imagine (Institute of molecular genetics and genetic engineering)
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Exploration of Pharmacogenomic Biomarkers in Chronic Immune Diseases Using Single-Cell RNA Sequencing
Get PDFBiological therapies have revolutionized management of the severe cases of Chronic Immune
Diseases refractory to the standard therapies. However, many patients do not respond
to the selected biological therapy, loose response over time, or develop adverse effects. A
personalized approach to treatment of these patients, based on reliable biomarkers is thus
clearly needed.
Non-invasive approaches, such as use of the peripheral blood immune cells, are favored for
novel biomarker discovery. However, the attention has shifted away from the bulk immune
cells and towards specific immune cell sub-populations. Thus, the single-cell RNA sequencing
(scRNA-seq) can prove highly valuable. By simultaneously capturing and profiling all the cells
in a sample, scRNA-seq allows the analysis of cellular heterogeneity and gene expression in
all immune cell sub-populations, targeted or adversely affected by the biological treatment.
In our ongoing research, scRNA-seq was utilized to analyze samples from Inflammatory
Bowel Disease and Childhood Asthma patients with varied response to the biological
therapy. Confounding effects of disease conditions and (biological) therapies on marker
genes were eliminated using computational integration in order to identify conserved
marker genes across all states. It turned out, that a reliable identification of the different
immune cell sub-populations in this setting is quite challenging due to subjective
cell-landscape clustering resolution. Several resolutions and automated annotation
approaches were subsequently tested and validated.A reference-based approach (Seurat-Azimuth) combined with manual cluster validation
proved superior. Alas, manual cluster validation is time consuming. Annotation validation
is important, especially to provide additional insights into unidentified clusters, which are
essential for the identification of predictive biomarkers for personalized therapies in the
vast heterogeneity of immune cell landscapes residing behind pathophysiology of chronic
immune diseases.Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 202
Machine learning approach in inferring main population-level COVID-19 risk factors
Get PDFMachine-learning methods have become indispensable in scientific research as the
amount of available data has grown exponentially in recent years. It is, thus, necessary
to employ various unsupervised and supervised machine learning methods to uncover
the main determinants of COVID-19 transmissibility and severity in the population.
Upon introducing appropriate disease transmissibility and severity measures and
gathering relevant socio-demographic, environmental, and health-related data for the
countries with obtained said measures, we implement several machine-learning-based
approaches to select the most prominent drivers of disease transmissibility and severity.
These approaches include regularization-based linear regression models and more
advanced Random Forest and Gradient Boost methods, which are not limited to the linear
relationships between the features and the response. Principal component analysis was
used for preselection to avoid overfitting, where numerous features were considered for a
relatively small number of observations (i.e., countries/states). As a result, a broad range
of potential COVID-19 risk factors was reduced to several prominent features, selected
robustly by different methods - we further untangle how they, directly or indirectly,
contribute to the transmissibility and severity of the disease. Our results underscore the
evolving nature of COVID-19, from the severity experienced during the first wave to the
emergence of new, highly transmissible variants like Omicron. These insights can guide
public health interventions, vaccine strategies, and policies aimed at reducing the burden
of COVID-19 and effectively managing future waves and emerging variants.Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 202
Beyond the Global Health Security Index: A Machine Learning Approach to Analyzing the Official COVID-19 Deaths and Excess Deaths Data
Get PDFThe Global Health Security Index (GHSI) is designed to assess the preparedness of
countries to deal with infectious disease outbreaks. However, the COVID-19 pandemic
has revealed a paradoxical relationship between the GHSI and the COVID-19 mortality,
with higher GHSI scores being associated with higher death rates. We aimed to explain
this puzzle. To rely on an accurate and robust measure of COVID-19 severity across
countries, we used our model-derived measure instead of the standard Case Fatality
Rate. We employed a range of statistical learning techniques, including non-parametric
machine learning methods, to identify the factors that influence COVID-19 severity in 85
countries. Also, we searched for the predictors of the largely unexplored excess mortality
counts. Our results suggest that the association of higher preparedness, measured by
the GHSI, with higher COVID-19 mortality may be an artifact of oversimplified statistical
analyses used in published studies. In addition, it could be a consequence of misclassified
COVID‑19 deaths, combined with the higher median age of the population and earlier
epidemics onset in countries with high GHSI scores.Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 202
Possible role of estrogen metabolism and aldo-keto reductase activity in chemoresistance of ovarian cancer
Get PDFHigh-grade serous ovarian cancer (HGSOC) is the most aggressive and chemoresistant form of
epithelial ovarian cancer (OC) and is responsible for ~80% of OC-related deaths. OC is associated with
disturbed estrogen action. In postmenopausal patients, estrogens are formed locally from steroid
precursors. Enzymes of the AKR1C subfamily are associated with resistance to chemotherapeutic
agents and are involved in the biosynthesis and metabolism of steroid hormones, thus may
contribute to the growth of hormone-dependent tumors. To date, the interplay of estrogen
synthesis and aldo-keto reductase activity in HGSOC chemoresistance remains unclear.
