Comparative genome sequencing and analyses of Mycobacterium cosmeticum reveal potential for biodesulfization of gasoline

<div><p><i>Mycobacterium cosmeticum</i> is a nontuberculous <i>Mycobacterium</i> recovered from different water sources including household potable water and water collected at nail salon. Individual cases of this bacterium have been reported to be associated with gastrointestinal tract infections. Here we present the first whole-genome study and comparative analysis of two new clinically-derived <i>Mycobacterium</i> sp. UM_RHS (referred as UM_RHS after this) and <i>Mycobacterium</i> sp. UM_NYF (referred as UM_NYF after this) isolated from patients in Indonesia and Malaysia respectively to have a better understanding of the biological characteristic of these isolates. Both strains are likely <i>Mycobacterium cosmeticum</i> as supported by the evidence from molecular phylogenetic, comparative genomic and Average Nucleotide Identity (ANI) analyses. We found the presence of a considerably large number of putative virulence genes in the genomes of UM_RHS and UM_NYF. Interestingly, we also found a horizontally transferred genomic island carrying a putative <i>dsz</i> operon proposing that they may have potential to perform biodesulfization of dibenzothiophene (DBT) that may be effective in cost reduction and air pollution during fuel combustion. This comparative study may provide new insights into <i>M</i>. <i>cosmeticum</i> and serve as an important reference for future functional studies of this bacterial species.</p></div

Comparative genome analysis of Fusobacterium nucleatum

Fusobacterium nucleatum is considered to be a key oral bacterium in recruiting periodontal pathogens into subgingival dental plaque. Currently F. Nucleatum can be subdivided into five subspecies. Our previous genome analysis of F. Nucleatum W1481 (referred to hereafter asW1481), isolated from an 8-mmperiodontal pocket in a patient with chronic periodontitis, suggested the possibility of a new subspecies. To further investigate the biology and relationships of this possible subspecies with other known subspecies, we performed comparative analysis between W1481 and 35 genome sequences represented by the five known Fusobacterium subspecies.Our analyses suggest thatW1481ismost likely anew F. Nucleatum subspecies, supported by evidence fromphylogenetic analysesandmaximaluniquematchindices(MUMi). Interestingly,wefoundahorizontally transferredW1481-specificgenomicisland harboring the tripartite ATP-independent (TRAP)-like transporter genes, suggesting this bacterium might have a high-Affinity transport system for the C4-dicarboxylates malate, succinate, and fumarate.Moreover, we found virulence genes in theW1481 genome that may provide a strong defense mechanism which might enable it to colonize and survive within the host by evading immune surveillance. This comparative study provides better understanding of F. Nucleatum and the basis for future functional work on this important pathogen

Development of ListeriaBase and comparative analysis of Listeria monocytogenes

Background: Listeria consists of both pathogenic and non-pathogenic species. Reports of similarities between the genomic content between some pathogenic and non-pathogenic species necessitates the investigation of these species at the genomic level to understand the evolution of virulence-associated genes. With Listeria genome data growing exponentially, comparative genomic analysis may give better insights into evolution, genetics and phylogeny of Listeria spp., leading to better management of the diseases caused by them. Description: With this motivation, we have developed ListeriaBase, a web Listeria genomic resource and analysis platform to facilitate comparative analysis of Listeria spp. ListeriaBase currently houses 850,402 protein-coding genes, 18,113 RNAs and 15,576 tRNAs from 285 genome sequences of different Listeria strains. An AJAX-based real time search system implemented in ListeriaBase facilitates searching of this huge genomic data. Our in-house designed comparative analysis tools such as Pairwise Genome Comparison (PGC) tool allowing comparison between two genomes, Pathogenomics Profiling Tool (PathoProT) for comparing the virulence genes, and ListeriaTree for phylogenic classification, were customized and incorporated in ListeriaBase facilitating comparative genomic analysis of Listeria spp. Interestingly, we identified a unique genomic feature in the L. monocytogenes genomes in our analysis. The Auto protein sequences of the serotype 4 and the non-serotype 4 strains of L. monocytogenes possessed unique sequence signatures that can differentiate the two groups. We propose that the aut gene may be a potential gene marker for differentiating the serotype 4 strains from other serotypes of L. monocytogenes. Conclusions: ListeriaBase is a useful resource and analysis platform that can facilitate comparative analysis of Listeria for the scientific communities. We have successfully demonstrated some key utilities of ListeriaBase. The knowledge that we obtained in the analyses of L. monocytogenes may be important for functional works of this human pathogen in future. ListeriaBase is currently available at http://listeria.um.edu.my

Development of ListeriaBase and comparative analysis of \u3ci\u3eListeria monocytogenes\u3c/i\u3e

