Starch is a very abundant and renewable carbohydrate and is an important feedstock for industrial applications. The conventional starch liquefaction and saccharification processes are energy-intensive, complicated, and not environmentally friendly. Raw starch-digesting glucoamylases are capable of directly hydrolyzing raw starch to glucose at low temperatures, which significantly simplifies processing and reduces the cost of producing starch-based products.Penicillium oxalicum GXU20 and biochemically characterized. The PoGA15A enzyme had a molecular weight of 75.4 kDa, and was most active at pH 4.5 and 65 °C. The enzyme showed remarkably broad pH stability (pH 2.0–10.5) and substrate specificity, and was able to degrade various types of raw starches at 40 °C. Its adsorption ability for different raw starches was consistent with its degrading capacities for the corresponding substrate. The cDNA encoding the enzyme was cloned and heterologously expressed in Pichia pastoris. The recombinant enzyme could quickly and efficiently hydrolyze different concentrations of raw corn and cassava flours  with the addition of α-amylase at 40 °C. Furthermore, when used in the simultaneous saccharification and fermentation of 150 g/L raw flours to ethanol with the addition of α-amylase, the ethanol yield reached 61.0 g/L with a high fermentation efficiency of 95.1 % after 48 h when raw corn flour was used as the substrate. An ethanol yield of 57.0 g/L and 93.5 % of fermentation efficiency were achieved with raw cassava flour after 36 h. In addition, the starch-binding domain deletion analysis revealed that SBD plays a very important role in raw starch hydrolysis by the enzyme PoGA15A.A novel raw starch-digesting glucoamylase PoGA15A with high enzymatic activity was purified from P. oxalicum, with high enzymatic activity, was biochemically, molecularly, and genetically identified. Its efficient hydrolysis of raw starches and its high efficiency during the direct conversion of raw corn and cassava flours via simultaneous saccharification and fermentation to ethanol suggests that the enzyme has a number of potential applications in industrial starch processing and starch-based ethanol production.A novel raw starch-digesting glucoamylase from The online version of this article (doi:10.1186/s13068-016-0636-5) contains supplementary material, which is available to authorized users. It is a polymer of glucose, and mainly consists of amylose and amylopectin. Amylose is a mostly linear molecule containing α-Starch is the second most important and abundant source of carbon and energy in plants, and thus has large market demand and many applications in industry. It can be used to produce many valuable food products in the food processing industry, such as maltose, glucose, fructose, glucose–fructose syrups, organic acids, amino acids, etc. . FurtherAlpha-amylase (EC 3.2.1.1) is an endo-acting enzyme that can catalyze the hydrolysis of α-1, 4-glycosidic linkages and some branched α-1, 6-glycosidic linkages from the inner chains of starch. This primarily leads to the release of maltose, smaller oligosaccharides, and dextrin as the main products .Conventionally, the way to convert starch to smaller oligosaccharides and glucose in industry includes energy-intensive liquefaction and saccharification, which mainly involve α-amylase and glucoamylase hydrolysis under high temperature conditions. In the primary liquefaction step, starch is first gelatinized and then liquefied to dextrin and small molecules by a thermophilic α-amylase from a bacterium at high temperature 95–105 °C) and at pH 6.0–6.5. In the following saccharification step, the liquefied starch slurry is cooled and the pH adjusted to 4.0–4.5. Glucoamylase from a fungus is added to further hydrolyze the oligosaccharides to glucose at the lower temperatures of 60–65 °C . Regardl5–105 °C Raw starch-digesting enzymes are enzymes that are able to directly hydrolyze raw starch granules below the starch gelatinization temperature. This significantly reduces the large energy inputs required and simplifies the manufacturing process for starch-based products because there would be no need for liquefaction and saccharification . The eneOnly approximately 10 % of amylolytic enzymes are able to digest raw starch because of the particle size and the densely compacted polycrystalline architecture of natural starch granules. Several types of raw starch-digesting enzymes, such as α-amylase, glucoamylase, and β-amylase, have been isolated from plants, animals, and microorganisms . A largeAspergillus niger [Aspergillus oryzae [Aspergillus awamori [Corticium rolfsii [Penicillium sp. [Aureobasidium pullulans [A. awamori var. kawachi. Two glucoamylases (GA1 and GA2) purified from A. niger showed different raw starch-degrading abilities and GA2 was approximately 6–13 times more active against raw starches than GA1 [Thermomucor indicae-seudaticae was a glycoprotein and acted optimally at pH 7.0 and 60 °C [Aureobasidium pullulans showed optimum activity at 60 °C and pH 4.5, but could only digest raw potato starch even though it possesses various raw starch adsorption abilities [The RSDGs are mainly produced by fungi or yeasts, such as us niger , 15, Asps oryzae , Aspergi awamori , Cortici rolfsii , Penicillium sp. , 20, andullulans . RSDGs hullulans  purifiedthan GA1 . Nagasakthan GA1 . A thermnd 60 °C . An extrbilities . These rPenicillium sp. GXU20 displayed high enzyme activity towards various raw starches. It could effectively hydrolyze 150 g/L of raw cassava flour into glucose as the main product, and a high ethanol yield (53.3 g/L) and fermentation efficiency (92 %) were achieved by simultaneous saccharification and fermentation (SSF) using the crude enzyme after 48 h at 40 °C [In previous studies, we reported that a crude enzyme preparation from Penicillium sp. GXU20 and biochemically characterized. The gene coding for the enzyme was cloned and heterologously expressed in Pichia pastoris. Then the recombinant enzyme was used to study the hydrolysis of raw corn and cassava flours and the simultaneous saccharification and fermentation of the raw flours to ethanol with the addition of α-amylase. This produced a rapid and efficient hydrolysis rate, and a high fermentation efficiency.In this study, a novel RSDG was purified from During enzyme purification, the ability to hydrolyze raw starch was monitored when raw cassava starch was used as a substrate. After ethanol precipitation and two chromatographic separation steps, a raw starch-digesting enzyme was purified to homogeneity on SDS-PAGE and native-PAGE Fig. . The resP. marneffei ATCC 18224 (XP_002149243), and P. oxalicum 114-2 (EPS34453).Trypsin-digested peptide fragments of the purified enzyme were analyzed by liquid chromatography–tandem mass spectrometry (LC/MS/MS) , 26. ThrP. oxalicum GXU20 was a glucoamylase.The hydrolysis products from raw cassava starch by the purified enzyme were separated and identified by HPLC Fig. A. When tP. oxalicum GXU20 was a glucoamylase. The purified enzyme was named PoGA15A.The data from the mass spectrometry analysis of the purified enzyme and the hydrolysis products detected by HPLC confirmed that the purified raw starch-digesting enzyme from C. rolfsii  [Halolactibacillus sp. [T. indicae-seudaticae (42 kDa) [The molecular weight of the purified enzyme PoGA15A was estimated to be approximately 75.4 kDa by SDS-PAGE   and Halollus sp. , but sig(42 kDa) .T. indicae-seudaticae [Cladosporium gossypiicola was used for hydrolysis [Photographs of native and enzyme-hydrolyzed cassava starch granules were detected using scanning electron microscopy (SEM). Figure udaticae . In contdrolysis . The difdrolysis .A. niger (GA2) and C. rolfsii  [Saccharomyces cerevisiae [The effects of pH on enzyme activity were determined using a citrate–phosphate buffer with a pH ranging from 3.0 to 7.0 at 37 °C. Figure  and G3) , 22. Therevisiae  and imprA. niger [Halolactibacillus sp. (70 °C) [The effect of temperature on enzyme activity was measured in a pH 4.5 citrate–phosphate buffer at temperatures ranging from 30 to 80 °C. The optimum temperature occurred at 65 °C and enzymatic activity significantly decreased above 70 °C  .A. awamori var. kawachi [Acremonium sp. endophytic fungus and the marine yeast A. pullulans were only stable between pH 3.0 and 7.0, and 4.0–8.0, respectively [Bacillus sp. and Aspergillus carbonarius have been reported to perform well across pH ranges of 3.0–9.0 and 4.5–11.0, respectively [To investigate the pH stability of RSDG PoGA15A, the enzyme solution was pre-incubated at 4 °C for 24 h in various pH buffers (pH 2.0–11.0) and the remaining enzymatic activity was then assayed. The enzyme was found to be highly stable between pH 2.0 and 10.5 Fig. c, but en kawachi . Moreoveectively . The rawectively , 31, butTo our knowledge, this is the first report of an RSDG with such a broad pH stability. An RSDG that possesses excellent stability over wide pH range should potentially improve enzyme production and storage. PoGA15A can perform at pH conditions that occur during starch hydrolysis with other related amylases, which extends its usage potential and reduces the need for pH adjustment. Therefore, these properties mean that RSDG PoGA15A should have a number of potential applications in the raw starch saccharification and ethanol production industries.The thermal stability of the purified enzyme was tested by incubating it at temperatures ranging from 30 to 80 °C for 1 h. Figure 2+ and Fe2+, but inhibited by Ag+, Cu2+, and SDS. Calcium ions had no obvious influence on enzyme activity. This suggested that PoGA15A was Ca2+-independent, which gives it an industrial application advantage. Potassium and Na+ ions also had