IdentiCS – Identification of coding sequence and in silico reconstruction of the metabolic network directly from unannotated low-coverage bacterial genome sequence

BACKGROUND: A necessary step for a genome level analysis of the cellular metabolism is the in silico reconstruction of the metabolic network from genome sequences. The available methods are mainly based on the annotation of genome sequences including two successive steps, the prediction of coding sequences (CDS) and their function assignment. The annotation process takes time. The available methods often encounter difficulties when dealing with unfinished error-containing genomic sequence. RESULTS: In this work a fast method is proposed to use unannotated genome sequence for predicting CDSs and for an in silico reconstruction of metabolic networks. Instead of using predicted genes or CDSs to query public databases, entries from public DNA or protein databases are used as queries to search a local database of the unannotated genome sequence to predict CDSs. Functions are assigned to the predicted CDSs simultaneously. The well-annotated genome of Salmonella typhimurium LT2 is used as an example to demonstrate the applicability of the method. 97.7% of the CDSs in the original annotation are correctly identified. The use of SWISS-PROT-TrEMBL databases resulted in an identification of 98.9% of CDSs that have EC-numbers in the published annotation. Furthermore, two versions of sequences of the bacterium Klebsiella pneumoniae with different genome coverage (3.9 and 7.9 fold, respectively) are examined. The results suggest that a 3.9-fold coverage of the bacterial genome could be sufficiently used for the in silico reconstruction of the metabolic network. Compared to other gene finding methods such as CRITICA our method is more suitable for exploiting sequences of low genome coverage. Based on the new method, a program called IdentiCS (Identification of Coding Sequences from Unfinished Genome Sequences) is delivered that combines the identification of CDSs with the reconstruction, comparison and visualization of metabolic networks (free to download at ). CONCLUSIONS: The reversed querying process and the program IdentiCS allow a fast and adequate prediction protein coding sequences and reconstruction of the potential metabolic network from low coverage genome sequences of bacteria. The new method can accelerate the use of genomic data for studying cellular metabolism

A quantitative image analysis pipeline for the characterization of filamentous fungal morphologies as a tool to uncover targets for morphology engineering: a case study using aplD in Aspergillus niger

Background Fungal fermentation is used to produce a diverse repertoire of enzymes, chemicals, and drugs for various industries. During submerged cultivation, filamentous fungi form a range of macromorphologies, including dispersed mycelia, clumped aggregates, or pellets, which have critical implications for rheological aspects during fermentation, gas/nutrient transfer, and, thus, product titres. An important component of strain engineering efforts is the ability to quantitatively assess fungal growth phenotypes, which will drive novel leads for morphologically optimized production strains. Results In this study, we developed an automated image analysis pipeline to quantify the morphology of pelleted and dispersed growth (MPD) which rapidly and reproducibly measures dispersed and pelleted macromorphologies from any submerged fungal culture. It (i) enables capture and analysis of several hundred images per user/day, (ii) is designed to quantitatively assess heterogeneous cultures consisting of dispersed and pelleted forms, (iii) gives a quantitative measurement of culture heterogeneity, (iv) automatically generates key Euclidian parameters for individual fungal structures including particle diameter, aspect ratio, area, and solidity, which are also assembled into a previously described dimensionless morphology number MN, (v) has an in-built quality control check which enables end-users to easily confirm the accuracy of the automated calls, and (vi) is easily adaptable to user-specified magnifications and macromorphological definitions. To concomitantly provide proof of principle for the utility of this image analysis pipeline, and provide new leads for morphologically optimized fungal strains, we generated a morphological mutant in the cell factory Aspergillus niger based on CRISPR-Cas technology. First, we interrogated a previously published co-expression networks for A. niger to identify a putative gamma-adaptin encoding gene (aplD) that was predicted to play a role in endosome cargo trafficking. Gene editing was used to generate a conditional aplD expression mutant under control of the titratable Tet-on system. Reduced aplD expression caused a hyperbranched growth phenotype and diverse defects in pellet formation with a putative increase in protein secretion. This possible protein hypersecretion phenotype could be correlated with increased dispersed mycelia, and both decreased pellet diameter and MN. Conclusion The MPD image analysis pipeline is a simple, rapid, and flexible approach to quantify diverse fungal morphologies. As an exemplar, we have demonstrated that the putative endosomal transport gene aplD plays a crucial role in A. niger filamentous growth and pellet formation during submerged culture. This suggests that endocytic components are underexplored targets for engineering fungal cell factories.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

Metabolic peculiarities of Aspergillus niger disclosed by comparative metabolic genomics

A genome-scale metabolic network and an in-depth genomic comparison of Aspergillus niger with seven other fungi is presented, revealing more than 1,100 enzyme-coding genes that are unique to A. niger

Functional exploration of co-expression networks identifies a nexus for modulating protein and citric acid titres in Aspergillus niger submerged culture

