Domestic hot-water boilers harbour active thermophilic bacterial communities distinctly different from those in the cold-water supply.

Running cold and hot water in buildings is a widely established commodity. However, interests regarding hygiene and microbiological aspects had so far been focussed on cold water. Little attention has been given to the microbiology of domestic hot-water installations (DHWIs), except for aspects of pathogenic Legionella. World-wide, regulations consider hot (or warm) water as 'heated drinking water' that must comply (cold) drinking water (DW) standards. However, the few reports that exist indicate presence and growth of microbial flora in DHWIs, even when supplied with water with disinfectant residual. Using flow cytometric (FCM) total cell counting (TCC), FCM-fingerprinting, and 16S rRNA-gene-based metagenomic analysis, the characteristics and composition of bacterial communities in cold drinking water (DW) and hot water from associated boilers (operating at 50 - 60 °C) was studied in 14 selected inhouse DW installations located in Switzerland and Austria. A sampling strategy was applied that ensured access to the bulk water phase of both, supplied cold DW and produced hot boiler water. Generally, 1.3- to 8-fold enhanced TCCs were recorded in hot water compared to those in the supplied cold DW. FCM-fingerprints of cold and corresponding hot water from individual buildings indicated different composition of cold- and hot-water microbial floras. Also, hot waters from each of the boilers sampled had its own individual FCM-fingerprint. 16S rRNA-gene-based metagenomic analysis confirmed the marked differences in composition of microbiomes. E.g., in three neighbouring houses supplied from the same public network pipe each hot-water boiler contained its own thermophilic bacterial flora. Generally, bacterial diversity in cold DW was broad, that in hot water was restricted, with mostly thermophilic strains from the families Hydrogenophilaceae, Nitrosomonadaceae and Thermaceae dominating. Batch growth assays, consisting of cold DW heated up to 50 - 60 °C and inoculated with hot water, resulted in immediate cell growth with doubling times between 5 and 10 h. When cold DW was used as an inoculum no significant growth was observed. Even boilers supplied with UVC-treated cold DW contained an actively growing microbial flora, suggesting such hot-water systems as autonomously operating, thermophilic bioreactors. The generation of assimilable organic carbon from dissolved organic carbon due to heating appears to be the driver for growth of thermophilic microbial communities. Our report suggests that a man-made microbial ecosystem, very close to us all and of potential hygienic importance, may have been overlooked so far. Despite consumers having been exposed to microbial hot-water flora for a long time, with no major pathogens so far been associated specifically with hot-water usage (except for Legionella), the role of harmless thermophiles and their interaction with potential human pathogens able to grow at elevated temperatures in DHWIs remains to be investigated.

Spatially-Variant CNN-based Point Spread Function Estimation for Blind Deconvolution and Depth Estimation in Optical Microscopy.

Optical microscopy is an essential tool in biology and medicine. Imaging thin, yet non-flat objects in a single shot (without relying on more sophisticated sectioning setups) remains challenging as the shallow depth of field that comes with highresolution microscopes leads to unsharp image regions and makes depth localization and quantitative image interpretation difficult. Here, we present a method that improves the resolution of light microscopy images of such objects by locally estimating image distortion while jointly estimating object distance to the focal plane. Specifically, we estimate the parameters of a spatiallyvariant Point Spread Function (PSF) model using a Convolutional Neural Network (CNN), which does not require instrument- or object-specific calibration. Our method recovers PSF parameters from the image itself with up to a squared Pearson correlation coefficient of 0.99 in ideal conditions, while remaining robust to object rotation, illumination variations, or photon noise. When the recovered PSFs are used with a spatially-variant and regularized Richardson-Lucy (RL) deconvolution algorithm, we observed up to 2.1 dB better Signal-to-Noise Ratio (SNR) compared to other Blind Deconvolution (BD) techniques. Following microscope-specific calibration, we further demonstrate that the recovered PSF model parameters permit estimating surface depth with a precision of 2 micrometers and over an extended range when using engineered PSFs. Our method opens up multiple possibilities for enhancing images of non-flat objects with minimal need for a priori knowledge about the optical setup.

ASAP: a web-based platform for the analysis and interactive visualization of single-cell RNA-seq data.

MOTIVATION: Single-cell RNA-sequencing (scRNA-seq) allows whole transcriptome profiling of thousands of individual cells, enabling the molecular exploration of tissues at the cellular level. Such analytical capacity is of great interest to many research groups in the world, yet these groups often lack the expertise to handle complex scRNA-seq datasets. RESULTS: We developed a fully integrated, web-based platform aimed at the complete analysis of scRNA-seq data post genome alignment: from the parsing, filtering and normalization of the input count data files, to the visual representation of the data, identification of cell clusters, differentially expressed genes (including cluster-specific marker genes), and functional gene set enrichment. This Automated Single-cell Analysis Pipeline (ASAP) combines a wide range of commonly used algorithms with sophisticated visualization tools. Compared with existing scRNA-seq analysis platforms, researchers (including those lacking computational expertise) are able to interact with the data in a straightforward fashion and in real time. Furthermore, given the overlap between scRNA-seq and bulk RNA-seq analysis workflows, ASAP should conceptually be broadly applicable to any RNA-seq dataset. As a validation, we demonstrate how we can use ASAP to simply reproduce the results from a single-cell study of 91 mouse cells involving five distinct cell types. AVAILABILITY AND IMPLEMENTATION: The tool is freely available at asap.epfl.ch and R/Python scripts are available at github.com/DeplanckeLab/ASAP. CONTACT: bart.deplancke@epfl.ch. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

ATLAS of Biochemistry: A Repository of All Possible Biochemical Reactions for Synthetic Biology and Metabolic Engineering Studies.

Because the complexity of metabolism cannot be intuitively understood or analyzed, computational methods are indispensable for studying biochemistry and deepening our understanding of cellular metabolism to promote new discoveries. We used the computational framework BNICE.ch along with cheminformatic tools to assemble the whole theoretical reactome from the known metabolome through expansion of the known biochemistry presented in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. We constructed the ATLAS of Biochemistry, a database of all theoretical biochemical reactions based on known biochemical principles and compounds. ATLAS includes more than 130 000 hypothetical enzymatic reactions that connect two or more KEGG metabolites through novel enzymatic reactions that have never been reported to occur in living organisms. Moreover, ATLAS reactions integrate 42% of KEGG metabolites that are not currently present in any KEGG reaction into one or more novel enzymatic reactions. The generated repository of information is organized in a Web-based database ( http://lcsb-databases.epfl.ch/atlas/ ) that allows the user to search for all possible routes from any substrate compound to any product. The resulting pathways involve known and novel enzymatic steps that may indicate unidentified enzymatic activities and provide potential targets for protein engineering. Our approach of introducing novel biochemistry into pathway design and associated databases will be important for synthetic biology and metabolic engineering.