Reinforcing synthetic data for meticulous survival prediction of patients suffering from left ventricular systolic dysfunction

Congestive heart failure is among leading genesis of concern that requires an immediate medical attention. Among various cardiac disorders, left ventricular systolic dysfunction is one of the well known cardiovascular disease which causes sudden congestive heart failure. The irregular functioning of a heart can be diagnosed through some of the clinical attributes, such as ejection fraction, serum creatinine etcetera. However, due to availability of a limited data related to the death events of patients suffering from left ventricular systolic dysfunction, a critical level of thresholds of clinical attributes can not be estimated with higher precision. Hence, this paper proposes a novel pseudo reinforcement learning algorithm which overcomes a problem of majority class skewness in a limited dataset by appending a synthetic dataset across minority data space. The proposed pseudo agent in the algorithm continuously senses the state of the dataset (pseudo environment) and takes an appropriate action to populate the dataset resulting into higher reward. In addition, the paper also investigates the role of statistically significant clinical attributes such as age, ejection fraction, serum creatinine etc., which tends to efficiently predict the association of death events of the patients suffering from left ventricular systolic dysfunctio

Genomic and proteomic analysis of lignin degrading and polyhydroxyalkanoate accumulating β-proteobacterium Pandoraea sp. ISTKB

Abstract Background Lignin is a major component of plant biomass and is recalcitrant to degradation due to its complex and heterogeneous aromatic structure. The biomass-based research mainly focuses on polysaccharides component of biomass and lignin is discarded as waste with very limited usage. The sustainability and success of plant polysaccharide-based biorefinery can be possible if lignin is utilized in improved ways and with minimal waste generation. Discovering new microbial strains and understanding their enzyme system for lignin degradation are necessary for its conversion into fuel and chemicals. The Pandoraea sp. ISTKB was previously characterized for lignin degradation and successfully applied for pretreatment of sugarcane bagasse and polyhydroxyalkanoate (PHA) production. In this study, genomic analysis and proteomics on aromatic polymer kraft lignin and vanillic acid are performed to find the important enzymes for polymer utilization. Results Genomic analysis of Pandoraea sp. ISTKB revealed the presence of strong lignin degradation machinery and identified various candidate genes responsible for lignin degradation and PHA production. We also applied label-free quantitative proteomic approach to identify the expression profile on monoaromatic compound vanillic acid (VA) and polyaromatic kraft lignin (KL). Genomic and proteomic analysis simultaneously discovered Dyp-type peroxidase, peroxidases, glycolate oxidase, aldehyde oxidase, GMC oxidoreductase, laccases, quinone oxidoreductase, dioxygenases, monooxygenases, glutathione-dependent etherases, dehydrogenases, reductases, and methyltransferases and various other recently reported enzyme systems such as superoxide dismutases or catalase–peroxidase for lignin degradation. A strong stress response and detoxification mechanism was discovered. The two important gene clusters for lignin degradation and three PHA polymerase spanning gene clusters were identified and all the clusters were functionally active on KL–VA. Conclusions The unusual aerobic ‘-CoA’-mediated degradation pathway of phenylacetate and benzoate (reported only in 16 and 4–5% of total sequenced bacterial genomes), peroxidase-accessory enzyme system, and fenton chemistry based are the major pathways observed for lignin degradation. Both ortho and meta ring cleavage pathways for aromatic compound degradation were observed in expression profile. Genomic and proteomic approaches provided validation to this strain’s robust machinery for the metabolism of recalcitrant compounds and PHA production and provide an opportunity to target important enzymes for lignin valorization in future

Comparative Structural Modeling of Six Old Yellow Enzymes (OYEs) from the Necrotrophic Fungus <i>Ascochyta rabiei</i> : Insight into Novel OYE Classes with Differences in Cofactor Binding, Organization of Active Site Residues and Stereopreferences

<div><p>Old Yellow Enzyme (OYE1) was the first flavin-dependent enzyme identified and characterized in detail by the entire range of physical techniques. Irrespective of this scrutiny, true physiological role of the enzyme remains a mystery. In a recent study, we systematically identified OYE proteins from various fungi and classified them into three classes viz. Class I, II and III. However, there is no information about the structural organization of Class III OYEs, eukaryotic Class II OYEs and Class I OYEs of filamentous fungi. <i>Ascochyta rabiei</i>, a filamentous phytopathogen which causes Ascochyta blight (AB) in chickpea possesses six OYEs (ArOYE1-6) belonging to the three OYE classes. Here we carried out comparative homology modeling of six ArOYEs representing all the three classes to get an in depth idea of structural and functional aspects of fungal OYEs. The predicted 3D structures of <i>A. rabiei</i> OYEs were refined and evaluated using various validation tools for their structural integrity. Analysis of FMN binding environment of Class III OYE revealed novel residues involved in interaction. The ligand <i>para</i>-hydroxybenzaldehyde (PHB) was docked into the active site of the enzymes and interacting residues were analyzed. We observed a unique active site organization of Class III OYE in comparison to Class I and II OYEs. Subsequently, analysis of stereopreference through structural features of ArOYEs was carried out, suggesting differences in R/S selectivity of these proteins. Therefore, our comparative modeling study provides insights into the FMN binding, active site organization and stereopreference of different classes of ArOYEs and indicates towards functional differences of these enzymes. This study provides the basis for future investigations towards the biochemical and functional characterization of these enigmatic enzymes.</p></div

Docking of <i>para</i>-hydroxybenzyaldehyde (PHB) in the active sites of ArOYEs.