The aim of this study was to investigate the differences in targeted transcriptomics of HGSOC
cell lines with different sensitivity to carboplatin: OVSAHO, OVCAR-3, Kuramochi, OVCAR-4, Caov-
3, and COV362, and to evaluate the differences in correlation patterns between targeted gene
expression profiles in platinum-sensitive and -resistant patients using publicly available data
(PAD) (cBioPortal).
We first determined the expression of genes involved in estrogen biosynthesis/metabolism (STS,
SULT1E1, HSD17B1, HSD17B2, HSD17B14, PAPSS1, PAPSS2), steroid transport (SLCO1A2, SLCO1B3,
SLCO2B1, SLCO4A1, SLCO4C1, ABCC1, ABCC4, ABCC11, ABCG2, SLC51A, SLC51B), estrogen action
(ESR1, ESR2, GPER) and oxidative metabolism (CYP1A1, CYP1A2, CYP1B1, SULT1A1, SULT2B1,
SULT1E1, UGTB7, COMT, NOQ1, NOQ2, GSTP1), NFE2L2 and AKR1C1-3 by qPCR. Next, by using PAD
we conducted a correlation analysis using the Pearson correlation coefficient for gene expression
data of targeted genes in OC patients. The patients were classified into two groups based on their
response to platinum treatment: sensitive and resistant. The correlation matrix was computed
independently for each group.
Expression analysis revealed that the estrogen receptor ESR2, the efflux transporter ABCG2
and aldo-keto reductase AKR1C1 were highly expressed in the most resistant cell lines COV362
and Caov-3. The mRNA levels of estrogen biosynthesis and oxidative metabolism genes STS,
HSD17B14, NOQ1, and GSTP1 increased with carboplatin resistance in the HGSOC cell lines. These
results indicate the potential of ESR2, STS, HSD17B14, NOQ1, GSTP1, and ABCG2 as predictive
markers for HGSOC chemoresistance. Furthermore, analysis of PAD revealed different correlation
profiles between genes in sensitive and resistant patients. In chemoresistant were found a
moderately to strong positive correlations (p<0.001) between gene pairs including AKR1C1–
AKR1C3, AKR1C1 – NFE2L2, AKR1C1 – SULT1E1, NOQ1 – HSD17B14, COMT – SULT1A1, ABCG2 –
SLC515. In chemosensitive patients was found a strong positive correlation (p<0.001) between
gene pair CYP1B1 – SULT1E1. The correlation differences between sensitive and resistant OC
patients suggest possible gene regulatory networks or molecular interactions contributing to
the heterogeneity of response to platinum in OC. Further studies are ongoing to elucidate the
mechanism of the interplay between local estrogen metabolism and aldo-keto reductase activity
in HGSOC chemoresistanceBook of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 202
Transcriptomic profiling of white blood cells reveals new insights into the molecular mechanisms of thalidomide in children with inflammatory bowel disease
Get PDFThalidomide has emerged as an effective immunomodulator in the treatment of pediatric
patients with inflammatory bowel disease (IBD) refractory to standard therapies. Cereblon,
a component of E3 protein ligase complex that mediates ubiquitination and proteasomal
degradation of target proteins, has been identified as the primary target of thalidomide.
