ALBERT

Description: Background Time-lapse microscopy live-cell imaging is essential for studying the evolution of bacterial communities at single-cell resolution. It allows capturing detailed information about the morphology, gene expression, and spatial characteristics of individual cells at every time instance of the imaging experiment. The image analysis of bacterial "single-cell movies" (videos) generates big data in the form of multidimensional time series of measured bacterial attributes. If properly analyzed, these datasets can help us decipher the bacterial communities' growth dynamics and identify the sources and potential functional role of intra- and inter-subpopulation heterogeneity. Recent research has highlighted the importance of investigating the role of biological "noise" in gene regulation, cell growth, cell division, etc. Single-cell analytics of complex single-cell movie datasets, capturing the interaction of multiple micro-colonies with thousands of cells, can shed light on essential phenomena for human health, such as the competition of pathogens and benign microbiome cells, the emergence of dormant cells (“persisters”), the formation of biofilms under different stress conditions, etc. However, highly accurate and automated bacterial bioimage analysis and single-cell analytics methods remain elusive, even though they are required before we can routinely exploit the plethora of data that single-cell movies generate. Results We present visualization and single-cell analytics using R (ViSCAR), a set of methods and corresponding functions, to visually explore and correlate single-cell attributes generated from the image processing of complex bacterial single-cell movies. They can be used to model and visualize the spatiotemporal evolution of attributes at different levels of the microbial community organization (i.e., cell population, colony, generation, etc.), to discover possible epigenetic information transfer across cell generations, infer mathematical and statistical models describing various stochastic phenomena (e.g., cell growth, cell division), and even identify and auto-correct errors introduced unavoidably during the bioimage analysis of a dense movie with thousands of overcrowded cells in the microscope's field of view. Conclusions ViSCAR empowers researchers to capture and characterize the stochasticity, uncover the mechanisms leading to cellular phenotypes of interest, and decipher a large heterogeneous microbial communities' dynamic behavior. ViSCAR source code is available from GitLab at https://gitlab.com/ManolakosLab/viscar.

Description: Background Members of the basic helix-loop-helix (bHLH) transcription factor family perform indispensable functions in various biological processes, such as plant growth, seed maturation, and abiotic stress responses. However, the bHLH family in foxtail millet (Setaria italica), an important food and feed crop, has not been thoroughly studied. Results In this study, 187 bHLH genes of foxtail millet (SibHLHs) were identified and renamed according to the chromosomal distribution of the SibHLH genes. Based on the number of conserved domains and gene structure, the SibHLH genes were divided into 21 subfamilies and two orphan genes via phylogenetic tree analysis. According to the phylogenetic tree, the subfamilies 15 and 18 may have experienced stronger expansion in the process of evolution. Then, the motif compositions, gene structures, chromosomal spread, and gene duplication events were discussed in detail. A total of sixteen tandem repeat events and thirty-eight pairs of segment duplications were identified in bHLH family of foxtail millet. To further investigate the evolutionary relationship in the SibHLH family, we constructed the comparative syntenic maps of foxtail millet associated with representative monocotyledons and dicotyledons species. Finally, the gene expression response characteristics of 15 typical SibHLH genes in different tissues and fruit development stages, and eight different abiotic stresses were analysed. The results showed that there were significant differences in the transcription levels of some SibHLH members in different tissues and fruit development stages, and different abiotic stresses, implying that SibHLH members might have different physiological functions. Conclusions In this study, we identified 187 SibHLH genes in foxtail millet and further analysed the evolution and expression patterns of the encoded proteins. The findings provide a comprehensive understanding of the bHLH family in foxtail millet, which will inform further studies on the functional characteristics of SibHLH genes.

