imagine (Institute of molecular genetics and genetic engineering)
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Modeling of the Hypothalamic-Pituitary-Adrenal Axis dynamics by Stoichiometric Networks
No full textThe hypothalamic-pituitary-adrenal (HPA) axis is a neuroendocrine system that regulates
the body’s response to stress and maintains homeostasis through the secretion of
cortisol, its primary hormone. Dysregulation of the HPA axis is implicated in numerous
stress-related disorders, including obesity, depression, chronic pain, metabolic disorders,
etc. Therefore, understanding the HPA axis is vital for comprehending stress-related
diseases and developing effective interventions. Investigating the dynamic nature of HPA
axis activity presents significant challenge, which can be effectively addressed through
mathematical modelling. Modelling can provide deep insights into the system’s responses
to stress, regulatory mechanisms involving ultradian and circadian rhythms, feedback
loops, and hormonal interactions. Furthermore, modelling the HPA axis facilitates
understanding how various factors influence its functioning, offering a powerful tool
for studying related disorders and developing targeted interventions. Hence, this paper
presents a detailed mathematical modelling approach utilizing stoichiometric networks to
describe the dynamics within the HPA axis. The model captures the interplay of response
strategies in the HPA axis, providing a framework for simulating its behaviour under
different conditions. This model has potential for studying stress modulation, improving
stress management strategies, and addressing health outcomes related to HPA axis
dysregulation.Book of abstracts: 5th Belgrade Bioinformatics Conference, Serbia, Belgrade,17-20 june 2024
Versatile Multi-Sample Single Cell RNA-Seq Pipeline with Extensive Customization Options
No full textSingle-cell RNA sequencing (scRNA-seq) technology has become the state-of-the-art
approach for describing cell subpopulation classification and cell heterogeneity. It allows
addressing medical questions such as the role of rare cell populations contributing to
disease progression and therapeutic resistance.
Presented here is the “Multi-Sample Clustering and Gene Marker Identification with
Seurat 4.1.0”, a highly customizable workflow for single-cell data analysis, implemented in
the Common Workflow Language (CWL). The workflow consists of three steps: 1. Loading
scRNA-seq Expression Datasets, 2. Quality Control and Preprocessing, and 3. Clustering
and Identification of Gene Markers. It supports gene-cell count matrices generated by
several commonly used quantifiers (Cell Ranger counts, STAR solo, Salmon Alevin, Kallisto
BUStools) coming from single or multiple single-cell datasets, different batches, as well as
single or multiple samples combined in a single SingleCellExperiment object.
Each workflow step contains several implemented options, allowing a high level of
customization. The quality control can be performed manually or automatically using
several options for normalization (LogNormalize, Deconvolution, SCnorm and Linnorm)
and batch effect correction (Seurat and Harmony). The workflow utilizes Seurat’s graphbased
approach for clustering, enabling the selection of multiple clustering resolutions.
The identification of gene markers on a cluster level is performed by differential expression
analysis step using various tests (wilcox, bimod, roc, and DESeq2).
To illustrate the utilization of this workflow in a standard single-cell analysis, two open
access datasets containing cells isolated from human pancreatic cells were processed.
Four clustering resolutions were employed to achieve different degrees of granularity,
after which cluster-specific marker genes were identified.
The workflow is available on the Cancer Genomics Cloud (CGC), powered by Seven Bridges
and funded by the NCI. CGC is a flexible cloud platform that ensures fast execution,
scalability and reproducibility of the results, offering over 1000 bioinformatics workflows.