Background: Filamentous fungal cell factories are used to produce numerous proteins, enzymes, and organic acids. Protein secretion and filamentous growth are tightly coupled at the hyphal tip. Additionally, both these processes require ATP and amino acid precursors derived from the citric acid cycle. Despite this interconnection of organic acid production and protein secretion/filamentous growth, few studies in fungi have identified genes which may concomitantly impact all three processes. Results: We applied a novel screen of a global co-expression network in the cell factory Aspergillus niger to identify candidate genes which may concomitantly impact macromorphology, and protein/organic acid fermentation. This identified genes predicted to encode the Golgi localized ArfA GTPase activating protein (GAP, AgeB), and ArfA guanine nucleotide exchange factors (GEFs SecG and GeaB) to be co-expressed with citric acid cycle genes. Consequently, we used CRISPR-based genome editing to place the titratable Tet-on expression system upstream of ageB, secG, and geaB in A. niger. Functional analysis revealed that ageB and geaB are essential whereas secG was dispensable for early filamentous growth. Next, gene expression was titrated during submerged cultivations under conditions for either protein or organic acid production. ArfA regulators played varied and culture-dependent roles on pellet formation. Notably, ageB or geaB expression levels had major impacts on protein secretion, whereas secG was dispensable. In contrast, reduced expression of each predicted ArfA regulator resulted in an absence of citric acid in growth media. Finally, titrated expression of either GEFs resulted in an increase in oxaloacetic acid concentrations in supernatants. Conclusion: Our data suggest that the Golgi may play an underappreciated role in modulating organic acid titres during industrial applications, and that this is SecG, GeaB and AgeB dependent in A. niger. These data may lead to novel avenues for strain optimization in filamentous fungi for improved protein and organic acid titres.TU Berlin, Open-Access-Mittel - 201

Is autoinducer-2 a universal signal for interspecies communication: a comparative genomic and phylogenetic analysis of the synthesis and signal transduction pathways

BACKGROUND: Quorum sensing is a process of bacterial cell-to-cell communication involving the production and detection of extracellular signaling molecules called autoinducers. Recently, it has been proposed that autoinducer-2 (AI-2), a furanosyl borate diester derived from the recycling of S-adenosyl-homocysteine (SAH) to homocysteine, serves as a universal signal for interspecies communication. RESULTS: In this study, 138 completed genomes were examined for the genes involved in the synthesis and detection of AI-2. Except for some symbionts and parasites, all organisms have a pathway to recycle SAH, either using a two-step enzymatic conversion by the Pfs and LuxS enzymes or a one-step conversion using SAH-hydrolase (SahH). 51 organisms including most Gamma-, Beta-, and Epsilonproteobacteria, and Firmicutes possess the Pfs-LuxS pathway, while Archaea, Eukarya, Alphaproteobacteria, Actinobacteria and Cyanobacteria prefer the SahH pathway. In all 138 organisms, only the three Vibrio strains had strong, bidirectional matches to the periplasmic AI-2 binding protein LuxP and the central signal relay protein LuxU. The initial two-component sensor kinase protein LuxQ, and the terminal response regulator luxO are found in most Proteobacteria, as well as in some Firmicutes, often in several copies. CONCLUSIONS: The genomic analysis indicates that the LuxS enzyme required for AI-2 synthesis is widespread in bacteria, while the periplasmic binding protein LuxP is only present in Vibrio strains. Thus, other organisms may either use components different from the AI-2 signal transduction system of Vibrio strains to sense the signal of AI-2, or they do not have such a quorum sensing system at all

Delayed Memory Unit: Modelling Temporal Dependency Through Delay Gate

Recurrent Neural Networks (RNNs) are renowned for their adeptness in modeling temporal dependencies, a trait that has driven their widespread adoption for sequential data processing. Nevertheless, vanilla RNNs are confronted with the well-known issue of gradient vanishing and exploding, posing a significant challenge for learning and establishing long-range dependencies. Additionally, gated RNNs tend to be over-parameterized, resulting in poor network generalization. To address these challenges, we propose a novel Delayed Memory Unit (DMU) in this paper, wherein a delay line structure, coupled with delay gates, is introduced to facilitate temporal interaction and temporal credit assignment, so as to enhance the temporal modeling capabilities of vanilla RNNs. Particularly, the DMU is designed to directly distribute the input information to the optimal time instant in the future, rather than aggregating and redistributing it over time through intricate network dynamics. Our proposed DMU demonstrates superior temporal modeling capabilities across a broad range of sequential modeling tasks, utilizing considerably fewer parameters than other state-of-the-art gated RNN models in applications such as speech recognition, radar gesture recognition, ECG waveform segmentation, and permuted sequential image classification

Protein identification from two-dimensional gel electrophoresis analysis of Klebsiella pneumoniae by combined use of mass spectrometry data and raw genome sequences