little effect on enzyme activity at 10 mM. The Fe2+ ion has been reported to be an activator for an RSDG from T. indicae-seudaticae, but Mn2+ inhibited the enzyme and Ag+ activated it [2+ and Cu2+ ions are activators for an RSDG from Acremonium sp., but Fe2+ had no effect on enzyme activity. However, EDTA could significantly inhibit enzyme activity [2+-independent, which is different from most of the raw starch-digesting glucoamylases [+, and Cu2+, which acted as inhibitors. The results also revealed that none of these ions and chemical reagents were absolutely required for catalytic activity.Metal ions and some chemical reagents  are known to act as activators, inhibitors, or stabilizers. In this study, the enzymatic activity was determined at pH 4.5 and 40 °C in the presence of various metal ions or chemical reagents at a concentration of 10 mM. Additional file vated it . The Mn2activity . It shouamylases , 24, 29.P. oxalicum was determined using soluble starch and a number of raw starches from various sources as substrates. The results are shown in Table A. pullulans N13d was reported to be adsorbed on raw potato, sweet potato, and corn starch, but only raw potato starch could be actively degraded [P. oxalicum also digested raw starches, including rice, buckwheat, corn, and potato. It had the highest enzymatic activity towards rice starch [C. rolfsii and P. oxalicum [Bacillus aquimaris was able to digest raw cassava and corn starches, but had no activity against soluble starches [The substrate specificity of the purified PoGA15A from degraded . Both gle starch . Howeveroxalicum , 32. In starches .Table 2SBacillus amyloliquefaciens with raw starch-digesting ability showed a strong correlation between adsorption to raw starch and hydrolysis. In contrast, a raw starch-digesting glucoamylase from A. pullulans was able to adsorb into raw potato, corn, and sweet potato starches, but only raw potato starch was degraded by the enzyme [PoGA15A exhibited 41–76 % adsorbability onto the different concentrations of raw starch flours. The adsorption rate was the highest for rice and corn starch flours followed by cassava starch flour, but the adsorption rate for potato was relatively low Table . The rate enzyme .Table 3AP. marneffei ATCC 18224 (XP_002149243) and P. oxalicum 114-2 (EPS34453) showed 100 % sequence identity with ALANHKVYTDSFR, IGSITITSTSLAFFK, and YPEDSYYGGNPWFLSNLAAAEQLYDAIYQWNK from the glucoamylase annotated in the unigenes obtained from the P. oxalicum GXU20 transcriptome [P. oxalicum GXU20 transcriptome data and named PoGA15A. The PoGA15A cDNA gene contained 1908 nucleotides, which coded for 635 amino acid residues. The polypeptide without the signal peptide (616 amino acid residues) showed a theoretical isoelectric point of 5.5 and had an approximate molecular weight of 65.4 kDa, which was lower than that of the purified enzyme (75.4 kDa). This indicated that glycosylation may occur during protein expression of P. oxalicum GXU20. BLASTP analysis revealed that the deduced amino acid sequence for the PoGA15A gene showed 99 % identity with a predicted glucoamylase from P. oxalicum 114-2 (EPS34453), 81 % identity with a putative glucoamylase from Talaromyces marneffei ATCC 18224 (XP_002149243), and 81 % identity with a functionally characterized glucoamylase from Talaromyces stipitatus ATCC 10500 (XP_002484948). Furthermore, a phylogenetic analysis of other functionally identified glucoamylases from various sources containing PoGA15A demonstrated that PoGA15A was closely related to the glucoamylase from T. stipitatus (XP_002484948) . The matched amino acid sequences of three internal peptides from criptome . Therefohttp://www.smart.embl-heidelberg.de), and the result showed that the sequences from 1–19, 39–454, and 533–629 belonged to the signal peptide, the catalytic domain of the glycosyl hydrolase family 15, and the starch-binding domain, respectively. A three-dimensional structure of PoGA15A was constructed using the SWISS-MODEL program based on the known crystal structure of the glucoamylase from Hypocrea jecorina (2vn7.1.A), which shared 56.3 % amino acid sequence identity with PoGA15A [The functional domain of the PoGA15A amino acid sequence was analyzed using SMART software , and below 50 °C. The effect of metal ions and chemical reagents, substrate specificity, and raw starch adsorption ability were also very similar to the native PoGA15A enzyme from P. oxalicum GXU20. HPLC analysis of raw starch hydrolysate also showed that only glucose was formed during the hydrolysis of raw starch by the rPoGA15A enzyme. The characteristics of the rPoGA15A enzyme were very similar to the native PoGA15A enzyme, which demonstrated that the rPoGA15A enzyme was correctly expressed in P. pastoris.PoGA15A has a number of advantageous properties and a high capacity for raw starch hydrolysis. Therefore, the cDNA gene encoding the enzyme without the signal peptide was expressed in The recombinant RSDG, rPoGA15A, had a strong ability to degrade raw starches. When raw rice, corn, cassava, and potato flours at a concentration of 10 g/L were hydrolyzed by the rPoGA15A enzyme at 40 °C for 72 h, the degree of hydrolysis for each starch flour was 86.5, 71.9, 30.9, and 14.8 %, respectively (data not shown). This result showed that the hydrolysis capacity of the enzyme towards raw rice and corn starches was higher than for raw cassava and potato starches, which was consistent with the substrate specificity of the native PoGA15A enzyme towards different raw starches.The effects of enzyme dosage on starch hydrolysis were investigated by varying the rPoGA15A enzyme dosages  used to hydrolyze 150 g/L raw corn flour in pH 4.5 citrate–phosphate buffer at 40 °C. After 72 h hydrolysis, the hydrolysis percentages for the enzyme dosages of 0.05, 0.1, 0.2, and 0.5 U/mg raw starch flour were 65.5, 66.5, 67.0, and 70.6 %, respectively. This result showed that different enzyme dosages had no significant effect on raw starch hydrolysis, which indicated that the low enzyme dosage of 0.05 U/mg raw starch could be used for raw starch hydrolysis, and SSF of raw starch to ethanol.Corn starch is largely produced and used as a food or industrial ingredient. It is also widely used to produce glucose–fructose syrup and bioethanol in the United States of America. Furthermore, considerable quantities of cassava starch, which is the most suitable non-grain and starch-rich feedstock, are produced in the subtropical area of China, Southeast Asian countries, Brazil, and South Africa. Therefore, raw corn and cassava starch flours were chosen for the starch hydrolysis and subsequent SSF studies. In order to more effectively hydrolyze raw starch, commercial α-amylase was added to the reaction mixture to act synergistically with the rPoGA15A during the degradation of raw starch.The rPoGA15A could rapidly and effectively hydrolyze raw corn and cassava flours with its synergetic action of commercial α-amylase. Figure The rPoGA15A enzyme or commercial α-amylase alone could not sufficiently and quickly hydrolyze raw cassava starch at the different concentrations of raw cassava flour, and reached no more than 37 % hydrolysis after 96 h degradation (data not shown). When both the rPoGA15A enzyme and α-amylase were used to hydrolyze the raw cassava starch, the hydrolysis efficiency significantly improved. Figure Penicillium sp. X–1 significantly hydrolyzed 15 % (w/v) of raw corn starch slurry after it was supplemented with commercial α-amylase at 65 °C and pH 6.5, and achieved much higher hydrolysis levels (92.4 %) after 2 h compared to using the enzyme alone [Aspergillus awamori KT-11, and thought that GA I initially degraded the starch granule surface to form many holes and released glucose as a product. Then Amyl III could adsorb to the holes to further digest the starch granule producing large oligosaccharides as the main hydrolysis products [The synergistic use of the rPoGA15A and commercial α-amylase remarkably improved raw starch hydrolysis. Similarly, an RSDG from me alone . Matsubame alone  investigproducts . Raw corproducts . This reproducts .P. oxalicum GXU20 after 72 h incubation at 40 °C [Laceyella sacchari LP175 bacteria at 50 °C after 12 h and reached 36.8 % hydrolysis. The hydrolysis was synergistically improved and reached up to 70 % after the addition of commercial glucoamylase [Microbacterium aurum could digest 5 % raw cassava starch at 37 °C and 76 % hydrolysis was obtained after a long incubation time of 148 h [To our knowledge, the hydrolysis performance of rPoGA15A with α-amylase at the relatively low temperature of 40 °C was significantly better in terms of hydrolysis speed and hydrolysis percentage than the activities reported by other studies using raw starch-digesting enzymes. A 95 % hydrolysis of 150 g/L raw cassava starch slurry was realized by the crude enzyme from at 40 °C . Li et aat 40 °C  also repoamylase . An extrRaw corn flour and raw cassava flour were used as fermentation substrates to investigate the SSF of raw starch to ethanol by rPoGA15A and commercial α-amylase. The SSF was conducted at 40 °C, and the concentrations of produced glucose, residual starch, and ethanol were measured at 12 h intervals. Figure The SSF of raw cassava flour to ethanol also occurred rapidly and successfully. Figure The SSF of raw cassava flour to ethanol was accomplished after 36 h, which was earlier than that of raw corn flour (48 h). The reason for this result might be the mutual coordination between the enzyme saccharification of starch and ethanol fermentation. Raw corn starch was hydrolyzed to glucose more quickly than the rate of yeast fermentation, and thus a large amount of glucose remained in the mixture, especially at 12 h. This might lead