To enable researchers to use this analysis as a guideline, this analysis was made as a
public project.Book of abstracts: 5th Belgrade Bioinformatics Conference, Serbia, Belgrade,17-20 june 2024
ENHANCING GUT HEALTH AND LONGEVITY THROUGH NOVEL STARTER CULTURES IN FERMENTED PRODUCTS
No full textFor nearly 10,000 years, fermented foods have been essential to the human diet, boasting a
significant variety today. The health advantages of fermented foods have been recognized for a long
time. These benefits may result from direct interactions of ingested live microorganisms with the
host, as a probiotic effect. They may also come indirectly as a result of the ingestion of microbial
metabolites synthesized during fermentation. Research suggests that probiotics found in dairy
products have positive effects on human health. In recent years, there has been extensive research
into the use of starter cultures with probiotic characteristics to address various health conditions. A
Western-type diet characterized by a high daily intake of saturated fats and refined carbohydrates
leads to the accumulation of a wide variety of molecular and cellular damages over time, leading to
several age-related diseases. According to the World Health Organization, the number of people
worldwide over 60 years of age was estimated to be 1 billion in 2019, with an expected increase to
2.1 billion by 2050. The increasing number of the elderly population will be accelerated in the
coming decades, particularly in developing countries, such as Serbia. We have tested several
carefully selected natural isolates of lactic acid bacteria, originating from artisanal dairy products
from specific geographical locations in the Balkan peninsula for the ability to decelerate the cellaging
process. Our results revealed that these strains possess exciting probiotic features such as
strengthening the epithelial intestinal barrier through stimulation of autophagy, upregulating the
tight junctions between the epithelial cells of the intestine which prevent the passage of harmful
substances from the intestine to other organs, activating the antimicrobial defense, and extending
the lifespan of Caenorhabditis elegans via autophagy activation, making them great candidates for
probiotic starter cultures for functional dairy food.Book of Abstracts : The 3rd International UNIFood Conference, UNIFood2024 Conference, Belgrade, June 28-29, 2024
EFFECTS OF PROTEOLYTICALLY-ACTIVE LACTOBACILLI STRAINS ON SOURDOUGH STARTER FERMENTATION PROCESS
No full textGluten-related disorders have surged in recent years, underscoring the necessity for innovative
approaches to alleviate symptoms and enhance gluten digestion. In this study, we embarked on a
comprehensive exploration of proteolytic activity within a collection of Lactobacillus strains to
uncover their potential in gluten peptide hydrolysis and sourdough fermentation process. We
initiated our investigation by screening 120 Lactobacillus strains for proteolytic activity on gluten
peptides, with a goal of identifying the most potent candidates. Subsequent probiotic
characterization followed, focusing on antimicrobial capabilities against prevalent pathogens.
Safety characterization ensued, including testing for antibiotic resistance, ensuring the suitability of
selected strains for further investigation, ending in the refinement of our selection to 11 candidates.
To assess their applicability in sourdough fermentation, the selected strains were introduced into
khorasan wheat sourdough starters, and their impact on growth kinetics and pH modulation was
monitored. Among the selected strains, only one strain of Lactobacillus brevis (BGZLS30-24)
demonstrated a significant effect on the growth kinetics and pH reduction of the sourdough starter.
Additionally, protein isolation from the mature sourdough starter facilitated the evaluation of
proteolytic activity within the dough inoculated with the selected strain. While a detectable
proteolytic activity was observed, its magnitude appeared to be attenuated compared to the initial
screening test. Furthermore, metagenomic analysis of sourdough starters was conducted to gain
insights into microbial diversity dynamics during the maturation process, revealing that the addition
of the BGZLS30-24 significantly shortens the microbiota maturation time. Textural analysis of
sourdough breads was conducted to elucidate the impact of BGZLS30-24 on the final product,
revealing its contribution to increased bread volume and reduced hardness, indicating
improvements in textural properties. Our findings show the versatile role of selected Lactobacillus
brevis BGZLS30-24 in gluten digestion and sourdough fermentation, hinting at it’s potential as an
adjunct in developing novel strategies for managing gluten-related disorders and enhancing the
quality of bakery products.Book of Abstracts : The 3rd International UNIFood Conference, UNIFood2024 Conference, Belgrade, June 28-29, 2024
LABBANK – THE FIRST BIOBANK OFAUTOCHTHONOUS PROBIOTIC LACTIC ACID BACTERIA
No full textLactic acid bacteria (LAB) are a group of bacteria that are generally recognized as safe (GRAS) and
are widely used in fermented foods. They are widespread in nature mainly in raw milk and dairy
products. The indigenous microbiota of raw milk directly affects the sensory properties of the raw
milk products. The WHO recommends to the use of fermented dairy, meat and vegetables products
in the daily diet, as LAB have a great impact on human health.