<p><i>para</i>-hydroxybenzyaldehyde (PHB) was docked in the respective active site pocket of each ArOYE. Active site residues interacting with the ligand were analyzed. Position of active site residues in respective ArOYE is indicated by numbers.</p

Comparison of ArOYEs with yeast OYE1 and YqjM.

<p>ArOYE1-3 (red and yellow) were superimposed upon yeast OYE1 (sky blue and dark blue), ArOYE4 and ArOYE5 (red and yellow) were superimposed upon <i>B. subtilis</i> YqjM (sky blue and dark blue), and ArOYE6 was superimposed upon both OYE1 and YqjM.</p

Proteins used as templates for homology modeling of <i>A. rabiei</i> OYEs.

<p>Proteins used as templates for homology modeling of <i>A. rabiei</i> OYEs.</p

Docking of <i>para</i>-hydroxybenzyaldehyde (PHB) in the active sites of ArOYEs.

<p><i>para</i>-hydroxybenzyaldehyde (PHB) was docked in the respective active site pocket of each ArOYE. Active site residues interacting with the ligand were analyzed. Position of active site residues in respective ArOYE is indicated by numbers.</p

Multiple sequence alignment of ArOYE1-6 along with previously reported OYEs.

<p>The alignment includes OYEs from bacteria [<i>Pseudomonas syringae</i> (PsNcr) AAD16106.1, <i>Pseudomonas fluorescens</i> (PfXenB) AAF02539.1, <i>Shewanella oneidensis</i> (SYE1) NP_718044.1, (SYE2) NP_718043.1, (SYE3) NP_719682.1, (SYE4) NP_718946.1, <i>Agrobacterium radiobacter</i> (ArNerA) CAA74280.1, <i>Pseudomonas putida</i> (PpmorB) AAC43569.1, (PpOYE) NP_743414.1, <i>Enterobacter cloacae</i> (EclOnr) AAB38683.1, <i>Escherichia coli</i> (EcNer) NP_416167.1, <i>Geobacter metallireducens</i> (GmOYE) YP_006721534.1, <i>Thermus thermophilus</i> (TtOYE) YP_143423.1, <i>Thermus scotoductus</i> (TsOYE) YP_004203660.1, <i>Thermoanaerobacter pseudethanolicus</i> (TpOYE) YP_001664021.1, <i>Geobacillus kaustophilus</i> (GkOYE) YP_148185.1 and <i>Bacillus subtilis</i> (BsYqjM) NP_390263.1], yeast [<i>Saccharomyces cerevisiae</i> (OYE2) NP_012049.1, (OYE3) NP_015154.1, <i>Kluyveromyces lactis</i> (KYE) AAA98815.1, <i>Saccharomyces pastorianus</i> (OYE1) Q02899.3, <i>Hansenula polymorpha</i> (HYE1) AAN09952.1, (HYE2) AAN09953.1, (HYE3) AAN09954.1, and <i>Pichia stipitis</i> (PsOYE) XP_001384055.1], filamentous fungi [<i>Aspergillus fumigatus</i> (AfEasA) Q4WZ70.1 and <i>Claviceps purpurea</i> (CpEasA) AET79178.1], land plants [<i>Arabidopsis thaliana</i> (AtOPR1) CAA71627.1, (AtOPR2) NP_177795.1, (AtOPR3) NP_178662.1 and <i>Solanum lycopersicum</i> (SlOPR1) NP_001234781.1, (SlOPR2) NP_001233868.1, (SlOPR3) NP_001233873.1] and protozoa [<i>Trypanosoma cruzi</i> (TcOYE) AAA74448.1]. The multiple sequence and structure alignment program PROMALS3D was used to generate the alignment using default parameters. The positions of the conserved active sites are highlighted with the rectangular boxes. The consensus sequence is illustrated below the alignment.</p

Stereopreference in ArOYEs.

<p>Pseudo-atoms were generated for the residues described by Oberdorfer et al. (33) involved in the stereoselectivity of ArOYEs. Bond distances between pseudo-atoms were analyzed for each ArOYE. Pseudo-atom distances indicate that ArOYE1 and ArOYE2 are exclusive <i>R</i>-selective, ArOYE3 is moderately selective and ArOYE4-6 are exclusive <i>S</i>-selective.</p

MOESM3 of Genomic and proteomic analysis of lignin degrading and polyhydroxyalkanoate accumulating β-proteobacterium Pandoraea sp. ISTKB

Additional file 3: Table S10. Other differentially expressed proteins (related to PHA metabolism, dehydrogenase, reductases, transferases, esterases and hydrolases) on kraft lignin