to inhibition of the following ethanol fermentation step due to the high glucose concentration, which may be eliminated by reducing the amount of enzyme used. However, enzyme saccharification of raw cassava starch matched ethanol fermentation, so very low amounts of glucose were present during the fermentation process and a more rapid fermentation speed was achieved compared to corn starch.A. niger and a yeast for the SSF of raw corn starch, and obtained a high fermentation efficiency of 95.9 %. The fermentation efficiency was very similar to this study (95.1 %), but a longer fermentation time (72 h) was needed for the SSF of raw corn starch to ethanol than our study (48 h). The fermentation efficiency of raw cassava starch in this study was higher than in previous studies, and the fermentation time was also shorter  at 40 °C. Table  at 40 °COnly about 10 % of the amylolytic enzymes possessing a distinct SBD are capable of raw starch degradation . CurrentCryptococcus sp. S-2 also has raw starch-digesting activity. Nevertheless, the mutant AMY-CS2 α-amylase lacking an SBD lost not only its capacity for raw starch degradation and adsorption, but also its thermal stability [A. niger was added to a glucoamylase from S. cerevisiae without an SBD, and thus conferred the hybrid enzyme capabilities for substrate binding and insoluble starch hydrolysis [An α-amylase (AMY-CS2) from yeast tability . Conversdrolysis . TherefoP. oxalicum GXU20 and biochemically characterized. Its cDNA was cloned and heterologously expressed in P. pastoris. The PoGA15A enzyme was most active at pH 4.5 and 65 °C. It showed remarkable stability over a wide pH range (2.0–10.5), and the enzymatic activity was not adversely influenced by most of the metal ions and chemical reagents tested. The enzyme showed broad substrate specificity against raw starches and could quickly and efficiently hydrolyze raw corn and cassava flours at different concentrations with the addition of α-amylase. The SSF of raw corn and cassava flours to ethanol was rapidly and efficiently accomplished by the rPoGA15A enzyme with the addition of α-amylase. Analysis of a mutant rPoGA15A enzyme that lacked an SBD revealed that the SBD was mainly responsible for the high raw starch degradation capacity of the rPoGA15A enzyme. This study has improved understanding of a novel RSDG, and its excellent properties mean that the enzyme has great potential in the starch hydrolysis and ethanol production industries.A novel raw starch-digesting glucoamylase PoGA15A displaying a high capacity for raw starch degradation was purified from ® AA XP) was obtained from Novozymes . All the chemicals used were of analytical grade and were obtained from commercially available sources.Hiprep 16/10 phenyl and Source 15S 4.6/100 PE were purchased from GE Healthcare Life Sciences . Raw starches  were purchased from a local market in Nanning, China. The raw starch substrates used for enzymatic activity assay were washed four times with ultra-pure water. The starch contents of raw flours were determined following a method previously described by Lin et al. . The staPenicillium sp. GXU20 was a fungal strain previously isolated from forest soil taken from Shiwandashan Mountain in Guangxi Zhuang Autonomous Region of China [Penicillium oxalicum based on the β-tubulin gene, the ITS sequence, and its morphological characteristics [8 spores/mL) was added to 150 mL of culture medium in a 500 mL Erlenmeyer flask. The culture medium used for enzyme production was composed of (w/v): 3.5 % wheat bran, 2.5 % soybean meal, 0.3 % KH2PO4, 0.02 % MgSO4·7H2O, 0.00255 % FeSO4·7H2O, and 0.013 % CaCl2. The medium was adjusted to pH 5.5. The cultures were maintained at 28 °C on a rotary shaker at 180 rpm. After 6 days of incubation, the culture medium was centrifuged at 4 °C and 4000×g for 20 min and the supernatant was then used for enzyme purification.eristics . The GXUEscherichia coli DH5α and P. pastoris GS115 strains were used for gene cloning and heterologous gene expression, respectively. A thermo-resistant dried yeast (Saccharomyces cerevisiae) purchased from the Angel Yeast Co., Ltd.  was used for the simultaneous saccharification and fermentation of raw starch to ethanol.g, 20 min), more ethanol was added to bring the supernatant to 67 % saturation. The solution was stored overnight at 4 °C and then centrifuged. The precipitate was dissolved in 50 mM phosphate buffer containing 1.5 M ammonium sulfate (pH 7.0) and then filtered through a 0.45 µm membrane filter.The culture supernatant was obtained by centrifuging as described above. Pre-chilled ethanol was added to the supernatant over ice until it reached 33 % saturation. After the removal of precipitation by centrifugation . The sample was loaded onto a Hiprep 16/10 phenyl column  equilibrated with 50 mM phosphate buffer containing 1.5 M (NH4)2SO4 at pH 7.0. The column was washed with equilibration buffer until no absorbance at 280 nm was observed in the eluent. The proteins were then eluted using (NH4)2SO4 (400 mL) across a linear concentration gradient of 1.5–0 M in the same buffer at a flow rate of 4 mL/min. Then, 5 mL fractions were collected and assayed for enzyme activity.The purification of the raw starch-digesting enzyme from Fractions with raw starch-digesting enzyme activity were pooled, concentrated by ultrafiltration in an Amicon Ultra-15 , and dialyzed against 25 mM sodium acetate buffer (pH 4.0). The sample was then loaded onto a source 15S 4.6/100 PE column  pre-equilibrated with 25 mM sodium acetate buffer at pH 4.0. The adsorbed enzyme was eluted using a linear NaCl concentration gradient (0–1 M) in the same buffer with a 1 mL/min flow rate. Protein purity was analyzed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). The purified active fractions were collected and used for further analysis.The raw starch-digesting enzyme activity was determined by measuring the reducing sugars released during raw starch hydrolysis. The reaction mixture, containing 450 μL of 1 % raw cassava flour in 0.1 M citrate–phosphate buffer (pH 4.5) and 50 μL enzyme solution, was incubated at 40 °C for 30 min and the reaction was stopped by heating for 10 min in boiling water. A control sample was incubated for 10 min in boiling water to inactivate the enzyme and incubated under the same conditions as above. The reducing sugar content was measured using the dinitrosalicylic (DNS) acid method . One unig for 5 min and was then incubated for 10 min in boiling water. The precipitate containing the raw cassava granules was washed with pure ethanol three times followed by drying at 35 °C to a constant weight. It was then used for the SEM study [The reaction mixture, containing 0.3 mL of purified enzyme solution (6 U) and 1.5 mL of 2 % raw cassava flour resuspended in pH 4.5 citrate–phosphate buffer, was incubated at 40 °C. An aliquot of the reaction mixture (300 μL) was removed at time intervals of 0.5, 2, 4, and 8 h. The supernatant used for hydrolysis product analysis was obtained by centrifugation at 12,000×EM study .The reaction products were analyzed by high-performance liquid chromatography (HPLC) on an instrument equipped with a refractive index detector as described previously  . A carboProtein concentration was determined by the bicinchoninic acid (BCA) method using the Pierce Protein Assay Kit from Thermo Fisher Scientific  . Bovine 2 reagent as described previously [SDS-PAGE (sodium dodecyl sulfate-polyacrylamide gel electrophoresis) analysis was performed using 12 % polyacrylamide to determine protein purity and the molecular mass of the purified enzyme as described previously . Proteineviously .The effect of pH on enzyme activity was measured at 37 °C in 0.1 M citrate–phosphate buffer at pH values ranging from 3.0 to 7.0. The effect of temperature on enzyme activity was investigated at temperatures ranging from 30 to 80 °C in citrate–phosphate buffer (pH 4.5). The pH stability was determined by pre-incubating the purified enzyme at various pHs (2.0–11.0) at 4 °C for 24 h and the residual enzyme activity was measured under standard conditions as described above. The buffers (0.1 M) used were citrate–phosphate buffer (pH 2.0–7.0), phosphate buffer (pH 7.0–8.0), Tris–HCl (pH 8.0–9.0), and glycine–NaOH (pH 9.0–11). Thermal stability was determined by pre-incubating the purified enzyme in the citrate–phosphate buffer (pH 4.5) at different temperatures (30–80 °C) for 1 h, and the residual enzyme activity was determined under standard assay conditions.