Cereblon plays a crucial role in thalidomide teratogenicity, however it is unclear whether it
is also involved in the therapeutic effects in IBD patients. This study aimed at identifying
the mechanisms underpinning thalidomide action in pediatric IBD. Ten IBD pediatric
patients clinically responsive to thalidomide were prospectively enrolled. RNA-sequencing
and functional enrichment analysis was carried out on peripheral blood mononuclear
cells obtained before and after treatment with thalidomide. RNA-sequencing analysis
revealed 378 differentially expressed genes after treatment with thalidomide. The
most deregulated pathways were cytosolic calcium ion concentration, cAMP-mediated
signaling, eicosanoid signaling and inhibition of matrix metalloproteinases. Neuronal
signaling mechanisms such as CREB signaling in neurons and axonal guidance signaling
also emerged. Connectivity Map analysis revealed that thalidomide gene expression
changes were similar to those induced by MLN4924, an inhibitor of NEDD8 activating
enzyme, suggesting that thalidomide exerts its immunomodulatory effects by acting on
the ubiquitin-proteasome pathway.
In vitro experiments on cell lines confirmed the effect of thalidomide on altered candidate
pathways observed in patients. These results represent a unique resource for enhanced
understanding of thalidomide mechanism in patients with IBD, providing novel potential
targets associated with drug response.Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 202
The use of tryptic food protein digests data in public proteomic repositories to assess the effects of chemical and post-translational modifications on digestion outcomes
Get PDFPorcine-derived trypsin generated proteomic data of the major peanut allergen Ara h 1
from the peanut was reassessed to search for possible facilitating/hindrance effects on
trypsin digestion efficacy caused by post-translational and chemical modifications (PTMs)
positioned on arginine or lysine (K/R) residues. If the potential effects caused by PTMs are
observed with porcine trypsin, they can be just augmented and more pronounced within
human intestinal digestion. The reasoning is in inferior performance of human trypsin
compared to porcine-derived used in proteomic digestion protocols, also in the lower
trypsin-to-sample ratio and much shorter digestion times, even though gastric digestion
precedes and trypsin is not the sole digestive enzyme.
A novel method was developed to decipher cleavage or miscleavage outcomes at scissile
bonds in each, modified and unmodified sequence counterparts, using PEAKS Studio-X+
(Bioinformatics Solutions Inc., Ontario, Canada) in the reassessment of high-resolution
tandem mass spectrometry data, from 18-hour long trypsin digestion proteomic protocols.
In general, eight site-specific and modified K/R residues with methylation, dihydroxy
and formylation showed significantly higher content of miscleaved bonds (at least >10%)
compared to their unmodified counterpart peptides. Specifically, dihydroxylation and
formylation hindered trypsin efficacy, while methylation on several K/R showed opposite
effects.
It is essential to elucidate the specific impacts of modifications on trypsin digestion
performance and if there are additional effects generated by food processing, which could
influence digestion outcomes and allergenicity of food proteins/peptides.Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 202
Analysis of Long COVID Phenotypes and their Impact on Mental Health and Daily Functioning: Insights from Twitter
Get PDFIn this study, we conducted an investigation into Long COVID from a user perspective, utilizing
Twitter social media data. Prior to analysis, the data underwent preprocessing to obtain raw text
per tweet. Our analysis commenced with basic statistical analysis and subsequently expanded to
identify characteristic periods for the phenotypes based on dynamic timelines. We also explored the
relationships between the phenotypes, as well as the interdependence between phenotypes and
geolocation.
In the context of this research, an analysis was conducted on a collection of tweets that encompassed
the timeframe from March 2020 to March 2022. The dataset consisted of approximately 1.9
million tweets. In order to concentrate on word phrases, extraneous elements such as mentions,
emoticons, links, and hashtags were eliminated. Subsequently, a process of lemmatization was
performed. For the purpose of reducing the number of distinct phenotypes under investigation
and facilitating the presentation of results, the collected data was categorized into five overarching
groups: Cardiovascular, Respiratory, Daily Living, Neurological and Mental Health, and Other.
The statistical data regarding the most commonly used words by individuals describing their
experiences during the Long COVID period are as follows: “Ampicillin” was tweeted 125,295 times,
“Death” was tweeted 121,156 times, “Suffer” was tweeted 125,113 times, and “Vaccine” was
tweeted 108,968 times. We observe distinct patterns in the emergence of certain phenotypes
during this period, particularly in relation to the quality of life. On August 1, 2020, the term “quality
of life” was mentioned in only 223 tweets, whereas one year later, during the same month, this
phenotype garnered 1,663 tweets.
Our findings reveal that the occurrence of Long COVID phenotypes is influenced by both temporal and
geographical factors. The analysis shows a clear and notable trend within the dataset. Specifically,
it is observed that neurological symptoms, along with symptoms that impede individuals’ daily
functioning, exhibit the highest prevalence, particularly during the latter half of the analyzed tweet
period. This period corresponds to a time when an increasing number of individuals have recovered
from COVID-19 and are reporting their experiences with Long COVID. Notably, fatigue, depression,
stress, and anxiety emerge as the most prevalent phenotypes.
