quantitative PCR with primers targeting the 16S rRNA gene as previously described [26]. Significant differences (p < 0.05) between overall abundance of strain GP were determined either by two-way ANOVA (to compare 16S rRNA copies/ml of GP and PR1 at different pH, temperature and salinity) or one-way ANOVA (to compare the ratio of the 16S rRNA copies/ml of strains GP and PR1 across different media) and Tukey’s tests using RStudio v 1.1.463 running with R v3.5.2 [33, 99, 100].

Electron microscopy

The consortium was visualized in mid-stationary phase (12 h incubation, MMSY, 0.6 mM SMX) by Cryo-Transmission Electron Microscopy (Cryo-TEM) for morphological characterization. Briefly, a 4 μl aliquot of the overnight grown liquid culture was adsorbed onto a holey carbon-coated grid (Lacey, Tedpella, USA), blotted with Whatman 1 filter paper and vitrified into liquid ethane at − 180 °C using a vitrobot (FEI, USA). Frozen grids were transferred onto a Talos Electron microscope (FEI, USA) using a Gatan 626 cryo-holder (GATAN, USA). Electron micrographs were recorded at an accelerating voltage of 200 kV using a low-dose system (20 to 40 e−/Å2) and keeping the sample at − 175 °C. Defocus values were − 3 to 6 μm. Micrographs were recorded on 4 K × 4 K Ceta CMOS camera. The cell size, and periplasmic and cell wall thickness were measured with Fiji from the ImageJ platform [101]. For Transmission Electron Microscopy (TEM) analyses, 4 μl aliquot of the sample was adsorbed onto a glow-discharged carbon film-coated copper grid, and subsequently negatively stained with 2% uranyl acetate. Images were recorded using Philips CM200FEG electron microscope operating at 200 kV on TemCam-F416 CMOS camera (TVIPS, Germany).

Leucobacter spp. type strains whole-genome sequencing and assembly

High-quality DNA of the selected Leucobacter spp. type strains (Table 1) was used for paired-end sequencing (2 × 150 bp) with the Hiseq 2500 platform (Illumina) by GATC Biotech (Germany). Paired-end reads were adapter and quality trimmed (≥ Q20) with the BBDuk tool from the BBMap package v35.74 [102]. High-quality reads were used for de novo assembly with SPAdes v3.11.1 [103] with the option –careful. Contigs longer than 500 bp were used further extension with SSPACE v3.0 [104] with recommended settings. All data has been deposited in NCBI under the BioProject accession number PRJNA489769.

Whole consortium sequencing

The metagenome of the consortium was sequenced in-house in the Miseq (Illumina) and MinION (Oxford Nanopore Technologies, ONT) platforms. The paired-end Miseq library was prepared from 1 μg of high-quality DNA with KAPA HyperPrep Kit (Kapa Biosystems) and TruSeq DNA PCR-free LT Kit library adaptors (Illumina) with a few modifications. Briefly, enzymatic fragmentation of the genomic DNA was increased to 25 min, and ligation was performed for 2 h at 20 °C. Eight cycles of enrichment PCR and size selection for fragments with approximately 500–700 bp was carried out with NucleoMag magnetic beads (Macherey Nagel). Paired-end sequencing (2 × 250 bp) was performed in an Illumina Miseq system (Illumina) with a V2 MiSeq Reagent Kit (500 cycles). Two independent libraries were prepared for MinION long-read sequencing. Both libraries were prepared from 1.5 μg high-quality DNA sheared with a g-TUBE (Covaris) to approximately 8 kb fragments. The libraries were then prepared with the 1D genomic DNA sequencing kit (SQK-LSK108), pooled, loaded with the Library Loading Bead Kit (EXP-LLB001) and sequenced using a flow cell with R9.4 chemistry (FLO-MIN 106, Oxford Nanopore).

Metagenome-assembled genome (MAG) of strain GP

ONT long reads were adapter trimmed with Porechop v0.2.3 [105]. Illumina paired-end reads were adapter and quality trimmed (≥Q20) with BBDuk from the BBMap package v35.74 [102]. The high-quality paired-end reads were used for hybrid error correction of ONT reads with LoRDEC v0.9 [106]. Resulting long reads were subsequently used for whole-metagenome assembly with Canu v1.7 [107]. Metagenomics contigs were analyzed with SSU finder from CheckM v1.0.11, to determine the amount and affiliation of taxonomic bins present in the metagenome [34]. The metagenome was aligned to the complete genome of strain PR1 (Genbank accession no. CP020917) [35] with BLASTn v2.7.1+ to remove contigs affiliated to the proteobacterium [108] (e-value, identity and hit length threshold cutoffs set to 1e-10, 80 and 30%, respectively). Contigs without significant hits were retrieved from the metagenome and used to construct the new taxonomic bin corresponding to strain GP. Both ONT Illumina-corrected and Illumina reads were used for read binning between strain PR1 and GP with GraphMap v0.5.2 [109] and BWA v0.7.12 [110], respectively. Reads mapping uniquely to strain GP bin were used for hybrid re-assembly with SPAdes v3.7.1 [103]. High-coverage contigs (≥ 1x k-mer coverage) obtained with hybrid assembly were used for further scaffolding and polishing with Circlator v1.5.5 [38] and four iterations with Pilon v1.22 [111]. All data has been deposited in NCBI under the Bioproject accession number PRJNA490017.

Genome annotation, completeness and mobile genetic elements

Quality scores of draft assemblies were assessed with QUAST v4.6.3 [49]. Genome contamination and completeness were determined with CheckM v1.0.11 [34], and tRNA were identified with tRNAscan-SE v2.0 [36]. Open-reading frames (ORFs) were predicted and annotated with NCBI Prokaryotic Genome Annotation Pipeline (PGAP) v4.7 [112] and with RASTtk on the RAST webserver v2.0 [113]. Antibiotic resistance genes were confirmed by aligning amino-acid sequences with BLASTp v2.7.1+ against the Antibiotic Resistance Database (ARDB) v1.1 from July, 2009 [114] and by analyzing the draft genome with the Resistance Gene Identifier (RGI) against the CARD database v3.0.1 [115]. Functional annotation and KEGG Orthology (KO) assignment was further performed with eggNOG-Mapper v4.5.1 [55] and BlastKOALA v2.1 [55]. The presence of plasmids in the genome of strain GP was investigated by assessing differences in coverage and G + C content between contigs, and by further searching for similarities with other plasmids using NCBI BLAST against the non-redundant (nr) database on November, 2018 [116]. The differences in coverage were identified by mapping both Illumina and ONT reads against the metagenome of the consortium (concatenated draft assembly of strain GP and complete genome of strain PR1) with BWA v0.7.12 [110] or Graphmap v0.5.2 [109], respectively. The coverage of sorted BAM files was evaluated with Qualimap v2.2.1 [117]. Genes typically associated with plasmids [44] were identified by aligning amino-acid sequences against the CDD database (v3.17) using the NCBI conserved domain search on with default settings on November, 2018 [118,119,120]. Conjugative elements associated with the type VI secretion systems and possible origins of replication were analyzed with CONJscan v1.0.2 using the Galaxy platform at Pasteur [40,41,42].


Watch the video: Microbiology lecture 8. bacterial identification methods in the microbiology laboratory (January 2022).