+, Na+, Mg2+, Ca2+, Ni2+, Zn2+, Li+, Mn2+, Cu2+, Fe2+, Fe3+, Co2+, Ag+, EDTA, SDS, Tween 80, and Triton X-100) at a final concentration of 10 mM. Enzyme activity assayed in the absence of any additives was taken as 100 %.The influence of various metal ions and chemicals on the enzyme activity was determined by measuring enzyme activity under standard conditions in the presence of various metal ions and chemical reagents  as substrates. Enzyme activity against different substrates was performed in a reaction mixture containing 1 % of each starch flour in citrate–phosphate buffer (pH 4.5) at 40 °C for 30 min. The enzyme activity towards raw cassava starch was taken as 100 %.g for 5 min, the enzyme activity of the supernatant was determined as described above. The percentage adsorption was calculated according to the following equation: Adsorption (%) = (A−B) × 100/A, where A is the enzyme activity of the control without raw starch and B is the residual enzyme activity in the supernatant after adsorption.To determine the adsorption of the enzyme onto raw starch, 50 μL of the purified enzyme solution was mixed with 450 μL of citrate–phosphate (pH 4.5) containing 1 or 5 % of each raw starch flour. The mixture was incubated at 40 °C for 30 min with occasional shaking. After centrifugation at 8000×P. oxalicum GXU20 under the culture conditions mentioned above had been obtained from a previous study [The purified enzyme bands were excised from the SDS-PAGE gels. The proteolytic peptides were analyzed using a nanoAcquity UPLC system coupled with a LTQ Orbitrap XL ETD mass spectrometer (Thermo Fisher Scientific) at the Institute of Biomedical Sciences of Fudan University . The transcriptome sequencing data for us study .P. oxalicum GXU20 by combining the results of the purified enzyme’s mass spectrometric analysis with the obtained transcriptome sequencing data for P. oxalicum GXU20 [The complete open reading frame encoding the enzyme was identified in the transcriptome unigenes of um GXU20 , 65.P. oxalicum GXU20, and cDNA was synthesized using a PrimeScript™ 1st Strand cDNA Synthesis Kit , following the manufacturer’s instructions. Based on the full-length sequence obtained from the transcriptome data, the cDNA gene was amplified by PCR using cDNA as the template and the forward primer (5′-ATGTCTCGACTTCTCTACGCAC-3′) and reverse primer (5′-TTAGCGCCAGGTGTCGTTCT-3′). PCR conditions were as follows: 95 °C, 5 min, followed by 30 cycles of 60 s at 95 °C, 30 s at 54 °C, 60 s at 72 °C, and finally, a 10 min extension at 72 °C. The PCR products were sequenced and the amplified sequences showed 100 % identity with the sequences obtained from the transcriptome data. The cDNA coding the PoGA15A enzyme was analyzed and the signal peptide sequence of the enzyme was identified using SignalP 4.1 software  [PoGA15A gene in P. pastoris, the PoGA15A gene without the sequence coding for the signal peptide underwent codon optimization by Generay Biotech Co., Ltd. . The optimized sequence with the addition of AvrII and NotI restriction sites, and the sequence coding 6× His-tag was also synthesized. The synthesized fragment was double digested with AvrII and NotI (Takara) and ligated into the AvrII and NotI sites of the pPIC9k vector , where it was fused in-frame with the α-factor signal peptide from P. pastoris. The resulting plasmid, pPIC9k-PoGA15A, was transformed into E. coli DH5α competent cells, and sequencing was performed to verify the construct by Shenzhen Huada Gene Technology Co., Ltd. . The pPIC9k-PoGA15A vector was linearized with SacI, and transformed into P. pastoris GS115 (Invitrogen) by electroporation using a Gene Pulser Xcell  following the manufacturer’s instructions. Transformants were screened based on their ability to grow on minimal dextrose (MD) medium plates, and then inoculated onto yeast extract peptone dextrose (YPD) medium plates. The cultures were grown in the presence of increasing concentrations of Geneticin G418  to screen for multicopy gene recombinants. They were then incubated for 3 days, and integration of the PoGA15A gene into the P. pastoris GS115 genome was confirmed by PCR using 5′ AOX1 and 3′ AOX1 primers [600 reached 10. Cell pellets were harvested by centrifugation at 1000×g for 5 min, resuspended in 40 mL of BMMY medium with 1 % methanol (v/v), and then incubated at 28 °C with shaking at 250 rpm. Methanol was added to make a final concentration of 1 % every 12 h to maintain the induction. The P. pastoris GS115 strain transformed with the empty vector was used as a control for background expression analysis. Enzymatic activity from the culture supernatant was detected using raw cassava as the substrate and following the method described above. The recombinant protein in the extracellular supernatant of the expression culture media was first purified by affinity chromatography with nickel–nitrilotriacetic acid agarose resin . Then the secreted and purified proteins were analyzed by SDS-PAGE. The purified recombinant glucoamylase with high raw starch degradation abilities was also biochemically characterized according to the methods previously mentioned above.Total RNA was extracted from the mycelia of ignalP/) . In orde primers . Yeast tP. pastoris expression supernatant, and commercial α-amylase were added to 0.05 U per mg of each raw starch flour. The reaction mixture, with a total volume of 1.0 mL, was incubated at 40 °C with shaking at 150 rpm. After incubation of the mixtures, samples were withdrawn at various time intervals and centrifuged for 5 min at 12,000×g. The reducing sugar content of the supernatant was determined by the DNS method using glucose as the standard. The percentage in hydrolysis of raw starch was defined by the following formula: Rh (%) = (A1/A0) × 0.9 × 100, where A1 was the amount of reducing sugar in the supernatant after enzymatic hydrolysis and A0 was the amount of raw starch before the reaction [Different concentrations of the raw corn or cassava flour suspensions  were prepared in citrate–phosphate buffer (pH 4.5), and the rPoGA15A enzyme solution, concentrated from the reaction .60 radiation at 20 kGy. The fermentation system comprised 150 g/L of raw starch flour, rPoGA15A (0.05 U/mg raw starch flour), commercial α-amylase (0.05 U/mg raw starch flour), 3 g/L urea, and 1 g/L yeast. The mixture (20 mL) was adjusted to pH 4.0 by 2 M HCl and incubated in 50 mL sterilized conical flasks at 40 °C without agitation. Then, 1.5 mL aliquots of the slurry were withdrawn at 12 h time intervals during the fermentation period and centrifuged at 12,000×g for 5 min. The residual raw starch contents of the precipitate were measured by a method described previously [® HPX-87H ion exclusion column  was used to separate the compounds at 60 °C in a 5 mM H2SO4 mobile phase with a 1 mL/min flow rate. Each analysis was conducted in duplicate. The fermentation efficiency was calculated based on the ethanol yield produced during the fermentation process and the theoretical ethanol yield, following the equation: Fermentation efficiency (%) = produced ethanol (g) × 100/theoretical yield (g), where the theoretical ethanol yield was calculated based on the assumption that 0.567 g of theoretical ethanol yield can be produced from 1 g of starch [SSF experiments were conducted using raw corn and cassava flours without the high temperature cooking and enzyme liquefaction pretreatments. In order to prevent microbial contamination in SSF, the raw corn and cassava flours were sterilized by Cof starch , 70.PoGA15A glucoamylase gene was cloned and heterologously expressed in P. pastoris using the methods described above. The expression culture supernatant was obtained by centrifugation after harvest. The recombinant protein was purified using nickel-affinity chromatography and analyzed by SDS-PAGE. The specific activities of the purified enzyme lacking a SBD towards various kinds of starches were determined by comparing it to the wild-type enzyme rPoGA15A under the standard assay conditions mentioned above.To investigate the effect of the starch-binding domain (SBD) on the activity of the rPoGA15A enzyme towards raw starch, the SBD-deleted P. oxalicum GXU20 was submitted to the GenBank database with assigned accession number KX434574.The nucleotide sequence of the cDNA encoding the RSDG PoGA15A enzyme from


Arterial stiffness predicts an increased risk of cardiovascular events. Inflammation plays a major role in large arteries stiffening, related to atherosclerosis, arteriosclerosis, endothelial dysfunction, smooth muscle cell migration, vascular calcification, increased activity of metalloproteinases, extracellular matrix degradation, oxidative stress, elastolysis, and degradation of collagen. The present paper reviews main mechanisms explaining the crosstalk between inflammation and arterial stiffness and the most common inflammatory markers associated with increased arterial stiffness, considering the most recent clinical and experimental studies. Diverse studies revealed significant correlations between the severity of arterial stiffness and inflammatory markers, such as white blood cell count, neutrophil/lymphocyte ratio, adhesion molecules, fibrinogen, C-reactive protein, cytokines, microRNAs, and cyclooxygenase-2, in patients with a broad variety of diseases, such as metabolic syndrome, diabetes, coronary heart disease, peripheral arterial disease, malignant and rheumatic disorders, polycystic kidney disease, renal transplant, familial Mediterranean fever, and oral infections, and in women with preeclampsia or after menopause. There is strong evidence that inflammation plays an important and, at least, partly reversible role in the development of arterial stiffness, and inflammatory markers may be useful additional tools in the assessment of the cardiovascular risk in clinical practice. Combined assessment of arterial stiffness and inflammatory markers may improve non-invasive assessment of cardiovascular risk, enabling selection of high-risk patients for prophylactic treatment or more regular medical examination. Development of future destiffening therapies may target pro-inflammatory mechanisms. The elasticity and distensibility of arteries maintain a relatively constant blood pressure, despite the pulsating nature of the blood flow by every heartbeat. Arteries expand by receiving blood ejected from the heart during systole and expel it to the periphery during diastole to supply the peripheral circulation with a steady flow of blood during both cardiac cycles . HoweverArterial stiffness is considered a growing epidemic, associated with an increased risk of cardiovascular events –8. It isLocal inflammation is a complex non-specific protective response of vascular tissue to injury in order to eliminate its cause. Atherosclerosis is considered an inflammatory disease, because low-grade inflammation contributes to all phases of atherosclerosis development, starting with the initial phase of endothelial