Description: Background Faced with the ongoing global pandemic of coronavirus disease, the ‘National Reference Centre for Whole Genome Sequencing of microbial pathogens: database and bioinformatic analysis’ (GENPAT) formally established at the ‘Istituto Zooprofilattico Sperimentale dell’Abruzzo e del Molise’ (IZSAM) in Teramo (Italy) is in charge of the SARS-CoV-2 surveillance at the genomic scale. In a context of SARS-CoV-2 surveillance requiring correct and fast assessment of epidemiological clusters from substantial amount of samples, the present study proposes an analytical workflow for identifying accurately the PANGO lineages of SARS-CoV-2 samples and building of discriminant minimum spanning trees (MST) bypassing the usual time consuming phylogenomic inferences based on multiple sequence alignment (MSA) and substitution model. Results GENPAT constituted two collections of SARS-CoV-2 samples. The first collection consisted of SARS-CoV-2 positive swabs collected by IZSAM from the Abruzzo region (Italy), then sequenced by next generation sequencing (NGS) and analyzed in GENPAT (n = 1592), while the second collection included samples from several Italian provinces and retrieved from the reference Global Initiative on Sharing All Influenza Data (GISAID) (n = 17,201). The main results of the present work showed that (i) GENPAT and GISAID detected the same PANGO lineages, (ii) the PANGO lineages B.1.177 (i.e. historical in Italy) and B.1.1.7 (i.e. ‘UK variant’) are major concerns today in several Italian provinces, and the new MST-based method (iii) clusters most of the PANGO lineages together, (iv) with a higher dicriminatory power than PANGO lineages, (v) and faster that the usual phylogenomic methods based on MSA and substitution model. Conclusions The genome sequencing efforts of Italian provinces, combined with a structured national system of NGS data management, provided support for surveillance SARS-CoV-2 in Italy. We propose to build phylogenomic trees of SARS-CoV-2 variants through an accurate, discriminant and fast MST-based method avoiding the typical time consuming steps related to MSA and substitution model-based phylogenomic inference.

Description: Background Optical maps record locations of specific enzyme recognition sites within long genome fragments. This long-distance information enables aligning genome assembly contigs onto optical maps and ordering contigs into scaffolds. The generated scaffolds, however, often contain a large amount of gaps. To fill these gaps, a feasible way is to search genome assembly graph for the best-matching contig paths that connect boundary contigs of gaps. The combination of searching and evaluation procedures might be “searching followed by evaluation”, which is infeasible for long gaps, or “searching by evaluation”, which heavily relies on heuristics and thus usually yields unreliable contig paths. Results We here report an accurate and efficient approach to filling gaps of genome scaffolds with aids of optical maps. Using simulated data from 12 species and real data from 3 species, we demonstrate the successful application of our approach in gap filling with improved accuracy and completeness of genome scaffolds. Conclusion Our approach applies a sequential Bayesian updating technique to measure the similarity between optical maps and candidate contig paths. Using this similarity to guide path searching, our approach achieves higher accuracy than the existing “searching by evaluation” strategy that relies on heuristics. Furthermore, unlike the “searching followed by evaluation” strategy enumerating all possible paths, our approach prunes the unlikely sub-paths and extends the highly-probable ones only, thus significantly increasing searching efficiency.

Description: Background Lipid levels in blood have decreased considerably during the past decades in the general population partly due to use of statins. This study aims to investigate the trends in lipid levels between 2001 and 2018 in a statin-free population from primary health care, overall and by sex and age. Methods In a cohort of 634,119 patients from general practice with no diagnoses or medical treatments that affected lipid levels of total cholesterol (TC; n = 1,574,339) between 2001 and 2018 were identified. Similarly, measurements of low-density lipoprotein cholesterol (LDL-C; n = 1,302,440), high-density lipoprotein cholesterol (HDL-C; n = 1,417,857) and triglycerides (TG; n = 1,329,477) were identified. Results Mean TC decreased from 5.64 mmol/L (95% CI: 5.63–5.65) in 2001 to 5.17 mmol/L (95% CI: 5.16–5.17) in 2018 while LDL-C decreased from 3.67 mmol/L (95% CI: 3.66–3.68) to 3.04 mmol/L (95% CI: 3.03–3.04). Women aged 70–74 years experienced the largest decreases in TC levels corresponding to a decrease of 0.7 mmol/L. The decrease in LDL-C levels was most pronounced in men ≥85 years with a decrease of 0.9 mmol/L. For both genders, TC and LDL-C levels increased with advancing age until around age 50. After menopause the women had higher TC and LDL-C levels than the men. The median (geometric mean) TG level decreased by 0.4 mmol/L from 2001 to 2008, after which it increased slightly by 0.1 mmol/L until 2018. During life the TG levels of the men were markedly higher than the women’s until around age 65–70. HDL-C levels showed no trend during the study period. Conclusions The levels of TC and LDL-C decreased considerably in a statin-free population from primary health care from 2001 to 2018. These decreases were most pronounced in the elderly population and this trend is not decelerating. For TG, levels have started to increase, after an initial decrease.