Group for Probiotics and Microbiota – Host Interaction within the Institute of Molecular Genetics
and Genetic Engineering, University of Belgrade has a large collection of LAB (about 5000 various
strains) isolated from artisanal autochthonous dairy products produced in a traditional way without
the addition starter cultures. The characterization of isolated LAB strains was done by classic
microbiological methods and identification by 16S rDNA sequencing. These LAB strains are
collected in previously 30 years and stored in refrigerator at -80℃.
Until now 23 distinctive LAB strains from LABbank collection have been deposited in the Belgian
Coordinated Collection of Microorganisms, University of Ghent, Belgium. The 13 strains have been
licensed through license agreements, as the subjects of innovations:
-HiraVet probiotic for prevention and treatment of intestinal infections in humans and animals.
-Diasolution probiotic for diabetes management.
-Lagendairy starter cultures for innovative dairy products for the production of soft white cheese,
cheese in brine, yogurt and sour cream.
-Sixteen LAB strains are currently the subject of innovations submitted as 2 PoC projects.
Our findings illustrate the importance of the research on natural isolates of LAB as a valuable
source of strains with novel properties, since they can provide a deeper and more complete insight
into the functioning and organization of the comprehensive metabolic system in these bacteria and
their impact on human and animal health.Book of Abstracts : The 3rd International UNIFood Conference, UNIFood2024 Conference, Belgrade, June 28-29, 2024
Establishing highthroughput screening of engineered polyhydroxyoctanoate (PHO) depolymerase variants using novel PHO model compounds
No full textPolyhydroxyoctanoate (PHO) is a biocompatible microbially produced polyester with elastomeric properties currently limited by its high production cost and poor
biodegradability in open environments. Enzymatic recycling of postconsumer PHO offers a biocyclable route to PHO utilization and poses a solution from both
ecological and economical aspects. Enzyme engineering enables industrial efficiency in polymer degradation. Therefore, we have created a mutant library for PHO
depolymerase from Pseudomonas fluorescens GK13 (PfPHOase) using errorprone PCR. To advance the speed of identification of PfPHOase variants with improved
PHO degradation rates highthroughput screening was performed spectroscopically with inhouse synthesized pnitrophenyl esters of 3hydroxyalkanoate monomer (3
HA monomer) and 3hydroxyalkanoic acid dimer (3HA dimer)1. Further, the degradation of PHO polymer for bestperforming PfPHOase variants was assessed on
commonly employed emulsified PHOagarose plates. The obtained results indicate the positive correlation between the degradation of novel PHO model compounds
with degradation of PHO polymer. Novel model compounds have been successfully employed for the identification of beneficial mutations for the advancement of
enzymatic PHO degradation
Label-Free Quantitative Proteomics of Pelargonium zonale: Tissue-Specific Differences
No full textVariegated Pelargonium zonale plants present excellent model for studying metabolite
fluxes and photosynthesis-related processes between the photosynthetically active
(green, vG) and inactive (white, vW) tissues within the same leaf under the same
microenvironmental conditions. We aimed to investigate the proteomic differences
between these two metabolically contrasting tissue types, as well as between vG and
plain morphs (G) to gain more insight into the evolutionary benefits of variegation.