dysfunction, up to the formation of the mature atherosclerotic plaque, disruption of atherosclerotic injuries, precipitation of plaque rupture, and the acute thrombotic complications of atheroma , 15–19. Both markers of inflammation and arterial stiffness are predictors of cardiovascular events . UnderstAlso, systemic inflammation has been shown to increase the risk of cardiovascular disease by accelerating atherosclerosis, destabilizing plaques, impairing endothelial function, or causing premature arterial stiffness –23. ConsIt is the aim of the present paper to highlight the role of inflammation on large vessels in view of arterial stiffness, review the main mechanisms explaining the crosstalk between inflammation and arterial stiffness and the most common inflammatory markers associated with an increased arterial stiffness, considering the most recent clinical and experimental studies published in biomedical research. Understanding the role of inflammation in the pathophysiology of arterial stiffness is crucial in order to enable development of new therapies by modulation of inflammatory pathways, which have been identified as major targets for the treatment of arterial stiffness.Cardiovascular risk factors induce a state of inflammation able to impair vascular function . Activatimpairs endothelial function and the mechanical properties of the arteries (Atherosclerosis is a syndrome caused due to chronic inflammatory interactions of white blood cells (WBCs) in the wall of arteries, and therefore, plasma inflammatory markers are considered potential tools for cardiovascular risk prediction , 25, 26.arteries . Endothearteries , 3, 29. arteries . Additioarteries , 15.Antigen-presenting cells such as dendritic cells and effector T lymphocytes play an important role in the synthesis of proatherogenic cytokines, such as IL-2, IL-18, and interferon gamma, and are therefore also important in atherosclerotic plaque progression . A specivia scavenger receptors or lectin-like oxidized low-density lipoprotein receptor 1 (LOX-1). In this context, it has been shown that overexpression of SIRT1 is able to decrease LOX-1 expression and prevent foam cell formation in a mouse model of atherogenesis regardless of serum lipid levels. The underlying mechanism for this effect was shown to rely on suppression of NFκB-signaling by deacetylating RelA/p65, thereby reducing LOX-1 expression and diminishing uptake of oxLDL and foam cell formation and consequently also arterial stiffness  tiffness . Thereforeceptor , and alsreceptor .enabling vascular fibrosis and smooth muscle cell proliferation , and cytokines, such as IL-1, IL-6, and IL-17   . MMP is embranes . The incembranes , 42. IncSystemic subclinical low-grade inflammation is closely related to most cardiovascular risk factors, especially to hypertension and diabetes, to all stages of atherosclerosis, to arteriosclerosis and to impaired arterial elastic properties , 43, 44.Pulse wave velocity inversely correlates with arterial distensibility, and an impaired arterial distensibility alters blood pressure, flow dynamics, increases afterload, and impairs coronary perfusion . ConcludAn increased WBC has been associated with arterial stiffness and other atherosclerotic events in multiple studies . ChronicThe distribution of WBC subtypes is regulated by the autonomic nervous system since lymphocytes have cholinergic receptors and granulocytes have adrenergic receptors , 51. Conhematologic malignancies and solid tumors  and monocyte count only in women, and between neutrophil count and intima-media thickness (IMT) in men, in a study including 500 subjects with ate risk . Gender ate risk .familial Mediterranean fever (FMF), which is an autosomal recessive disorder restricted to certain ethnic groups, characterized by recurrent inflammatory febrile attacks of the serosal and synovial membranes; the ongoing and recurring inflammatory state is associated with an increased cardiovascular risk and an elevated arterial stiffness (via PWV) was correlated with the severity of inflammation and several inflammatory markers, including WBC, CRP, ESR, fibrinogen, and neutrophil/lymphocyte ratio (NLR), and not to the genetic mutation  are a group of cytokines produced by cells of the immune system and have a pro-inflammatory effect. They trigger the acute phase reaction by continued recruitment and activation of leukocytes, and stimulate proliferation of fibroblasts . An incrpreeclamptic pregnancies and systemic arterial properties 6 months post-partum, although those markers were increased also at term. Even at term, no general correlation between the increase of systemic and vascular inflammatory markers and systemic arterial properties could be established , as a positive correlation between PWV, augmentation indices, and ILs are reported in a single study with 108 subjects (53 patients and 55 controls)  .chronic kidney disease  polymorphism among men aged 34–49 years in a study examining polymorphisms in several inflammatory genes . CD14 acIn summary, ILs and other cytokines have been associated with arterial stiffness in several disorders, such as metabolic syndrome, SpA, and preeclampsia Table  79, 82,,82, 83.C-reactive protein is an acute phase reactant that predicts cardiovascular events in healthy subjects and in patients with preexisting cardiovascular disorders , 90. CRPThe level of CRP has been associated with indices of arterial function in several populations , 97–100.after renal transplant, and overall CRP was suggested to be a useful marker to anticipate graft survival and cardiovascular morbidity in renal transplant recipients (A significant correlation was reported between aortic flow propagation velocity (AVP), which is a parameter of reduced arterial stiffness, assessed by transthoracic echocardiography, and high CRP levels, which is indicating a link between aortic stiffness and inflammation . Severalcipients .Rheumatoid arthritis is a chronic, systemic, inflammatory disease, associated with an increased cardiovascular mortality . PWV corVascular hsCRP production is stimulated by cytokines (IL-6 and IL-1) and has modulatory functions by inhibiting endothelial NO synthase and inducing the expression of adhesion molecules in ECs. hsCRP has also a major role in increasing cytokines expression and generation of reactive oxygen species by monocytes and neutrophils, promoting vasoconstriction, VSMC migration and proliferation, activation of platelets, and vascular stiffness , 43, 104C-reactive protein and hsCRP were associated with arterial stiffness in patients with metabolic syndrome, renal transplant, diabetes mellitus, and RA Table  7, 21, , 104. MaSoluble cell adhesion molecules include intercellular adhesion molecule-1 (ICAM-1), vascular cell adhesion molecule-1 (VCAM-1), the platelet (P-selectin), and endothelial selectin (E-selectin) . AdhesioSelectins are C-type lectins, including L-selectin expressed on leukocytes, E-selectin expressed by cytokine-activated ECs and P-selectin which is expressed by platelets and ECs . E-selecperipheral arterial disease and 34 controls and found a significantly reduced endothelial function index, an increased augmentation index (AI), estimated PWV, ICAM-1, hsCRP, myeloperoxidase, and urinary 8-iso-prostaglandin F2a  is associated with cell membranes  but also found as soluble VAP-1 in plasma. It serves both the function as an adhesion molecule and an amine oxidase producing aldehyde and hydrogen peroxide. It is involved in vascular injury due to its semicarbazide-sensitive amine oxidase (SSAO) activity because of releasing of formaldehyde and methylglyoxal from the breakdown of primary amines, which is responsible for direct cytotoxic damage to ECs , 112. VAfounders , 114. VAsystemic lupus erythematosus, antiphospholipid antibodies bind to receptors on the endothelium to upregulate adhesion molecules such as E-selectin, ICAM-1, and VCAM-1, but the presence of the antibodies was not associated with arterial stiffness (In tiffness .In summary, adhesion molecules were associated with arterial stiffness in patients with a peripheral arterial disease, resistant hypertension, and subjects over 60 years Table  34, 110110.The association between oral inflammation and the risk of major cardiovascular events, such as myocardial infarction and stroke, was described two decades ago , 116. BaPeriodontitis is associated with endothelial dysfunction due to periodontal pathogens, atherosclerosis, an increased risk of myocardial infarction, stroke, and peripheral arterial disease , 119. A Porphyromonas gingivalis and Treponema denticola, bacteria causing periodontal disease, have been found in the atherosclerotic plaque (Microbial pathogens associated with periodontal disease, predominantly Gram-negative bacteria, cause high levels of bacteremia after routine dental procedures and every day activities, including tooth brushing  and a chc plaque . The levc plaque . High pec plaque , 126, anc plaque . Probablc plaque . It is ac plaque .Several studies reported an increased arterial stiffness in patients with periodontitis compared to controls, suggesting that patients with periodontitis suffer from a subclinical vascular dysfunction Table  129–134134. FutuConcluding, arterial stiffness is impaired in patients with periodontitis. No study reported yet a symbiotic relationship between periodontitis and arterial stiffness or any relationship between odontogenic foci and arterial stiffness, and those could be aims of future studies.Increasing evidence indicate that miRNAs, which are small non-coding RNAs, able to regulate gene expression and to prevent or reduce protein synthesis, have distinct profiles and play crucial roles in various physiological and pathological