Correction: Composition of nasal bacterial community and its seasonal variation in health care workers stationed in a clinical research laboratory.

[This corrects the article DOI: 10.1371/journal.pone.0260314.].

Data on SSR markers and SNPs filtered from transcriptome of Parvocalanus crassirostris.

Calanoid copepod populations are being severely affected due to the effects of ocean acidification (OA) and ocean warming (OW). These marine organisms are the most abundant primary consumers contributing significantly in the marine food web. Any effect on the abundance and diversity of copepods due to climate change is likely to have serious implications on the marine ecosystem functioning. Molecular studies that play a vital role in assessing the genetic changes under the influence of environmental imbalances are completely lacking for this species. Here we report the genetic variations in three generations of copepods through transcriptome sequencing. RNA sequencing was performed on an Illumina HiSeq platform employing the 2 × 100 bp paired-end chemistry. Approximately, 10GB of data was obtained for all the samples. The raw sequences were assembled through Trinity 2.6.6 and mined for single nucleotide polymorphisms (SNPs) and simple sequence repeats (SSRs). MIcroSAtellite Identification Tool (MISA) was used for SSR detection and Primer 3 (v 3.0) was utilized to design short oligonucleotide primers (18-20 mers). A total of 15,222 SSRs were identified and 28,944 primer pairs were designed against these motifs. The transcriptome possessed 413,890 SNPs at a frequency of 2.8 per kb. The newly discovered SSRs and SNPs could act as genetic markers for future studies on genetic diversity and conservation for Parvocalanus crassirostris.

Deep learning-driven prediction of drug mechanism of action from large-scale chemical-genetic interaction profiles.

MOTIVATION: Chemical-genetic interaction profiling is a genetic approach that quantifies the susceptibility of a set of mutants depleted in specific gene product(s) to a set of chemical compounds. With the recent advances in artificial intelligence, chemical-genetic interaction profiles (CGIPs) can be leveraged to predict mechanism of action of compounds. This can be achieved by using machine learning, where the data from a CGIP is fed into the machine learning platform along with the chemical descriptors to develop a chemogenetically trained model. As small molecules can be considered non-structural data, graph convolutional neural networks, which can learn from the chemical structures directly, can be used to successfully predict molecular properties. Clustering analysis, on the other hand, is a critical approach to get insights into the underlying biological relationships between the gene products in the high-dimensional chemical-genetic data. METHODS AND RESULTS: In this study, we proposed a comprehensive framework based on the large-scale chemical-genetics dataset built in Mycobacterium tuberculosis for predicting CGIPs using graph-based deep learning models. Our approach is structured into three parts. First, by matching M. tuberculosis genes with homologous genes in Escherichia coli (E. coli) according to their gene products, we grouped the genes into clusters with distinct biological functions. Second, we employed a directed message passing neural network to predict growth inhibition against M. tuberculosis gene clusters using a collection of 50,000 chemicals with the profile. We compared the performance of different baseline models and implemented multi-label tasks in binary classification frameworks. Lastly, we applied the trained model to an externally curated drug set that had experimental results against M. tuberculosis genes to examine the effectiveness of our method. Overall, we demonstrate that our approach effectively created M. tuberculosis gene clusters, and the trained classifier is able to predict activity against essential M. tuberculosis targets with high accuracy. CONCLUSION: This work provides an analytical framework for modeling large-scale chemical-genetic datasets for predicting CGIPs and generating hypothesis about mechanism of action of novel drugs. In addition, this work highlights the importance of graph-based deep neural networks in drug discovery.

Composition of nasal bacterial community and its seasonal variation in health care workers stationed in a clinical research laboratory.

The microorganisms at the workplace contribute towards a large portion of the biodiversity a person encounters in his or her life. Health care professionals are often at risk due to their frontline nature of work. Competition and cooperation between nasal bacterial communities of individuals working in a health care setting have been shown to mediate pathogenic microbes. Therefore, we investigated the nasal bacterial community of 47 healthy individuals working in a clinical research laboratory in Kuwait. The taxonomic profiling and core microbiome analysis identified three pre-dominant genera as Corynebacterium (15.0%), Staphylococcus (10.3%) and, Moraxella (10.0%). All the bacterial genera exhibited seasonal variations in summer, winter, autumn and spring. SparCC correlation network analysis revealed positive and negative correlations among the classified genera. A rich set of 16 genera (q < 0.05) were significantly differentially abundant (LEfSe) across the four seasons. The highest species counts, richness and evenness (P < 0.005) were recorded in autumn. Community structure profiling indicated that the entire bacterial population followed a seasonal distribution (R2-0.371; P < 0.001). Other demographic factors such as age, gender and, ethnicity contributed minimally towards community clustering in a closed indoor laboratory setting. Intra-personal diversity also witnessed rich species variety (maximum 6.8 folds). Seasonal changes in the indoor working place in conjunction with the outdoor atmosphere seems to be important for the variations in the nasal bacterial communities of professionals working in a health care setting.

