imagine (Institute of molecular genetics and genetic engineering)
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ASSOCIATION OF CYP2E1 RSAI AND TNF-A PROMOTER VARIANTS WITH ONSET OF ALCOHOL-RELATED LIVER CIRRHOSIS
No full textOnly a minority of excess alcohol drinkers develop alcohol-related liver cirrhosis (ALC).
Chronic alcohol consumption elevates CYP2E1 activity, leading to increased levels of celldeleterious
reactive oxygen species (ROS), and stimulates Kupffer cells to secrete
proinflammatory cytokine tumor necrosis factor-α (TNF-α). These factors sensitize hepatocytes
and lead to chronic liver injury and cirrhosis. Our study aimed to estimate the association of
CYP2E1 RsaI and ΤNF-α promotor variants (-238G˃A and -308G˃A) with ALC susceptibility.
A total of 118 patients with ALC and 131 sex- and age-matched healthy controls were clinically
examined and genetically tested. DNA was extracted from peripheral blood lymphocytes and
genotyping was performed using PCR-RFLP for each variant. The polygenic risk score (PRS)
based on these three SNPs was computed, and binary logistic regression was used to obtain
odds ratios.
Carriers of the CYP2E1 c2 allele had 2.89 times higher risk of developing the disease (OR=2.89,
95% CI=1.30-6.41; p=0.009). Concerning the TNF-α -238G>A variant, a significant
association between A allele carriers and the risk of ALC (OR=2.36, 95% CI=1.15-4.83;
p=0.019) was observed. No significant differences were found in either the genotype or allelic
frequencies of the − 308 TNF-α variant (p=0.463). Patients with ALC had a higher PRS than
controls (0.079 vs. 0.030; p=0.0027).
The -238 TNF-α –A and CYP2E1 c2 alleles were associated with a higher risk of ALC. Further
development of PRS (inclusion of more variants) will enhance the identification of at-risk
patients.VII Congress of the Serbian Genetic Society Zlatibor; October 2 to 5, 2024
Bacterial diversity of Serbian agricultural soils
No full textThe diversity and abundance of rhizosphere microbiota significantly influence plant growth. Interactions within this microbiota, as well as with plants, are complex and varied, ranging from beneficial to harmful. Despite increasing research on these plant-microbe relationships, much remains unknown. Our hypothesis is that rhizosphere microbiota vary across different agricultural soils and may include strains useful for plant protection. Three soil samples were collected from the open field (OF) and protected field (PF) used for vegetable production, as well as intact soil from nearby woodlands (W) in S. Palanka. Bacterial isolation and cultivation was done using complex media that favor slower growing and usually spore-forming representatives. Bacterial diversity present in 3 direct soil samples (designated as OF.D, PF.D and W.D) and in 3 cultivated samples (designated as OF.C, PF.C and W.C) was assessed by partial next-generation sequencing of the 16S rDNA using primer pair targeting V3-V4 hypervariable region of 16S rRNA gene. In direct samples, 26 (OF.D), 30 (PF.D) and 19 (W.D) bacterial phyla were detected. Over 80% of bacterial Amplicon Sequence Variants (ASVs) in all direct samples were classified to 6 phyla: Actinobacteriota, Proteobacteria, Acidobacteriota, Firmicutes, Chloroflexi and Myxococcota. Only 3 phyla (Firmicutes, Proteobacteria and Actinobacteriota) were detected in all 3 cultivated samples and >65% of all ASVs were classified to phyla Firmicutes. In these samples, 244 (OF.D), 235 (PF.D) and 148 (W.D) bacterial genera were detected, including Bacillus Rhodococcus, Streptomyces, Sphingomonas and Gaiella. In OF.D sample, besides unclassified bacteria (44.8%), most abundant genera were: Rhodococcus (9%), Nocardioides (4.6%), Bacillus (4%), Streptomyces (3.1%), Gaiella (2.6%), as well as Skermanella, Sphingomonas, Lysobacter, Rubrobacter and Gemmatimonas (with 1.2% relative abundance each). PF.D sample contained, besides unclassified bacteria (57.5%), Bacillus (5.8%), Rhodococcus (4.2%), Sphingomonas (1.9%), Gaiella (1.6%), Longispora (1.3%), Hydrogenispora (1.3%), Pedomicrobium (1.1%) and Streptomyces (1%). In W.D samples, besides unclassified bacteria (42.8%), most abundant genera, with relative abundance >1%, were: Micromonospora (10%), Streptomyces (9%), Bacillus (7.4%), Sphingomonas (4.2%), Solirubrobacter (2.1%), Bradyrhizobium (1.9%), Gaiella (1.5%), Rhodococcus (1.4%), Pseudonocardia (1.4%), Nocardioides (1.2%) and Kribbella(1.2%). In cultivated samples, 25 (OF.C), 20 (W.C) and 17 (PF.C) bacterial genera were detected. Culturing conditions drastically reduced bacterial diversity and heavily favored growth of Bacillus in all cultivated samples. Most abundant genera, with relative abundance >1%, for OF.C sample were Bacillus (80.2%), Rhodococcus (8.4%), Mitsuaria (2.7%), Paenibacillus (2.6 %) and Cupriavidus (1.3%); for PF.C sample: Bacillus (65.4%), Lysobacter (21.4%), Rhodococcus (4.1%), Ensifer (4%) and Paenibacillus (1.5%); and for W.C sample: Bacillus (62.2%), Cupriavidus (30.3%), Brevibacillus (1.9%), Paenibacillus (1.8%), Burkholderia-Caballeronia-Paraburkholderia group (1.3%) and Achromobacter (1.2%). These results showed differences in complexity of the rhizospere microbiota affected by soil type and management and will be used as a starting point for further study of bacterial population in order to select species beneficial for plant growth and protection