Label-free proteomics was performed using liquid chromatography coupled to tandem
mass spectrometry. The data were searched against the newly expanded RNAseq
database for P. zonale leaves. Analysis was done with X!Tandem, using 10 ppm precursor
mass precision and fragment 0.02 Th mass tolerance. The identified proteins were filtered
using X!TandemPipeline, requiring at least two peptides with E-values lower than 0.01
and a protein E-value<10-5. Peptide ions, and their parental proteins, were quantified by
integrating signal intensities from extracted ion currents (XIC) using MassChroQ software.
A total of 2707 protein groups were identified. After removing dubious data and peptides
with missing values, we obtained 2009 protein group. We annotated 564 and 79
differentially abundant proteins in vG vs. vW and vG vs. G, respectively. The differentially
abundant proteins were mapped into metabolic pathways using the modified MapMan
3.6.0RC1 software. Proteins related to photosynthesis and carbohydrate metabolism
were more abundant in vG compared with vW, while proteins related to oxidative stress
and protein degradation were more abundant in vW than in vG. Compared to vG, G tissue
contained more proteins involved in energy production and protein synthesis. Briefly,
this study has paved the way to uncover the evolutionary advantages of the variegated
phenotype.Book of abstracts: 5th Belgrade Bioinformatics Conference, Serbia, Belgrade,17-20 june 2024
Exploring biotechnological potential of LLDPE- and mixed plastics-degrading bacteria from contaminated soils
No full textGlobal research efforts to develop biocatalytic processes based on microbial enzymes to
alleviate plastic pollution are underway. Despite a proposed overlap between enzymatic
capacity to degrade plastics and lignocellulosic biomass, bioprospecting efforts to identify
microorganisms capable of degrading both types of substrates remain limited.
Plastic and lignocellulose degrading potential of 15 bacterial isolates from polluted Serbian
soils, belonging to genera detected with abundance >1% in virgin plastics (LLDPE)- or postconsumer representative mixed plastics (MS)-enriched 16S metagenomes, was explored.
Plastic polymers Impranil® DLN-SD (SD) and DL2077 (DL), bis(2-hydroxyethyl) terephthalate
(BHET), polycaprolactone diol (PCL) and polylactic acid (PLA) and lignocellulosic polymers
carboxymethyl cellulose (CMC), arabinoxylan (AXYL) and lignin (LIG) were provided as sole
carbon source for the isolates.
16S rDNA dendrogram of 9 LLDPE-enriched isolates from genera Lysinibacillus, Rhodococcus,
Hydrogenophaga, Pseudomonas, Nocardoides and Psychrobacillus and 6 MS-enriched isolates
from genera Pseudomonas, Advenella and Paenibacillus showed no clear grouping, suggesting
that relatively distinct isolates were selected for the screening. No zones of clearing, indicating
complete substrate degradation is taking place, were observed. Single Advenella isolate
demonstrated growth on all plastic substrates, while 11 isolates demonstrated growth on PCL,
10 on BHET, 6 on SD, 4 on DL and 4 on PLA. Fourteen isolates grew on all tested lignocellulosic
substrates.
Genome mining of 3 sequenced isolates using PAZy database identified putative PU-, PET, PCLand PLA-active enzymes in both Hydrogenophaga, growing on all plastic substrates except PLA
and Pseudomonas, growing on BHET and PCL, while Lysinibacillus, with predicted PCL and PLA
activities, demonstrated no growth on any of the tested plastic substrates. Lignocelullolytic
enzymes (GHs, CEs, PLs and AAs) were also predicted in these isolates, demonstrating growth
on all tested lignocellulosic substrates, using CAZy database.
Investigated isolates should be further explored using a wider range of plastic substrates and
screening conditions.Book of abstracts: 5th Belgrade Bioinformatics Conference, Serbia, Belgrade,17-20 june 2024
Using Singular Value Decomposition for Extracting Underlying Gene Expression Patterns in Transcriptomic Analysis
No full textSingular Value Decomposition (SVD) is a mathematical approach that can be useful in
analysis of transcriptome data. SVD is based on transforming gene expression data from
genes × arrays to reduced eigengenes/eigenarrays vector space. In high-throughput
analysis, the goal is often to reduce the dimensionality of data, excluding non-informative
noise, and to extract patterns reflecting biological processes. Arranging the data based
on eigenvectors provides a comprehensive overview of gene expression dynamics, where
individual genes are classified into groups of similar regulation and function, or similar
cellular state and biological phenotype.
