imagine (Institute of molecular genetics and genetic engineering)
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Linking quinoline ring to 5-nitrofuran moiety via sulfonyl hydrazone bridge: Synthesis, structural characterization, DFT studies, and evaluation of antibacterial and antifungal activity
No full textIn the present work, we report the synthesis, structural characterization, and computational studies of (E)-N'-((5-nitrofuran-2-yl)methylene)quinoline-8-sulfonohydrazide (QNF) as a potential antimicrobial drug candidate. To design the target molecule, we utilized a molecular hybridization technique that connects two antimicrobial pharmacophores (quinoline and 5-nitrofuran rings) with a sulfonyl hydrazone moiety. QNF was synthesized by the condensation of quinoline-8-sulfonohydrazide with 5-nitrofuran-2-carbaldehyde, and characterized by various spectral techniques including single-crystal X-ray crystallography. QNF was extensively evaluated for its antibacterial and antifungal activity. The inhibition capacity of QNF on Candida albicans filamentation and biofilm formation was further investigated. Biofilm inhibition of QNF against C. albicans was supported by molecular docking studies in the binding site of agglutinin-like sequence 3 (Als3). Drug-like profile of QNF was confirmed by in silico calculation of its significant physicochemical properties. Additionally, the optimized geometrical structure, natural bond orbital calculations, frontier molecular orbital and molecular electrostatic potential analysis of QNF were carried out using the density functional theory method at the B3LYP with 6-31+G(d,p) basis set. 1H and 13C NMR chemical shift values were performed using the gauge-invariant atomic orbital method. Structural parameters and NMR values obtained experimentally were compared with the calculated values
Targeting LLPS in disease: a new modality in drug development
Get PDFBiomolecular condensation is a process whereby many macromolecules (proteins and
RNAs) form non-stoichiometric, functional assemblies. The dominant mechanism of such
biomolecular condensation is liquid-liquid phase separation (LLPS), which leads to the
formation of membraneless organelles (MLOs), such as the nucleolus and stress granules,
in the cell. The proteins involved often have a high proportion of intrinsic structural
disorder, which drive LLPS by transient, multivalent interactions. As MLOs play key roles in
cell signaling, the misregulation of their formation and dissolution often leads to diseases
termed “condensatopathies”. In my presentation, I will outline the basic mechanisms
leading to such disease states, focusing on cancer, viral infections and neurodegeneration.
I will also discuss the different potential strategies for correcting these errors in cell
signaling, and show through specific examples how drug candidates, “c-mods” capable of
correcting MLO misregulation, can be developed.Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 202
Exploiting the linear organisation of omics network embedding spaces
Get PDFWe are increasingly accumulating large-scale biological omics data that describe different
aspects of cellular functioning. These datasets are typically modelled and analyzed as
networks. To ease the downstream analyses, recent approaches embed the nodes of a
network into a low-dimensional space by using a skip-gram neural network (e.g. DeepWalk,
LINE and node2vec). These methods are implicitly factorizing a positive pointwise mutual
information (PPMI) matrix, which could be explicitly factorized with Non-negative Matrix
Tri-Factorization (NMTF). Importantly, in Natural Language Processing (NLP), word
embeddings obtained by using similar approaches showed linear algebraic structures,
which allows for answering analogy questions by using simple linear vector operations.
Thus, we investigate if we can obtain and exploit similar linear embedding spaces for the
biological omics networks.
We initiate the use of the PPMI matrices to capture the neighborhood relationship or the
structural (topological) similarities of nodes in the network. By embedding the human
Protein-Protein Interaction (PPI) network by factorizing its PPMI matrix representations
with NMTF, we demonstrate that the embedding vectors of genes having different Gene
Ontology (GO) annotations are linearly separated in the PPI embedding space.
Then, in analogy to the embedding vector of a sentence being obtained as the sum
(average) of the embedding vectors of its constituent words in NLP, we show that the
embedding vectors of biological functions and of protein complexes can be obtained by
averaging he embedding vectors of the genes that participate in then, and that these
embeddings can be used to predict protein complex memberships and cancer genes.