Signaling function of NH4+ in the activation of Fe-deficiency response in cucumber (Cucumis sativus L.)
No full textNH4+ is necessary for full functionality of reduction-based Fe deficiency response in plants.
Nitrogen (N) is present in soil mainly as nitrate (NO3–) or ammonium (NH4+). Although the significance of a balanced supply of NO3– and NH4+ for optimal growth has been generally accepted, its importance for iron (Fe) acquisition has not been sufficiently investigated. In this work, hydroponically grown cucumber (Cucumis sativus L. cv. Maximus) plants were supplied with NO3– as the sole N source under –Fe conditions. Upon the appearance of chlorosis, plants were supplemented with 2 mM NH4Cl by roots or leaves. The NH4+ treatment increased leaf SPAD and the HCl-extractable Fe concentration while decreased root apoplastic Fe. A concomitant increase in the root concentration of nitric oxide and activity of FRO and its abolishment by an ethylene action inhibitor, indicated activation of the components of Strategy I in NH4+-treated plants. Ammonium-pretreated plants showed higher utilization capacity of sparingly soluble Fe(OH)3 and higher root release of H+, phenolics, and organic acids. The expression of the master regulator of Fe deficiency response (FIT) and its downstream genes (AHA1, FRO2, and IRT1) along with EIN3 and STOP1 was increased by NH4+ application. Temporal analyses and the employment of a split-root system enabled us to suggest that a permanent presence of NH4+ at concentrations lower than 2 mM is adequate to produce an unknown signal and causes a sustained upregulation of Fe deficiency-related genes, thus augmenting the Fe-acquisition machinery. The results indicate that NH4+ appears to be a widespread and previously underappreciated component of plant reduction-based Fe deficiency response
GENOMICS AS A BASIS FOR PRECISION MEDICINE
No full textAlthough medicine always aimed to be personalized, true implementation of personalized medicine in
health care practice has started recently. Fascinating progress of molecular genetics has strongly
contributed to this great achievement of modern medicine. Personalized medicine, also known as
genome-based medicine and precision medicine, uses the knowledge of molecular basis of the disease in
order to individualize treatment for each patient. Development of novel powerful high-throughput
technologies has enabled better insight into “oms” landscape of many diseases, resulting in application
of precision medicine approaches in their treatment. There are four cornerstones of modern precision
medicine: “omics”-based diagnostics, pharmacogenomics, specific molecular targeted, gene and cellular
therapy and predictive genomics. One of the most important successes of precision medicine is a
discovery of novel diagnostic molecular markers. Furthermore, numerous newly discovered molecular
markers have contributed to more precise classification of patients in distinct prognostic groups, leading
to specific, more successful treatment protocols. Development of pharmacogenomics platforms and
application of molecular–targeted therapy have led to the individualization of therapy, tailored to genetic
profile of a disease in each patient. The development of gene therapies which can cure or prevent a disease
by targeting disease-causing molecular defect has confirmed that the precision medicine has responded
successfully to a great challenge. Additionally, cellular and tissue therapies have opened new possibilities
for personalized treatment of many patients. Growing knowledge in predictive genomics leads to the
preventive medicine, the most important goal of modern medicine. There is no doubt that we are getting
closer to full implementation of precision medicine in every day clinical practice.Book of abstracts: 2nd B&H Symposium of Laboratory Geneticists and Molecular Biologists
(with International Participation)
May, 202
Data analysis and modelling of climate and environmental drivers of vector borne diseases - some methodological approaches and challenges of OneHealth data
No full textDue to climate change and environmental degradation the spread and the connected risk
of vector-borne diseases are spatially shifting. This requires better understanding and
more detailed modelling of climate and environmental drivers of those diseases, to assist
efficient and timely preparedness of primarily public and veterinary health systems for
such changes, true applications for dedicated information like early warning systems.