Here we applied SVD to microarray gene expression data involving 21,176 genes from
ulcerative colitis patients both glucocorticoid-sensitive (n=20) and glucocorticoidresistant
(n=20). Our aim was to validate SVD based methodology as an alternative to
classical differential gene expression analysis (PMID: 20941359).
The SVD process involves decomposing a matrix Am×n (m=number of genes, n=number
of patients) into three matrices Um×m, Dm×n, and VTn×n . The VT matrix can be utilized to
identify the eigengenes that differ the most between the sensitive and resistant patient
groups. The greatest differences were found for eigengenes 7, 4, and 5 (p=0.007, p=0.078,
and p=0.090, respectively (t-test)). For each eigengene of interest, lists of genes with the
highest absolute values of projection and correlation were identified and used for gene
and disease ontology analysis. Our results showed that the top 40 genes with the highest
projection on selected eigengenes participate in the same five most important biological
processes as the genes obtained from the differential gene expression analysis (PMID:
20941359).
In summary, SVD is a powerful tool for gene expression analysis, capable of isolating
significant biological patterns. Further validation on additional datasets is necessary to
confirm the robustness of SVD compared to more commonly used methods for differential
expression analysis.Book of abstracts: 5th Belgrade Bioinformatics Conference, Serbia, Belgrade,17-20 june 2024
Integration of Whole Exome and Single-Cell Transcriptomic Data Analysis to Identify Potentially Pathogenic Variants in Unicuspid Aortic Valve Disease
No full textUnicuspid aortic valve (UAV) disease is a congenital heart defect that can lead to severe
cardiovascular complications. This study aimed to identify genetic variants contributing to UAV
disease by integrating whole exome sequencing (WES) data with single-cell transcriptomics
of the developing human heart.
WES was conducted on 28 subjects, including 9 with UAV and 19 non-affected family
members by using protocol of the Beijing Genomics Institute (BGI). To refine the candidate
gene set, previously published single-cell RNA sequencing data from 6.5-7 weeks postconception
(PCW) embryonic hearts were utilized [1]. The cell type and differential gene
expression analyses were performed using Python, employing libraries such as Scanpy
and scVI. WES data were filtered for high-impact and damaging missense variants, as
predicted by three independent in silico tools, with an allele frequency of less than 10% in the
GnomAD database, in genes that were notably expressed in cell types involved in aortic valve
development. Subsequently, g:Profiler was utilized to perform functional profiling of the
candidate genes and principal component analysis (PCA) was conducted to identify clustering
patterns among the UAV patients.
The analysis identified 308 candidate variants in 283 genes, the majority of which are crucial
in maintaining and organizing the extracellular matrix, supporting cellular adhesion and
signaling, and contributing to the development of anatomical structures. Among these, 62
genes had damaging variants present in at least two UAV patients. Additionally, 15 novel
variants were identified, not previously reported in the GnomAD database. Eleven variants
were classified as pathogenic or likely pathogenic according to ClinVar or ACMG (American
College of Medical Genetics and Genomics) criteria. Further, the PCA results revealed
significant genetic variation across the UAV patients, with some patients showing closer
proximity in affected gene profiles, suggesting potential clustering.
In conclusion, the approach of integrating WES data with existing single-cell transcriptomics
data provided valuable insights into the genetic underpinnings of UAV disease. The study
identified several novel candidate genes and variants, enhancing our understanding of the
genetic basis of this congenital heart defect and potentially guiding future research, and
diagnostic or therapeutic strategies.Book of abstracts: 5th Belgrade Bioinformatics Conference, Serbia, Belgrade,17-20 june 2024