Description: Background Proline can promote growth of plants by increasing photosynthetic activity under both non-stress and abiotic stress conditions. However, its role in non-stressed conditions is least studied. An experiment was conducted to assess as to whether increase in growth of wheat due to seed priming with proline under non-stress condition was associated with proline-induced changes in photosystem II (PSII) activity. Seeds of four wheat varieties (S-24, Sehar-06, Galaxy-13, and Pasban-90) were primed with different concentrations of proline (0, 5, 15 and 25 mM) for 12 h and allowed to grow under normal conditions. Biomass accumulation and photosynthetic performance, being two most sensitive features/indicators of plant growth, were selected to monitor proline modulated changes. Results Seed priming with proline increased the fresh and dry weights of shoots and roots, and plant height of all four wheat varieties. Maximum increase in growth attributes was observed in all four wheat varieties at 15 mM proline. Maximum growth improvement due to proline was found in var. Galaxy-13, whereas the reverse was true for S-24. Moreover, proline treatment changed the Fo, Fm, Fv/Fo, PIABS, PITot in wheat varieties indicating changes in PSII activity. Proline induced changes in energy fluxes for absorption, trapping, electron transport and heat dissipation per reaction center indicated that var. Galaxy-13 had better ability to process absorbed light energy through photosynthetic machinery. Moreover, lesser PSII efficiency in var. S-24 was due to lower energy flux for electron transport and greater energy flux for heat dissipation. This was further supported by the fact that var. S-24 had disturbance at acceptor side of PSI as reflected by reduction in ΔVIP, probability and energy flux for electron transport at the PSI end electron acceptors. Conclusion Seed priming with proline improved the growth of wheat varieties, which depends on type of variety and concentration of proline applied. Seed priming with proline significantly changed the PSII activity in wheat varieties, however, its translation in growth improvement depends on potential of processing of absorbed light energy by electron acceptors of electron transport chain, particularly those present at PSI end.

Description: Background Extremely low levels of high-density lipoprotein cholesterol (HDL-C) are related to high cardiovascular mortality. The underlying mechanism is not well known. This research aims to study the clinical characteristics of cardiovascular patients with extremely low levels of HDL-C. Methods All cardiovascular patients in a single Chinese cardiology center that were admitted from January to December 2019 were reviewed. The clinical characteristics of those with HDL-C

Description: Background Grapevine (Vitis vinifera) productivity has been severely affected by various bacterial, viral and fungal diseases worldwide. When a plant is infected with the pathogen, various defense mechanisms are subsequently activated in plants at various molecular levels. Thus, for substantiating the disease control in an eco-friendly way, it is essential to understand the molecular mechanisms governing pathogen resistance in grapes. Results In our study, we performed genome-wide identification of various defensive genes expressed during powdery mildew (PM) and downy mildew (DM) infections in grapevine. Consequently, we identified 6, 21, 2, 5, 3 and 48 genes of Enhanced Disease Susceptibility 1 (EDS1), Non-Race-specific Disease Resistance (NDR1), Phytoalexin deficient 4 (PAD4), Nonexpressor of PR Gene (NPR), Required for Mla-specified resistance (RAR) and Pathogenesis Related (PR), respectively, in the grapevine genome. The phylogenetic study revealed that V. vinifera defensive genes are evolutionarily related to Arabidopsis thaliana. Differential expression analysis resulted in identification of 2, 4, 7, 2, 4, 1 and 7 differentially expressed Nucleotide-binding leucine rich repeat receptor (NLR), EDS1, NDR1, PAD4, NPR, RAR1 and PR respectively against PM infections and 28, 2, 5, 4, 1 and 19 differentially expressed NLR, EDS1, NDR1, NPR, RAR1 and PR respectively against DM infections in V. vinifera. The co-expression study showed the occurrence of closely correlated defensive genes that were expressed during PM and DM stress conditions. Conclusion The PM and DM responsive defensive genes found in this study can be characterized in future for impelling studies relaying fungal and oomycete resistance in plants, and the functionally validated genes would then be available for conducting in-planta transgenic gene expression studies for grapes.