This scientific investigation of the complex interactions between Long COVID phenotypes, mental
health, and the manifestation of diverse symptoms is offering insights into the profound consequences
on individuals’ lives. These findings shed light on the significant burden posed by Long COVID and its
cascading effects on various aspects of individuals’ well-being and society at large.Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 202
Short-term effects of Brevibacillus laterosporus supplemented diet on worker honey bee microbiome: a pilot study
No full textIn the current study, honey bees’ diet was supplemented with spores of Brevibacillus laterosporus BGSP11 at concentration of 108 CFU/ml in sucrose solution and its short-term effects on their micro- and mycobiota have been analyzed using Illumina MiSeq sequencing. Obtained results indicate that this treatment does not lead to potentially harmful changes in the bacterial microbiome of worker bees, slightly affecting the composition of core microbiota. Moreover, several potentially beneficial changes have been observed. The treatment has led to a significant increase in the abundance of Snodgrassella alvi, and species from Lactobacillus and Bifidobacterium genera which play important roles in protection against several honey bee pathogens. Simultaneously, B. laterosporus enriched diet have led to almost complete eradication of Enterobacteriaceae family, the taxon that contains several putative pathogen species. On the other hand, the treatment affected mycobiota more profoundly, which was expected considering the greater instability compared to microbiota. Although the observed changes in honey bee mycobiome cannot be considered a priori beneficial or harmful, since the interaction between the bee and its mycobiome has not been sufficiently studied, certain beneficial consequences of the treatment have been observed. They are primarily reflected in the reduction of phytopathogenic fungi that can affect the organoleptic and techno-functional characteristics of honey. In addition, before introducing B. laterosporus in beekeeping practice as a biological agent for pathogen control it is necessary to perform more thorough studies of the impact on the honey bee microbiome, immune system, physiology and economic characteristics of honey bee colonies
Reproduction of Hatchery-Reared Pike-Perch (Sander lucioperca) Fed Diet with Low-Marine-Ingredients: Role of Dietary Fatty Acids
Get PDFThis research aimed to evaluate the reproductive potential of hatchery-reared (F1) pike-perch (Sander lucioperca) broodstock fed a commercial diet with low levels of long-chain polyunsaturated fatty acids (Lc-PUFA) and wild (F0) pike-perch broodstock fed forage fish. Reproductive parameters, including pseudogonadosomatic index (PGSI), egg size, latency time, hatching rate, embryo survival, and eggs’ fatty acid (FA) composition, as well as plasma sex hormone, glucose and immunoglobulin levels after hormone injection, were analyzed. The results showed low PGSI (10% in F1 vs. 14% in F0) and embryo survival (24% in F1 vs. 61% in F0) in F1 broodstock, but a satisfactory hatching rate (63% in F1 vs. 78% in F0) and larval size (4.6 mm in F1 vs. 4.7 mm in F0). A low arachidonic acid (ARA) percentage in F1 fish eggs (1.32%), along with increased immunoglobulin levels (17.31 g/L), suggests that immune system activation might have depleted the reserves of ARA in F1 fish, which is the key fatty acid for successful oocyte maturation. We assumed that the administration of more sustainable diets, based on terrestrial plant ingredients, is not inferior to higher-quality diets based on marine ingredients.Raw data: [https://imagine.imgge.bg.ac.rs/handle/123456789/2075
Supplementary data for the article: Marković, Z., Budimir, M., Danko, M., Milivojević, D., Kubat, P., Zmejkoski, D., Pavlović, V., Mojsin, M., Stevanović, M.,& Todorović Marković, B.. (2023). Structural, optical, and bioimaging characterization of carbon quantum dots solvothermally synthesized from o-phenylenediamine. in Beilstein Journal of Nanotechnology, 14(1), 165-174. https://doi.org/10.3762/bjnano.14.17
Get PDFFigure S1: (a) TEM micrograph of CQDs, (b) top view AFM image of CQDs, (c) height profile of CQDs, and (d) particle size distribution of CQDs.Related to published version: [https://imagine.imgge.bg.ac.rs/handle/123456789/1889]Supplementary data for: [https://doi.org/10.3762/bjnano.14.17