Separation of proteins by two-dimensional gel electrophoresis (2-DE) coupled with identification of proteins through peptide mass fingerprinting (PMF) by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is the widely used technique for proteomic analysis. This approach relies, however, on the presence of the proteins studied in public-accessible protein databases or the availability of annotated genome sequences of an organism. In this work, we investigated the reliability of using raw genome sequences for identifying proteins by PMF without the need of additional information such as amino acid sequences. The method is demonstrated for proteomic analysis of Klebsiella pneumoniae grown anaerobically on glycerol. For 197 spots excised from 2-DE gels and submitted for mass spectrometric analysis 164 spots were clearly identified as 122 individual proteins. 95% of the 164 spots can be successfully identified merely by using peptide mass fingerprints and a strain-specific protein database (ProtKpn) constructed from the raw genome sequences of K. pneumoniae. Cross-species protein searching in the public databases mainly resulted in the identification of 57% of the 66 high expressed protein spots in comparison to 97% by using the ProtKpn database. 10 dha regulon related proteins that are essential for the initial enzymatic steps of anaerobic glycerol metabolism were successfully identified using the ProtKpn database, whereas none of them could be identified by cross-species searching. In conclusion, the use of strain-specific protein database constructed from raw genome sequences makes it possible to reliably identify most of the proteins from 2-DE analysis simply through peptide mass fingerprinting

The intra- and extracellular proteome of Aspergillus niger growing on defined medium with xylose or maltose as carbon substrate

The filamentous fungus Aspergillus niger is well-known as a producer of primary metabolites and extracellular proteins. For example, glucoamylase is the most efficiently secreted protein of Aspergillus niger, thus the homologous glucoamylase (glaA) promoter as well as the glaA signal sequence are widely used for heterologous protein production. Xylose is known to strongly repress glaA expression while maltose is a potent inducer of glaA promoter controlled genes. For a more profound understanding of A. niger physiology, a comprehensive analysis of the intra- and extracellular proteome of Aspergillus niger AB1.13 growing on defined medium with xylose or maltose as carbon substrate was carried out using 2-D gel electrophoresis/Maldi-ToF and nano-HPLC MS/MS

A genome-wide study of two-component signal transduction systems in eight newly sequenced mutans streptococci strains

<p>Abstract</p> <p>Background</p> <p>Mutans streptococci are a group of gram-positive bacteria including the primary cariogenic dental pathogen <it>Streptococcus mutans </it>and closely related species. Two component systems (TCSs) composed of a signal sensing histidine kinase (HK) and a response regulator (RR) play key roles in pathogenicity, but have not been comparatively studied for these oral bacterial pathogens.</p> <p>Results</p> <p>HKs and RRs of 8 newly sequenced mutans streptococci strains, including <it>S. sobrinus </it>DSM20742, <it>S. ratti </it>DSM20564 and six <it>S. mutans </it>strains, were identified and compared to the TCSs of <it>S. mutans </it>UA159 and NN2025, two previously genome sequenced <it>S. mutans </it>strains. Ortholog analysis revealed 18 TCS clusters (HK-RR pairs), 2 orphan HKs and 2 orphan RRs, of which 8 TCS clusters were common to all 10 strains, 6 were absent in one or more strains, and the other 4 were exclusive to individual strains. Further classification of the predicted HKs and RRs revealed interesting aspects of their putative functions. While TCS complements were comparable within the six <it>S. mutans </it>strains, <it>S. sobrinus </it>DSM20742 lacked TCSs possibly involved in acid tolerance and fructan catabolism, and <it>S. ratti </it>DSM20564 possessed 3 unique TCSs but lacked the quorum-sensing related TCS (ComDE). Selected computational predictions were verified by PCR experiments.</p> <p>Conclusions</p> <p>Differences in the TCS repertoires of mutans streptococci strains, especially those of <it>S. sobrinus </it>and <it>S. ratti </it>in comparison to <it>S. mutans</it>, imply differences in their response mechanisms for survival in the dynamic oral environment. This genomic level study of TCSs should help in understanding the pathogenicity of these mutans streptococci strains.</p

The role of mesenchymal stem cells derived exosomes as a novel nanobiotechnology target in the diagnosis and treatment of cancer

Mesenchymal stem cells (MSCs), one of the most common types of stem cells, are involved in the modulation of the tumor microenvironment (TME). With the advancement of nanotechnology, exosomes, especially exosomes secreted by MSCs, have been found to play an important role in the initiation and development of tumors. In recent years, nanobiotechnology and bioengineering technology have been gradually developed to detect and identify exosomes for diagnosis and modify exosomes for tumor treatment. Several novel therapeutic strategies bioengineer exosomes to carry drugs, proteins, and RNAs, and further deliver their encapsulated cargoes to cancer cells through the properties of exosomes. The unique properties of exosomes in cancer treatment include targeting, low immunogenicity, flexibility in modification, and high biological barrier permeability. Nevertheless, the current comprehensive understanding of the roles of MSCs and their secreted exosomes in cancer development remain inadequate. It is necessary to better understand/update the mechanism of action of MSCs-secreted exosomes in cancer development, providing insights for better modification of exosomes through bioengineering technology and nanobiotechnology. Therefore, this review focuses on the role of MSCs-secreted exosomes and bioengineered exosomes in the development, progression, diagnosis, and treatment of cancer