processes, including vascular aging and inflammation –140. An It is too early to consider microRNAs as diagnostic and prognostic biomarkers or therapeutic target for arterial stiffness, because there are very few studies linking vascular function and non-coding RNAs. However, miRNAs could be useful as markers of vascular remodeling and plaque instability in future studies.During inflammation, phospholipase brakes down phospholipids of the WBC membrane into arachidonic acid, which enables synthesis of prostaglandins through the cyclooxigenase pathway, involving cyclooxygenase 1 and 2. The prostaglandins formed by the COX-2 pathway perpetuate inflammation and amplify the effects of other inflammatory mediators, due to its upregulation in monocyte-derived macrophages from the atherosclerotic lesions , 151. MiThe relationship between COX-2 and vascular remodeling was described in the pulmonary circulation. Pulmonary arterial stiffness occurs early in experimental pulmonary hypertension and is an independent risk factor for mortality in pulmonary hypertension . COX-2 iCyclooxygenase-2 derived prostaglandin E2, acting on EP1 receptors, is responsible for increased arterial stiffness, increased vasoconstrictor responses, extracellular matrix deposition, endothelial dysfunction, and vascular inflammation . AII indOn the other hand, Vlachopoulos et al. demonstrated that, especially, COX-1, and also COX-2 mediate the unfavorable effects of smoking on arterial stiffness . EndotheDespite the mechanisms linking COX-2, inflammation, and arterial stiffness, chronic COX-2 inhibition is not an effective destiffening solution, because of its prothrombotic and hypertensive undesirable effects. There is urgent need for the discovery of new effective and cardiovascular safe anti-inflammatory drugs, and mPGES-1 inhibitors may be the solution.Most of the studies were cross-sectional, often with only a relatively small number of participants, and sometimes with just one measurement of inflammatory markers, all of which is unfavorable for the significance of results and their interpretation. The cross-sectional design of most studies cannot establish causality, it can only establish an association . RelativSeveral confounding factors may influence the level of inflammatory markers and not all of them are known currently. The level of soluble adhesion molecules is, for example, at least influenced by age, smoking status, diabetes mellitus, other inflammatory conditions, exercise, and changes in blood pressure . The varvia the brachial PP using a sphygmomanometer and are then converted into central PP by a transfer function. However, since vascular dimensions depend on body size and vascular properties vary with arterial pressure, age, or treatment, the premise that the characteristics of the vascular system between the two measuring sites are the same in all individuals and under all conditions is therefore not true (Also, the methodologies of assessing arterial stiffness are limiting the significance of the obtained results. For example, PWV measurements have the immanent problem that it remains difficult to accurately record the femoral pressure wave in participants with peripheral artery disease, and also obesity affects the absolute value of PWV by overestimating the distance and thus yielding artificial values . Also, mnot true . More sonot true , 92.There is ample evidence of a crosstalk between arterial stiffness and systemic inflammation, and inflammation plays an important role in the development of arterial stiffness. Inflammatory markers may be useful additional tools in the assessment of the cardiovascular risk, atherosclerotic plaque remodeling, and preclinical atherosclerotic changes in clinical practice and may be used to develop risk scores for possible future cardiovascular events. This might help to close the currently existing gap between predicted cardiovascular events and their real prevalence. Most of the inflammatory markers are inexpensive and easily measurable, widely available, standardized and may be included in the annual examination of patients at risk, considering that inflammation causes a reversible increase of arterial stiffness.Several studies revealed significant associations between arterial stiffness and inflammatory markers, such as WBC, NLR, adhesion molecules, fibrinogen, CRP, hsCRP, cytokines, microRNAs, and COX-2 in patients with metabolic syndrome, diabetes mellitus, coronary heart disease, systemic and pulmonary hypertension, peripheral arterial disease, malignant and inflammatory rheumatic disorders, polycystic kidney disease, renal transplant, FMF, in women with preeclampsia or after menopause, and in patients with severe periodontitis. Further studies are needed, in several other disorders and healthy subjects, to confirm the reversible association linking arterial stiffness and inflammatory markers and its predictive value for cardiovascular events.Combined assessment of arterial stiffness and inflammatory markers may improve non-invasive assessment of cardiovascular risk in several disorders, enabling selection of high-risk patients for prophylactic treatment or more regular medical examination. Development of future destiffening therapies may target pro-inflammatory mechanisms, including miRNAs, enabling stabilization of the atherosclerotic plaque, control of cardiovascular risk factors, and inflamm-aging.IM has written the first draft of the manuscript. CM, JH, CG, DS, CL, and AA revised and improved the first draft. All authors have seen and agreed on the finally submitted version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Pseudomonas aeruginosa and Acinetobacter spp. are found to be associated with biofilm and metallo-β-lactamase production and are the common causes of serious infections mainly in hospitalized patients. So, the main aims of this study were to determine the rates of biofilm production and metallo beta-lactamase production (MBL) among the strains of Pseudomonas aeruginosa and Acinetobacter spp. isolated from hospitalized patients.P. aeruginosa isolates and 50 Acinetobacter spp. isolates isolated from different clinical specimens from patients admitted to Shree Birendra Hospital, Kathmandu, Nepal from July 2013 to May 2014 were included in this study. The bacterial isolates were identified with the help of biochemical tests. Modified Kirby-Bauer disc diffusion technique was used for antimicrobial susceptibility testing. Combined disc diffusion technique was used for the detection of MBL production, while Congo red agar method and tube adherence method were used for detection of biofilm production.A total of 85 P. aeruginosa isolates and 22% of the strains of Acinetobacter spp. were metallo β-lactamase producers. Out of 85 P. aeruginosa isolates, 23 (27.05%) were biofilm producers according to tube adherence test while, only 13 (15.29%) were biofilm producers as per Congo red agar method. Similarly, out of 50 Acinetobacter spp. 7 (14%) isolates were biofilm producers on the basis of tube adherence test, while only 5 (10%) were positive for biofilm production by Congo red agar method. Highest rates of susceptibility of P. aeruginosa as well as Acinetobacter spp. were seen toward colistin.Around 16.4% of Pseudomonas aeruginosa and Acinetobacter spp. However, no statistically significant association could be established between biofilm production and metallo beta-lactamase production.In our study, biofilm production and metallo beta-lactamase production were observed among  Pseudomonas aeruginosa and Acinetobacter spp. are the common causes of life threatening infections mainly in hospitalized patients [Pseudomonas aeruginosa and Acinetobacter spp. Further, we determined the antimicrobial susceptibility patterns of these organisms.With the emergence of carbapenemase  producing bacterial strains, the clinical utility of carbapenem as reserve drug is under threat . Such bapatients . In addiP. aeruginosa isolates and 50 Acinetobacter spp. isolates, isolated from different clinical specimens from patients admitted to Shree Birendra Hospital, Kathmandu, Nepal from July 2013 to May 2014 were included in this study. The bacterial isolates were identified on the basis of the microbiological procedures as described in the Bergey’s manual of determinative bacteriology and were further evaluated for MBL production by combined disk diffusion method using imipenem and imipenem/ethylenediaminetetraacetate discs. Similarly, biofilm production was detected by tube adherence method and Congo red agar method [p < 0.05 was considered significant.A total of 85 r method . ModifieP. aeruginosa isolates, 14 (16.4%) were metallo β-lactamase producers, while of the 50 Acinetobacter spp. 11 (22%) were metallo β-lactamase producers.Of the total 85 P. aeruginosa isolates, 23 (27.05%) were biofilm producers according to tube adherence test, while only 13 (15.29%) were biofilm producers as per Congo red agar method. Similarly, out of 50 Acinetobacter spp. 7 (14%) isolates were biofilm producers on the basis of tube adherence test while, only 5 (10%) were positive for biofilm production by Congo red agar method. Of the total 14 MBL producing P. aeruginosa isolates, only 2 were biofilm producers on the basis of Congo red agar method, whereas 4 were biofilm producers as per tube adherence method. Similarly, of total 11 MBL producing strains of Acinetobacter spp., only 2 were biofilm producers on the basis of Congo red agar method, whereas 1 was biofilm producer as per tube adherence method. Highest rate of susceptibility of P. aeruginosa was seen toward colistin (83.5%) followed by netilmycin (71.9%). Lowest rate of susceptibility was seen toward cefepime (22.4%). Highest rate of susceptibility