Finally, we investigate the embeddings of cancer and control tissue specific PPI networks
and show that simple subtractions allow for identifying cancer altered biological functions
and cancer genes.Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 202
Some Applications of Graph-Based Machine Learning Methods on Biological Data
Get PDFMachine learning has made considerable contributions to various fields, most notably by
providing methods for predictive modeling and data analysis. Usually, different kinds of
data are best modeled by specialized machine learning models, tailored to account for the
specifics of the data at hand. Graphs are an expressive data representation most suited
for representing relationships between objects. The relationships can be interactions,
hierarchies, similarities, or others. Such structures can be found in different kinds of
data, including biological ones. Luckily, machine learning toolbox abounds with methods
suitable for handling these kinds of data and we consider several applications of such
graph-based machine learning methods on biological data. First we discuss tree-like
hierarchies over the target variable values and the ways to account for such hierarchies
in learning. We consider enzyme classification as a suitable application. Then we discuss
hierarchies over the target variable values corresponding to directed acyclic graphs and
graph neural network as a suitable model for this kind of data. We consider protein function
classification as a suitable application. Finally, we discuss construction of similarity graphs
over tabular instances, based on autoencoders and graph representation learning ideas.
We consider the application of such techniques to the exploratory analysis of biological
data related to expression of schizophreniaBook of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 2023Acknowledgement: I would like to thank my coauthors and collaborators: Jovana Kovačević,
Petar Veličković, Stefan Spalević, Nevena Ćirić, Predrag Janjić, and Stefan Kapuna
Omics Data Fusion for Understanding Molecular Complexity Enabling Precision Medicine
Get PDFWe are flooded by increasing volumes of heterogeneous, interconnected, systems-level,
molecular (multi-omic) data. They provide complementary information about cells, tissues
and diseases. We need to utilize them to better stratify patients into risk groups, discover
new biomarkers, and repurpose known and discover new drugs to personalize medical
treatment. This is nontrivial, because of computational intractability of many underlying
problems, necessitating the development of algorithms for finding approximate solutions
(heuristics).
We develop a versatile data fusion (integration) machine learning (ML) framework to
address key challenges in precision medicine from these data: better stratification of
patients, prediction of biomarkers, and re-purposing of approved drugs to particular
patient groups, applied to cancer, Covid-19, rare thrombophilia and Parkinson’s Disease.
Our new methods stem from graph-regularized non-negative matrix tri-factorization
(NMTF), a machine learning technique for dimensionality reduction, inference and coclustering
of heterogeneous datasets, coupled with novel network science algorithms. We
utilize our new framework to develop methodologies for improving the understanding the
molecular organization and disease from the omics data embedding space.Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 202
Development of hybrid and optimized deep learning classifiers for speech recognition in tracheostomy patients: a case study
Get PDFThe ability of a machine or program to recognize words spoken aloud and translate
them into legible text is known as speech recognition and has gained a lot of attention
in the last decade especially in healthcare to promote the quality of care. The problem of
speech recognition in patients with tracheostomy has not yet been investigated in the
literature. In this work, we propose a hybrid and highly scalable deep learning workflow
which utilizes both CNN and RNN architectures across video recordings to identify speech.
Dropout rates were also used to avoid overfitting effects. Hyperparameter optimization
was applied using the GridSearch method to fine tune the DL workflow on each patient.
A case study was applied, where video records were collected from 25 patients in Greece
who read specific texts from Greek language, selected by logotherapy experts. A fully
automated data processing pipeline was initially applied to extract the video frames
based on the provided annotations by the experts (start time, end time per word). Then,
we handled the speech recognition problem as a multiclass classification problem,
where each word represents a class. Two different types of models were developed; 25
personalized models, which were trained and tested across each individual patient, and a
generalized model which was trained and tested on randomly selected instances from all
patients. Our results highlight the increased accuracy in terms of reduced word error rate
in both the personalized and the generalized hybrid DL models against the conventional
DL models.Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 202
Multiomics Integration by Non-Negative Tri-Matrix Factorization Reveals New Target Genes in Parkinson’s Disease
Get PDFParkinson’s disease (PD) is the second most common neurodegenerative disease which is
characterized by neuronal loss of dopaminergic neurons (mDA) in the substantia nigra. The
underlying complexity of the disease and limited amount of patient material limits current
interventions to only symptomatic and no curative treatment despite intensive research.