Here several modelling approaches and data analysis techniques that can be used for
such purposes will be presented. These are employed in two projects – CLIMOS and
PLANET4HEALTH. Finally, experiences and challenges of use of One Health datasets of
sand fly borne diseases and mosquito borne diseases will be shortly discussed.
The CLIMOS consortium is co-funded by the European Commission grant 101057690
and UKRI grants 10038150 and 10039289. The six Horizon Europe projects, BlueAdapt,
CATALYSE, CLIMOS, HIGH Horizons, IDAlert, and TRIGGER, form the Climate Change and
Health Cluster.
The PLANET4HEALTH project is funded by European Commission grant 101136652. The
five Horizon Europe projects, GO GREEN NEXT, MOSAIC, PLANET4HEALTH, SPRINGS, and
TULIP, form the Planetary Health Cluster.Book of abstracts: 5th Belgrade Bioinformatics Conference, Serbia, Belgrade,17-20 june 2024
Mechanism-based classification of SARS-CoV-2 Variants by Molecular Dynamics Resembles Phylogenetic Tree
No full textThe COVID-19 pandemics has demonstrated the vulnerability of our societies to viral
infectious disease. The mitigation of COVID-19 was complicated by the emergence of
Variants of Concern (VOCs) with varying properties including increased transmissibility
and immune evasion. Traditional population sequencing proved to be slow and not
conducive for timely action. To tackle this challenge, we introduce the Persistence Score
(PS) that assesses the pandemic potential of VOCs based on molecular dynamics of the
interactions between the SARS-CoV-2 Receptor Binding Domain (RBD) and the ACE2
residues. Our mechanism-based classification approach successfully grouped VOCs into
clinically relevant subgroups with higher sensitivity than classical affinity estimations
and allows for risk assessment of hypothetical new VOCs. Interestingly, the PS-based
interaction analysis across VOCs resembled the phylogenetic tree of SARS-CoV-2 with
high accuracy and reveals for the first time a clear link between sequence determined
structures and resulting molecular dynamics further demonstrating the predictive
relevance for pandemic preparedness of our approach. Thus, PS allows for early detection
of a variant’s pandemic potential, and an early risk evaluation for data-driven policymaking.Book of abstracts: 5th Belgrade Bioinformatics Conference, Serbia, Belgrade,17-20 june 2024
Z-Flipons conserved between human and mouse are associated with increased transcription initiation rates
No full textA long-standing question concerns the role of Z-DNA in transcription. Here we use a deep
learning approach based on the published DeepZ algorithm that predicts Z-flipons based
on DNA sequence, structural properties of nucleotides and omics data. We examined
Z-flipons that are conserved between human and mouse genomes after generating
whole-genome Z-flipons maps by training DeepZ on ChIP-seq Z-DNA data, then
overlapping the results with a common set of omics data features. We revealed similar
pattern of transcription factors and histone marks associated with conserved Z-flipons,
showing enrichment for transcription regulation coupled with chromatin organization. 15%
and 7% of conserved Z-flipons fell in alternative and bidirectional promoters. We found
that conserved Z-flipons in CpG-promoters are associated with increased transcription
initiation rates. Our findings empower further experimental explorations to examine how
the flip to Z-DNA alters the readout of genetic information by facilitating the transition of
one epigenetic state to another.Book of abstracts: 5th Belgrade Bioinformatics Conference, Serbia, Belgrade,17-20 june 2024
The danger of powerful mitochondria: life-history traits shape the evolution of bird mtDNA
No full textA>G mutation in the mitochondria heavy chain is one of the most discussed types of mutational
signatures: previous studies prove its association with mtDNA chemical damage, caused by
elevated energy production level. Here we study A>G mutation patterns in birds - creatures
with highest energy consumption in the world. We demonstrate a highly expected excess of
A>G mutation frequency in birds compared to mammals. In order to understand biochemical
mechanisms, which govern A>G mutations in birds, we describe a list of associations between
A>G mutation frequencies and life history traits of birds.First, we show that unlike mammals,
birds demonstrate no connection between frequency of A>G mutations and most obvious
chemical damage correlates: body mass, lifespan or basal metabolic rate (BMR). It is a
surprising result, which we interpret as a sign of a highly optimized electron-transport chain,
which produces a very stable level of chemical damage agents in a wide range of “regular”
conditions. However, according to our results, there is a list of “irregular” conditions, which
cause significant change of A>G mutation frequencies. Decrease of A>G mutation is caused by
