Genome scale modeling (GSM) predicts the performance of microbial workhorses and helps identify beneficial gene targets. GSM integrated with intracellular flux dynamics, omics, and thermodynamics have shown remarkable progress in both elucidating complex cellular phenomena and computational strain design (CSD). Nonetheless, these models still show high uncertainty due to a poor understanding of innate pathway regulations, metabolic burdens, and other factors (such as stress tolerance and metabolite channeling). Besides, the engineered hosts may have genetic mutations or non-genetic variations in bioreactor conditions and thus CSD rarely foresees fermentation rate and titer. Metabolic models play important role in design-build-test-learn cycles for strain improvement, and machine learning (ML) may provide a viable complementary approach for driving strain design and deciphering cellular processes. In order to develop quality ML models, knowledge engineering leverages and standardizes the wealth of information in literature (e.g., genomic/phenomic data, synthetic biology strategies, and bioprocess variables). Data driven frameworks can offer new constraints for mechanistic models to describe cellular regulations, to design pathways, to search gene targets, and to estimate fermentation titer/rate/yield under specified growth conditions (e.g., mixing, nutrients, and O). This review highlights the scope of information collections, database constructions, and machine learning techniques (such as deep learning and transfer learning), which may facilitate "Learn and Design" for strain development.

Currently silver nanoparticles (AgNP)-modified filter are widely used to inactivate airborne microbes in indoor environment. However, AgNP is extremely small and thus will penetrate cells membranes to cause cytotoxicity. AgNPs/NSP has been proven to be less cytotoxic to human body. In this study, it was the first time that AgNPs/NSP was used to develop a new antimicrobial air filter with low cytotoxicity. The AgNPs/NSP filter was made by dip-coating of filter with AgNPs/NSP and acrylic resin solution and three different amount of silver on filter were obtained including 12.6, 31.5 and 63 ppm. The filtration efficiency and the antimicrobial activity of AgNP/NSP filter were evaluated by bioaerosols including Escherichia coli and Candida famata in testing chamber and HVAC simulation system under 30% and 70% relative humidity (RH). The results showed that filtration efficiency of AgNPs/NSP-modified filter increased by about 13 to 20% compared to unmodified filter for E. coli but remained almost the same for C. famata. The antimicrobial efficiency of AgNPs/NSP modified filter of 63 ppm was 95.1% for E. coli at RH of 30%. In addition, 91% of antimicrobial efficiency for C. famata was found at RH of 70%. On the other hand, the antimicrobial efficiency of yeast for AgNPs/NSP-modified filter was 97.8% and 86.4% for RH of 30% and 70% respectively when yeast just started to contact with filter in HVAC system. The results suggest that AgNP/NSP-modified air filter can effectively inactivate microorganisms retained on. Therefore, emission of bioaerosols from air filter can be avoided in order to improve the air cleaning technology in indoor environment.

DNA and RNA sequencing technologies have revolutionized biology and biomedical sciences, sequencing full genomes and transcriptomes at very high speeds and reasonably low costs. RNA sequencing (RNA-Seq) enables transcript identification and quantification, but once sequencing has concluded researchers can be easily overwhelmed with questions such as how to go from raw data to differential expression (DE), pathway analysis and interpretation. Several pipelines and procedures have been developed to this effect. Even though there is no unique way to perform RNA-Seq analysis, it usually follows these steps: 1) raw reads quality check, 2) alignment of reads to a reference genome, 3) aligned reads’ summarization according to an annotation file, 4) DE analysis and 5) gene set analysis and/or functional enrichment analysis. Each step requires researchers to make decisions, and the wide variety of options and resulting large volumes of data often lead to interpretation challenges. There also seems to be insufficient guidance on how best to obtain relevant information and derive actionable knowledge from transcription experiments. In this paper, we explain RNA-Seq steps in detail and outline differences and similarities of different popular options, as well as advantages and disadvantages. We also discuss non-coding RNA analysis, multi-omics, meta-transcriptomics and the use of artificial intelligence methods complementing the arsenal of tools available to researchers. Lastly, we perform a complete analysis from raw reads to DE and functional enrichment analysis, visually illustrating how results are not absolute truths and how algorithmic decisions can greatly impact results and interpretation.

Sea urchins, in many instances, are collected from the wild, maintained in the laboratory aquaculture environment, and used as model animals for various scientific investigations. It has been increasingly evident that diet-driven dysbiosis of the gut microbiome could affect animal health and physiology, thereby impacting the outcome of the scientific studies. In this study, we compared the gut microbiome between naturally occurring (ENV) and formulated diet-fed laboratory aquaculture (LAB) sea urchin Lytechinus variegatus by amplicon sequencing of the V4 region of the 16S rRNA gene and bioinformatics tools. Overall, the ENV gut digesta had higher taxa richness with an abundance of Propionigenium, Photobacterium, Roseimarinus, and Flavobacteriales. In contrast, the LAB group revealed fewer taxa richness, but noticeable abundances of Arcobacter, Agarivorans, and Shewanella. However, Campylobacteraceae, primarily represented by Arcobacter spp., was commonly associated with the gut tissues of both ENV and LAB groups whereas the gut digesta had taxa from Gammaproteobacteria, particularly Vibrio spp. Similarly, the co-occurrence network displayed taxonomic organizations interconnected by Arcobacter and Vibrio as being the key taxa in gut tissues and gut digesta, respectively. Predicted functional analysis of the gut tissues microbiota of both ENV and LAB groups showed a higher trend in energy-related metabolisms, whereas amino acids, carbohydrate, and lipid metabolisms heightened in the gut digesta. This study provides an outlook of the laboratory-formulated diet-fed aquaculture L. variegatus gut microbiome and predicted metabolic profile as compared to the naturally occurring animals, which should be taken into consideration for consistency, reproducibility, and translatability of scientific studies.

A Gram-stain-negative or -positive, strictly anaerobic, non-spore-forming and pleomorphic bacterium (designated 14-104T) was isolated from the saliva sample of a patient with oral squamous cell carcinoma. It was an acid-tolerant neutralophilic mesophile, growing at between 20 and 40 °C (with optimum growth at 30 °C) and pH between pH 3.0 and 7.0 (with optimum growth at pH 6.0–7.0). It contained anteiso-C15 : 0 and C15 : 0 as the major fatty acids. The genome size of strain 14-104T was 2.98 Mbp, and the G+C content was 39.6 mol%. It shared <87 % 16S rRNA sequence similarity, <71 % orthologous average nucleotide identity, <76 % average amino acid identity and <68 %% of conserved proteins with its closest relative, Phocaeicola abscessus CCUG 55929T. Reconstruction of phylogenetic and phylogenomic trees revealed that strain 14-104T and P. abscessus CCUG 55929T were clustered as a distinct clade without any other terminal node. The phylogenetic and phylogenomic analyses along with physiological and chemotaxonomic data indicated that strain 14-104T represents a novel species in the genus Phocaeicola , for which the name Phocaeicola oris sp. nov. is proposed. The type strain is 14-104T (=BCRC 81305T= NBRC 115041T).