We use patient-derived induced pluripotent stem cells to generate mDAs and investigate
disease mechanisms by multiomics characterization including single cell RNA-sequencing
and bulk proteomics and metabolomics. For this purpose, we developed an extended Non-
Negative TriMatrix Factorization approache that allows to integrate the heterogeneous
omics data with knowledge of molecular databases including protein-protein, genetic and
metabolic interactions as well as co-expression profiles. Our approach was able to identify
already PD-associated but also new druggable candidate genes of PD development.Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 202
Deciphering key regulatory networks and drug repurposing candidates through scRNAseq data analysis using SCANet
Get PDFDifferences in co-expression networks between two or multiple cell (sub)types across
conditions is a pressing problem in single-cell RNA sequencing (scRNA-seq). A key
challenge is to define those co-variations that differ between or among cell types and/
or conditions and phenotypes to examine small regulatory networks that can explain
mechanistic differences. To this end, we developed SCANet, an all-in-one Python
package that uses state-of-the-art algorithms to facilitate the workflow of a combined
single-cell GCN and GRN pipeline including inference of gene co-expression modules
from scRNA-seq, followed by trait and cell type associations, hub gene detection, coregulatory
networks, and drug-gene interactions. To illustrate the power of SCANet, we
examined data from two studies. First, we identify the drivers of the mechanotype of
a cytokine storm associated with increased mortality in patients with acute respiratory
illness. Secondly, we find 20 drugs for 8 potential pharmacological targets in cellular driver
mechanisms in the intestinal stem cells of obese mice. SCANet is available as a free, open
source, and user-friendly Python package that can be easily integrated in systems biology
pipelines.Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 202
Root colonization ability of herbicide-resistant PGP bacteria evaluated by 16S rRNA metabarcoding
Get PDFIn terms of agricultural sustainability, herbicide-resistant, plant growth promoting (PGP)
bacteria that can improve crop yield are valuable resource. To exhibit PGP traits, the
bacteria must be able to colonize and survive in the rhizosphere.
Upon screening the herbicide-resistant bacterial collection, candidates with the highest
PGP potential were grouped into three consortia to evaluate their ability to colonize roots
and persist in the natural/local plant microbiome in the pot. Experiments were conducted
with seeds of commercial maize hybrids under controlled conditions, with and without
herbicide. Colonization ability was evaluated by examining multiple plants from each
treatment at two-time points during the experiment. 16S rRNA amplicon community
profiling was performed to precisely target the bacterial strains used in the three
consortia and investigate how the local microbiome might be altered by the application of
the consortia. Bioinformatic analysis was performed using qiime2, clustering of reads into
amplicon sequence variants ASVs using the DADA2 plugin, and the taxonomic assignment
was based on a customized dataset formed from the 16S rRNA gene sequences of the
ten isolates used in this study or by using the Silva rRNA database. For clustering and
comparison of ASVs based on sequence similarity, the program cd-hit was used, with
the sequence similarity parameter set to 98% to be considered part of the same cluster.
The obtained dataset was imported into R using the package qiime2R, and subsequent
analyzes and graphs were generated using either the R packages phyloseq, microbiome,
or reshape2. We identified seven out of ten inoculated strains in both time points tested
and with comparable abundance, indicating that most of the bacterial isolates tested have
the ability to colonize the root system of maize. Furthermore, the natural/local microbiome
of maize plants is not disturbed by the three consortia used in this study, implying that
they are good candidates for future biotechnological applications.Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 202
Expression of PET-hydrolyzing enzymes in Streptomyces spp.
Get PDFPlastic waste has become a serious global challenge that calls for sustainable solutions and requires
rapid actions. Biocatalysis could present an adequate answer to this problem by providing different
enzymes capable of degrading plastic polymers. Streptomyces strains as predominant soil
inhabitants have also adapted to the presence of variety of plastic waste in natural environments,
so they have been examined for the plastic degrading capabilities.
The aim of this work was to improve the biocatalytic properties of Streptomyces strains for their
use in biodegradation of plastic polymers and develop a system for heterologous expression of
polyethylene therephtalate (PET) degrading enzymes in Streptomyces spp.
Well studied Streptomyces lividans TK24 and S. albus NRRL B-1335, as well as two newly
isolated Streptomycetes were used for expression of benchmark PETases and cutinases. Enzymes
were cloned into pGM1202 Escherichia coli–Streptomyces shuttle vector and subsequently
introduced into Streptomyces hosts either by polyethylene glycol-mediated protoplasts
transformation or by electroporation. Cell-free extracts and supernatants of transformed cells were
tested on different plastics using bis(2-hydroxyethyl) terephthalate (BHET), polycaprolactone
(PCL) and Impranil as substrates in plate assays.
Expression of leaf-branch compost cutinase in S. albus and S. lividans resulted in an 8.5- and 2.5-
times increase in esterase activities, respectively. Introduction of the enzyme into newly isolated
strains that already showed some plastic degrading activity resulted in synergistic activity of the
recombinant strains.10th FEMS Congress of European Microbiologists, Hamburg, Germany from July 9 -13th, 2023