Characterization of Supragingival Plaque and Oral Swab Microbiomes in Children With Severe Early Childhood Caries.

The human oral cavity harbors one of the most diverse microbial communities with different oral microenvironments allowing the colonization of unique microbial species. This study aimed to determine which of two commonly used sampling sites (dental plaque vs. oral swab) would provide a better prediction model for caries-free vs. severe early childhood caries (S-ECC) using next generation sequencing and machine learning (ML). In this cross-sectional study, a total of 80 children (40 S-ECC and 40 caries-free) < 72 months of age were recruited. Supragingival plaque and oral swab samples were used for the amplicon sequencing of the V4-16S rRNA and ITS1 rRNA genes. The results showed significant differences in alpha and beta diversity between dental plaque and oral swab bacterial and fungal microbiomes. Differential abundance analyses showed that, among others, the cariogenic species Streptococcus mutans was enriched in the dental plaque, compared to oral swabs, of children with S-ECC. The fungal species Candida dubliniensis and C. tropicalis were more abundant in the oral swab samples of children with S-ECC compared to caries-free controls. They were also among the top 20 most important features for the classification of S-ECC vs. caries-free in oral swabs and for the classification of dental plaque vs. oral swab in the S-ECC group. ML approaches revealed the possibility of classifying samples according to both caries status and sampling sites. The tested site of sample collection did not change the predictability of the disease. However, the species considered to be important for the classification of disease in each sampling site were slightly different. Being able to determine the origin of the samples could be very useful during the design of oral microbiome studies. This study provides important insights into the differences between the dental plaque and oral swab bacteriome and mycobiome of children with S-ECC and those caries-free.

Draft Genome Sequences of Six Multidrug-Resistant Clinical Strains of Acinetobacter baumannii, Isolated at Two Major Hospitals in Kuwait.

Acinetobacter baumannii is an important opportunistic pathogen in global health care settings. Its dissemination and multidrug resistance pose an issue with treatment and outbreak control. Here, we present draft genome assemblies of six multidrug-resistant clinical strains of A. baumannii isolated from patients admitted to one of two major hospitals in Kuwait.

Draft Genome Sequences of Five Clinical Strains of Brucella melitensis Isolated from Patients Residing in Kuwait.

Human brucellosis is a neglected and underrecognized infection of widespread geographic distribution. Brucellosis is present on all inhabited continents and endemic in many areas of the world, including Kuwait and the Middle East. Here, we present draft genome assemblies of five Brucella melitensis strains isolated from brucellosis patients in Kuwait.

Concentration-dependent antagonistic persuasion of SDS and naphthalene derivatives on the fibrillation of stem bromelain.

Sodium dodecyl sulfate, a biological membrane mimetic, can be used to study the conversion of globular proteins into amyloid fibrils in vitro. Using multiple approaches, the effect of SDS was examined on stem bromelain (SB), a widely recognized therapeutic protein. SB is known to exist as a partially folded intermediate at pH 2.0, situation also encountered in the gastrointestinal tract (its site of absorption). In the presence of sub-micellar SDS concentration (500-1000 µM), this intermediate was found to exhibit great propensity to form large-sized ß-sheeted aggregates with fibrillar morphology, the hall marks of amyloid structure. We also observed inhibition of fibrillation by two naphthalene-based compounds, ANS and bis-ANS. While bis-ANS significantly inhibited fibril formation at 50 µM, ANS did so at relatively higher concentration (400 µM). Alcohols, but not salts, were found to weaken the inhibitory action of these compounds suggesting the possible involvement of hydrophobic interactions in their binding to protein. Besides, isothermal titration calorimetry and molecular docking studies suggested that inhibition of fibrillation by these naphthalene derivatives is mediated not just through hydrophobic forces, but also by disruption of π-π interactions between the aromatic residues together with the inter-polypeptide chain repulsion among negatively charged ANS/bis-ANS bound SB.