loss of flight: this change is in line with drastic decrease of energy consumption. We have found
an increase of A>G mutation rate in diving birds (diving hypoxia is known to produce chemical
damage) and in long range migrators (which are known for outstanding peak metabolism
levels). The results are significant after phylogenetic generalized least squares correction and
show strong phylogenetic inertia.Book of abstracts: 5th Belgrade Bioinformatics Conference, Serbia, Belgrade,17-20 june 2024
INMTD: integrative clustering with 2D genotypes and 3D facial images in the presence of confounders
No full textBy integrating genomic and facial images, we can achieve a more comprehensive, multiview
clustering of individuals. Among the various approaches for multi-view clustering,
integrative nonnegative matrix tri-factorization (NMTF) has emerged as advantageous
in learning low-rank embedding of samples and features and interpreting these
representations. Incorporating 3D imaging is challenging, but here is where nonnegative
Tucker decomposition (NTD) can come in. In this work, we introduce a novel multiview
clustering method based on both NMTF and NTD, namely INMTD, that integrates
genotypes and 3D facial images to generate unconfounded subgroups of individuals.
Indeed, there is a need to handle unwanted drivers of clusterings (i.e. confounders). We
applied our method to real-life multi-view data on 4680 individuals from a US cohort.
Several confounders were also available, such as age, sex and height. When removing
these factors, one would expect population structure to be the prevailing driver for the
heterogeneity. In particular, INMTD generates three embedding matrices for 1) samples,
2) SNPs and 3) facial landmarks. The biological relevance of these embeddings was
investigated in several ways. For 1), most sample embedding vectors were statistically
significantly associated with ancestry axes or confounders. By removing confounded
vectors in the sample embedding, we derived an unconfounded clustering with better
internal quality and stronger association with population structure; the genetic and facial
annotations of each derived subgroup highlighted different physiological or morphological
characteristics. Regarding 2), clusters of embedded SNPs showed good enrichment
of genes and Gene Ontology terms. For 3), the segmentation on the facial embedding
improved cophenetic correlation compared to earlier reports on the same data. Projecting
SNPs and facial landmarks to the sample embedding space revealed known and novel SNPface
biological relationships. In conclusion, INMTD can effectively integrate omics data
and 3D images for unconfounded clustering with biologically meaningful interpretation.Book of abstracts: 5th Belgrade Bioinformatics Conference, Serbia, Belgrade,17-20 june 2024
Machine learning methods for metabolite biomarkers detection
No full textMetabolites provide a unique view of the state of the entire organism. These small
molecules produced by cellular processes can serve as indicators of a significant change
in the body. The latest technological advances enabled measurement of up to a thousand
metabolites from the blood, which paved the way for their usage as biomarkers or
therapeutic activity indicators. The obtained metabolomic data requires special statistical
and machine learning techniques for analyzing datasets with large number of features.
In our study we propose the methodology for processing metabolomics datasets with
samples originating from groups with different phenotypes (e.g. disease and control group)
and detecting metabolite candidates for potential biomarkers. We used preeclampsia
datasets as a case study to test our methodology. The research focus was to determine
whether any of the measured metabolites could indicate the onset of preeclampsia, and if
so, to identify the most significant ones.
The approach to this problem involved developing a XGBoost classifier that would predict
whether a patient has preeclampsia based on measured concentration of metabolites.
For addressing the high dimensionality in the dataset, feature selector mRMR (minimum
Redundancy - Maximum Relevance) was applied. The resulting model with an accuracy of
0.74 and ROC-AUC score of 0.8 on the test data, was used to identify the most important
features that represent potential biomarker candidates. Statistical tests such as the T-test
and Mann-Whitney test additionally confirmed a significant difference in the distribution
of concentration of these metabolites between patients with and without preeclampsia.
We detected increased concentration of specific fatty acids, along with cortisol, the stress
hormone, in patients with preeclampsia. Further research will focus on understanding the
mechanisms underlying these changes and their clinical relevance.Book of abstracts: 5th Belgrade Bioinformatics Conference, Serbia, Belgrade,17-20 june 2024