Background: Listeria consists of both pathogenic and non-pathogenic species. Reports of similarities between the genomic content between some pathogenic and non-pathogenic species necessitates the investigation of these species at the genomic level to understand the evolution of virulence-associated genes. With Listeria genome data growing exponentially, comparative genomic analysis may give better insights into evolution, genetics and phylogeny of Listeria spp., leading to better management of the diseases caused by them. Description: With this motivation, we have developed ListeriaBase, a web Listeria genomic resource and analysis platform to facilitate comparative analysis of Listeria spp. ListeriaBase currently houses 850,402 protein-coding genes, 18,113 RNAs and 15,576 tRNAs from 285 genome sequences of different Listeria strains. An AJAX-based real time search system implemented in ListeriaBase facilitates searching of this huge genomic data. Our in-house designed comparative analysis tools such as Pairwise Genome Comparison (PGC) tool allowing comparison between two genomes, Pathogenomics Profiling Tool (PathoProT) for comparing the virulence genes, and ListeriaTree for phylogenic classification, were customized and incorporated in ListeriaBase facilitating comparative genomic analysis of Listeria spp. Interestingly, we identified a unique genomic feature in the L. monocytogenes genomes in our analysis. The Auto protein sequences of the serotype 4 and the non-serotype 4 strains of L. monocytogenes possessed unique sequence signatures that can differentiate the two groups. We propose that the aut gene may be a potential gene marker for differentiating the serotype 4 strains from other serotypes of L. monocytogenes. Conclusions: ListeriaBase is a useful resource and analysis platform that can facilitate comparative analysis of Listeria for the scientific communities. We have successfully demonstrated some key utilities of ListeriaBase. The knowledge that we obtained in the analyses of L. monocytogenes may be important for functional works of this human pathogen in future. ListeriaBase is currently available at http://listeria.um.edu.my

Comparative genome analyses of mycobacteria give better insights into their evolution.

Mycobacteria a genus of Actinobacteria are widespread in nature ranging from soil-dwelling saprophytes to human and animal pathogens. The rate of growth has been a classifying factor for the Mycobacterium spp., dividing them into the rapid growers and the slow growers. Here we have performed a comparative genome study of mycobacterial species in order to get better understanding of their evolution, particularly to understand the distinction between the rapid and slow growers. Our study shows that the slow growers had generally gained and lost more genes compared to the rapid growers. The slow growers might haved eventually lost genes (LivFGMH operon, shaACDEFG genes and MspA porin) that could contribute to the slow growth rate of the slow growers. The genes gained and lost in mycobacteria had eventually helped these bacteria to adapt to different environments and have led to the evolution of the present day rapid and slow growers. Our results also show high number of Mycobacterium abscessus specific genes (811 genes) and some of them are associated with the known bacterial quorum sensing genes that might be important for Mycobacterium abscessus to adapt and survive in variety of unfavorable environments. Mycobacterium abscessus also does not contains genes involved in the bacterial defense system and together with the quorum sensing genes may have contributed to the high gene gain rate of Mycobacterium abscessus

An On-Device Learning System for Estimating Liquid Consumption from Consumer-Grade Water Bottles and Its Evaluation

A lightweight on-device liquid consumption estimation system involving an energy-aware machine learning algorithm is developed in this work. This system consists of two separate on-device neural network models that carry out liquid consumption estimation with the result of two tasks: the detection of sip from gestures with which the bottle is handled by its user and the detection of first sips after a bottle refill. This predictive volume estimation framework incorporates a self-correction mechanism that can minimize the error after each bottle fill-up cycle, which makes the system robust to errors from the sip classification module. In this paper, a detailed characterization of sip detection is performed to understand the accuracy-complexity tradeoffs by developing and implementing a variety of different ML models with varying complexities. The maximum energy consumed by the entire framework is around 119 mJ during a maximum computation time of 300 μs. The energy consumption and computation times of the proposed framework is suitable for implementation in low-power embedded hardware that can be incorporated in consumer grade water bottles

Characterization of Homology Model of VCO395_1035.

<p>A. The cartoon representation of 3D modeled structure of VCO395_1035 using PDB ID: 3STJ. Helix (blue), sheets (Purple) and loops (Sky Blue). B. The β-barrel like structure of protease Domain of VCO395_1035 showing active site loops LD: Activation loop, L1: Oxyanion loop, L2: Substrate specificity and L3: Regulatory loop along with interdomain linker (IDL) helix. ML 1: Modeled loop 1 in Protease domain (residue 79–89) on FALC-Loop server indicated as α1-helix. C. The PDZ1 Domain of VCO395_1035, showing flexible carboxylate binding loop (CBL) and interacting clamp (IC). ML 2: Modeled loop 2 in PDZ1 domain (residue 176–189) on FALC-Loop server indicated as α6-helix.</p

Active site and Protein-substrate interaction using Hex 5.0.

<p>A. The surface view of protease domain containing active site showing the oxyanion hole and properly oriented shallow S1 hydrophobic pocket. B. The surface view of PDZ1 containing hydrophobic binding groove formed by CBL and α7-Helix showing shallow P₀ and P₋₂ substrate binding pocket. C. The C-terminal of poly-alanine peptide substrate (blue) docked into active side of protease domain. D. The C-terminal of poly-alanine peptide substrate (blue) docked into active side of PDZ1 domain via β-aggumentation. E. The superimposition of substrate docked into the protease active site (blue) with respective to template (3STJ) substrate (red). F. The superimposition of substrate docked into the active site PDZ1 domain (blue) with respective to template (3STJ) substrate (red).</p

Comparison of the catalytic triad residues and active site arrangement of active and inactive form of the protease domain.

<p>Comparison of the catalytic triad residues and active site arrangement of active and inactive form of the protease domain.</p