Description: Background Drug repositioning has caught the attention of scholars at home and abroad due to its effective reduction of the development cost and time of new drugs. However, existing drug repositioning methods that are based on computational analysis are limited by sparse data and classic fusion methods; thus, we use autoencoders and adaptive fusion methods to calculate drug repositioning. Results In this study, a drug repositioning algorithm based on a deep autoencoder and adaptive fusion was proposed to mitigate the problems of decreased precision and low-efficiency multisource data fusion caused by data sparseness. Specifically, a drug is repositioned by fusing drug-disease associations, drug target proteins, drug chemical structures and drug side effects. First, drug feature data integrated by drug target proteins and chemical structures were processed with dimension reduction via a deep autoencoder to characterize feature representations more densely and abstractly. Then, disease similarity was computed using drug-disease association data, while drug similarity was calculated with drug feature and drug-side effect data. Predictions of drug-disease associations were also calculated using a top-k neighbor method that is commonly used in predictive drug repositioning studies. Finally, a predicted matrix for drug-disease associations was acquired after fusing a wide variety of data via adaptive fusion. Based on experimental results, the proposed algorithm achieves a higher precision and recall rate than the DRCFFS, SLAMS and BADR algorithms with the same dataset. Conclusion The proposed algorithm contributes to investigating the novel uses of drugs, as shown in a case study of Alzheimer's disease. Therefore, the proposed algorithm can provide an auxiliary effect for clinical trials of drug repositioning.

Description: Background Generating high-quality de novo genome assemblies is foundational to the genomics study of model and non-model organisms. In recent years, long-read sequencing has greatly benefited genome assembly and scaffolding, a process by which assembled sequences are ordered and oriented through the use of long-range information. Long reads are better able to span repetitive genomic regions compared to short reads, and thus have tremendous utility for resolving problematic regions and helping generate more complete draft assemblies. Here, we present LongStitch, a scalable pipeline that corrects and scaffolds draft genome assemblies exclusively using long reads. Results LongStitch incorporates multiple tools developed by our group and runs in up to three stages, which includes initial assembly correction (Tigmint-long), followed by two incremental scaffolding stages (ntLink and ARKS-long). Tigmint-long and ARKS-long are misassembly correction and scaffolding utilities, respectively, previously developed for linked reads, that we adapted for long reads. Here, we describe the LongStitch pipeline and introduce our new long-read scaffolder, ntLink, which utilizes lightweight minimizer mappings to join contigs. LongStitch was tested on short and long-read assemblies of Caenorhabditis elegans, Oryza sativa, and three different human individuals using corresponding nanopore long-read data, and improves the contiguity of each assembly from 1.2-fold up to 304.6-fold (as measured by NGA50 length). Furthermore, LongStitch generates more contiguous and correct assemblies compared to state-of-the-art long-read scaffolder LRScaf in most tests, and consistently improves upon human assemblies in under five hours using less than 23 GB of RAM. Conclusions Due to its effectiveness and efficiency in improving draft assemblies using long reads, we expect LongStitch to benefit a wide variety of de novo genome assembly projects. The LongStitch pipeline is freely available at https://github.com/bcgsc/longstitch.