of Acinetobacter spp. was seen toward colistin (74%) followed by netilmicin (70%). Lowest rate of susceptibility was seen toward ceftriaxone (6%)  were multidrug resistant, while of the total Acinetobacter spp. isolates, 45 (90%) were multidrug resistant. Higher rates of drug resistance were seen among biofilm producers in comparison to biofilm non producers but the correlation was statistically insignificant.Out of 85 Similarly, no statistically significant association between biofilm production and metallo beta-lactamase production could be established.Pseudomonas aeruginosa and Acinetobacter spp. are common causes of life threatening infections mainly in hospitalized patients. Further, the biofilm production and the metallo-β-lactamase production among these bacteria have further aggravated the problem. Infections with multidrug resistant P. aeruginosa and Acinetobacter spp. are of serious concern mainly in admitted patients [patients .P. aeruginosa were metallo β-lactamase producers which was in agreement with the finding by Kali et al. (16.3%) [Acinetobacter spp. as in our study was also reported by Lee et al. (15.1%) [In this study, 16.4% of  (16.3%) . Similar (15.1%) .Acinetobacter spp. and P. aeruginosa. However, due to their high toxicity, polymyxins are used for the treatment of only serious infections caused by pan-resistant Gram negative bacilli [P. aeruginosa and Acinetobacter spp. were susceptible to polymyxin B and colistin [Polymyxin B and colistin have been reported to demonstrate reasonable success in treatment of infections caused by MBL producing  bacilli . A studycolistin .P. aeruginosa (90% by Congo red agar method and 83.33% by tube adherence method) [Acinetobacter spp. (65.2%) [Very high rates of biofilm production in comparison to our study were reported by Rewatkar and Wadher among the strains of  method)  and by B (65.2%) .In our study, no statistically significant association between biofilm production and metallo beta-lactamase production along with antimicrobial resistance could be established, which was in contrast to the findings by Heydari and Eftekhar  and SingThough the higher numbers of biofilm producers were detected by tube adherence method in our study, due to the simplicity and cost effectiveness of the Congo red agar method  in compaDue to lack of resources we could not use molecular techniques in our study. Further, this is a uni-center study; multi-center study with large sample size would have generated more reliable results.Pseudomonas aeruginosa and Acinetobacter spp. However, no statistically significant association could be established between biofilm production and metallo beta-lactamase production.In our study, biofilm production and metallo beta-lactamase production were observed among


Solving the shortest path and min-cut problems are key in achieving high-performance and robust communication networks. Those problems have often been studied in deterministic and uncorrelated networks both in their original formulations as well as in several constrained variants. However, in real-world networks, link weights  are often correlated due to spatial or temporal reasons, and these correlated link weights together behave in a different manner and are not always additive, as commonly assumed.In this paper, we first propose two correlated link weight models, namely (1) the deterministic correlated model and (2) the (log-concave) stochastic correlated model. Subsequently, we study the shortest path problem and the min-cut problem under these two correlated models.We prove that these two problems are NP-hard under the deterministic correlated model, and even cannot be approximated to arbitrary degree in polynomial time. However, these two problems are solvable in polynomial time under the (constrained) nodal deterministic correlated model, and can be solved by convex optimization under the (log-concave) stochastic correlated model. Both the shortest path problem and the min-cut problem are of great importance to various kinds of network routing applications . A traffic request can be routed in the most efficient way  by computing a shortest path. On the other hand, the min-cut problem arises in the context of network reliability, network throughput, etc. Fortunately, both of these problems are solvable in polynomial time for networks with independent additive link weights.deterministic correlated model and a stochastic correlated model.We propose two correlated link weight models, namely a We study the shortest path problem and the min-cut problem under the deterministic correlated model, and we prove that both of them are NP-hard and even cannot be approximated in polynomial time.On the other hand, we also show that both the shortest path problem and the min-cut problem are solvable in polynomial time under a (constrained) nodal deterministic correlated model.To solve both problems under the proposed correlated models, we propose exact algorithms under the deterministic correlated model, and develop convex optimization formulations for the stochastic correlated model.However, often correlations or (inter-)dependencies exist among link weights. For example, in overlay  or multiThe remainder of this paper is organized as follows. “A network having node and link weights can be transformed into a directed network with only link weights, as done in . Therefow(l) to represent the weight of link l. For simplicity, in this paper we call w(l) the cost of l, although it could also reflect other metrics such as delay and energy. In the deterministic correlated model, for any two links correlated. When correlated, the use of one link may influence the cost of another in this model. For example, in Fig. s, a) and  are correlated with joint cost of 11, and all the other links have uncorrelated costs. We can see that in path s–b–t, the cost of link  is 10, since another link  in this path is not correlated with it. Therefore, this path’s cost is equal to 18. However, in path s–a–b–t, the cost of link  should be calculated together with link , leading to a joint cost 11, since they are correlated and both appear in this path. Therefore, this path’s total cost is equal to Without loss of generality, we use mentclass2pt{minimEquivalently, we could formulate l’s weight is multiplicative , then by using Analogously, for given mentclass2pt{minimX and Y with expected values linear correlation coefficient Cov represents the covariance of X and Y.In probability theory, given two random variables linear correlation coefficient in probability theory is different from and cannot be transformed to the one defined in the deterministic correlated model, because the variances of X and Y in Eq.  , 12. ForCorrelated Group (CG):We first define the N nodes and L links. A Correlated Group (CG) is a subset of links l and Given is a network l is uncorrelated/independent with all the other links, then we say i-th MCG, where i-th MCG, a multivariate Accordingly, the Maximum Correlated Group (MCG) is defined as a CG with the maximum number of correlated links. If a link l ranges from 0 to i, and j.Therefore, if the possible cost of link linear correlation coefficient in the stochastic correlated model, since we will later prove that via the log-concave property of this model, the shortest path problem can be solved by convex optimization.For example, Fig. w(l) following the deterministic correlated model. The Shortest Path under the Deterministic Correlated Model (SPDCM) problem is to find a path from a source s to a destination t with minimum cost.Given is a directed network s–b has a smaller cost than subpath s–a–b, path s–a–b–t (instead of path s–b–t) has minimum cost. In the following, we will study the complexity of the SPDCM problem.In conventional deterministic networks, a subpath of a shortest path is also the shortest. We refer to this property as the dominance of the subpath. However, this is not the case in networks with deterministic correlated link weights, which means that a dominated path may also lead to an optimal solution. For instance, in the example of Fig. The SPDCM problem is NP-hard.When the correlation coefficient is equal to 1, the SPDCM problem can be solved in polynomial time by a conventional shortest path algorithm. We therefore prove in the following that the SPDCM problem is NP-hard for “increasing correlation” Increasing correlations and t such that at most one node from each pair in the set i and j form a forbidden pair, their costs are correlated such that i, .) and  represent any link that contains an end node of i and j, respectively. In all the other cases, the link costs are uncorrelated and finite. Since When the correlation coefficient is greater than 1, we make a reduction to the forbidden pairs shortest path problem, which is known to be NP-hard . In a giDecreasing correlationg is the total number of colors, and given the color set s to destination node t such that it uses the least amount of colors.When the correlation coefficient is less than 1, we make a reduction to the Minimum Color Single-Path (MCSiP) problem, which is NP-hard . Given aq is the total number of distinct colors belonging to these m links. Therefore, the SPDCM problem is equivalent to the MCSiP problem. Assume that each color The SPDCM problem cannot be approximated to arbitrary degree in polynomial time, unless P NP.We provide a proof by contradiction.Increasing correlationi and j, we further assume s to t, then i and j cannot be simultaneously traversed by this path Assume that a polynomial-time approximation algorithm exists that can find a path with a cost at most Decreasing correlationG contain z mutually link-disjoint paths, one connecting i, u, v) and any We first introduce the Disjoint Connecting Paths problem . Given au, v) is not traversed, i.e., it traverses only one link from s to t. However, the optimal solution which traverses link  has a total cost of c is the sum of minimum hops from s to u and from v to t. For any given u, v). In that case, the algorithm can, in polynomial time, find two link-disjoint paths from s to u, and from v to t, which results in a contradiction. According to this assumption, the minimum value of a shortest path is 1 if link : the set of nodes adjacent to node u.The pseudo-code of the exact algorithm is given in Algorithm 1.Q contains at most O(1) time. Step 12 invokes t to select the minimum cost path. Hence, the overall time complexity of Algorithm 1 is The time complexity of Algorithm 1 can be computed as follows. Let rding to , \docume(kmaxN))  in Step nodal correlation.In some real-world networks , the links that are spatially  close to each other are usually correlated, whereas the links that are located far from each other are usually uncorrelated. We make an additional assumption, which is that only the links sharing the same node can be correlated. We call this a, there are generally two cases of nodal correlation, namely (1) links in the form of  and , and (2) links in the form of  and  are correlated. When  and  are correlated, a simple path cannot traverse both of them, since looping is not allowed. In this sense, any simple path only traverses at most one of them, which means that the links’ correlation will not affect the cost calculation of any simple path. Therefore, we only need to consider the case when  and  are correlated. We first define that if  and  are correlated, then a and b are called correlated nodes, which is represented by uncorrelated nodes, which is denoted by G, create new nodes For any two links uncorrelated node  and  in G, create links w and w, respectively, and the link u, v) and .For any two correlated links .For each link For each link For each link Although the SPDCM problem is NP-hard, we will show that, by transforming the original graph to an auxiliary graph, the Shortest Path under the Nodal Deterministic Correlated Model (SPNDCM) problem is solvable in polynomial time. For any node u, v) and  are correlated, we create four corresponding nodes a, b), , , and  are nodal correlated, then Fig. a, b),  and ,  may have different correlation coefficients, in Fig. uncorrelated node to a correlated node in the original graph, e.g.,  in Fig. correlated node to an uncorrelated node in the original graph, e.g.,  in Fig. The idea of the auxiliary graph is that if two links  problem: In a given directed graph We present a convex optimization formulation to solve the SPSCM problem. Convex optimization problems can usually be solved quickly and accurately with convex optimization solvers . Let us ObjectiveConstraintsu, v) is part of the shortest path. When u, v) appears on the path, else s and t, the number of incoming and outgoing links that are part of the path must be the same. For the source node s, the number of its outgoing links should be 1, and for the destination node t the number of its incoming links should be 1. The dual of the above Linear Program (LP) can be expressed as follows:ObjectiveConstraintsObjectiveConstraintsu, v), s to t such that the minimum link weight among all its traversed links is maximized. This problem appears with bottleneck metrics, such as bandwidth. The WPDN problem is solvable in polynomial time: First, we order all the link weights in the network in increasing order. After that, each round we prune lowest weight links in the graph and run a Depth First Search (DFS) or a Breadth First Search (BFS) algorithm to find a path from s to t. The algorithm will end if there is no path anymore from s to t and return the pruned weight value of the previous round.The Widest Path in Deterministic Networks (WPDN) problem is to find a path from W, then for each link the maximum average/amortized weight is In the Widest Path under the Deterministic Correlated Model (WPDCM) problem, if However, the WPDCM problem is still NP-hard and cannot be approximated to arbitrary degree. The proof follows analogously from Theorems w(l). It is assumed that two or more link costs are correlated under the deterministic correlated model. Given a source s and a target t, find a cut G into two disjoint subsets X (s and t are in different subsets and the cost of the cut C is minimized.The Min-Cut under the Deterministic Correlated Model (MCDCM) problem: Given is a network s, a) and  are correlated with a joint cost of 8. In this example, the maximum flow from s to t is s, a) and  and has a cost of 8.The min-cut value is not equal to the maximum flow value under the deterministic correlated model. For example, in Fig. The MCDCM problem is NP-hard.s. We set s to the destination t.In Fig. C should contain one link of either Since we want to find a min-cut that separates The SPDCM problem to the MCDCM problem: Considering an optimal solution of the SPDCM problem, denote by s to t with minimum cost. Hence, a solution to the MCDCM problem can also solve the SPDCM problem. The MCDCM problem to the SPDCM problem: An optimal solution of the MCDCM problem should be composed of either The MCDCM problem cannot be approximated to arbitrary degree in polynomial time, unless P NP.The proof follows from the fact that the SPDCM problem cannot be approximated to arbitrary degree in polynomial time according to Theorem By assigning each link l with the costw(l), running a conventional min-cut algorithm will return a cut with total cost at mostThe proof follows analogously from Theorem A and B, with s in A and t in B. Then we have A and B. Each combination assignment corresponds to a cut to separate A and B, and the one with minimum cost is returned as the optimal solution.Since the MCDCM problem is NP-hard and even does not admit a polynomial-time approximation algorithm, we suggest a brute-force approach to solve it. The idea is that we start with two sets In “The SRLG-like correlated model: Suppose The Shortest Path under the SRLG-like model (SP-SRLG) problem is NP-hard, since it is a general case of the MCSiP problem introduced in “x nodes, z nodes, and one y node. We set In the Nodal SRLG-like correlated model, we assumed that only the links that share the same node follow the SRLG-like correlated model. As “u, v) and  follow the SRLG-like correlated model and/or (2) for any node u, v) and  follow the SRLG-like correlated model. To prove case (1), let us first study the following theorem:However, we found that the MC-NSRLG problem is solvable in polynomial time when (1) only the nodal links in the form of  and  will never both appear in the optimal solution of the MC-NSRLG problem in case (1).s and t are separated by a min-cut C such that s is in the subset A and t is in the subset B. A proof by contradiction: we assume that  and  are both in the min-cut C. Since C is the min-cut that separates s and t, then node u should be in subset A, otherwise if node u is in subset B, there is no need to use  and  as the cut links, since their existence does not affect the connectedness between A and B. Similarly, node v is in subset B, otherwise if node v is also in A, there is no need to cut link . Based on this analysis, if y is in A, then  is not necessarily the link in the cut C, since link  does not affect the connectedness from A to B. However, if node y is in B, link  is also not necessarily the link in the min-cut, since nodes v and y are in the same subsets, which results in a contradiction. Suppose Based on Theorem ObjectiveConstraintsc stands for the link weight  of link  in the deterministic network and u, v) belongs to the cut.G as follows:G, create a new node u, v) and , where u, v) and .For each pair of two links u, v) and  are correlated in G, draw link w and w, respectively.For any link G, create a new node v, u) and , where v, u) and .For each pair of two links v, u) and  are correlated in G, draw link w and w, respectively.For any link c, d) also in For the other links The proposed auxiliary graph shares similarities with the auxiliary graph in Fig. Under case (2), the MC-NSRLG problem can be solved in polynomial time by running the above LP (Eqs. –17)) on) on17)) G into two disjoint subsets X  problem: In a given directed graph We propose a corresponding convex optimization formulation based on Eqs. –17)::17):ObjectiveConstraintsu, v), f and g are convex, both nondecreasing (or nonincreasing) and positive (nonnegative) functions on an interval, then It remains to show that Eq.  is conveIn a network with each link having multiple additive link weight metrics , the Quality of Service (QoS) routing problem is to find a path that satisfies a given constraints vector. Kuipers and Van Mieghem  study thu, v) is in state a then  is in state b, is given. Two similar link weight models, called link-based congestion model and node-based congestion model, are proposed in [There is also some literature dealing with correlated routing problems in stochastic networks . For exaposed in . Based oposed in , 29 defiposed in , 29 thers, t) min-cut problem refers to partitioning the network into two disjoint subsets, such that nodes s and t are in different subsets and the total weight of the cut links is minimized. This problem can be solved by finding the maximum flow from s to t [s, t). A summary and comparison of polynomial-time algorithms to solve the min-cut problem can be found in [s, t) min-cut problem.The  the deterministic correlated model and (2) the (log-concave) stochastic correlated model. We have proved that these two problems are NP-hard under the deterministic correlated model, and cannot be approximated to arbitrary degree, unless P