Project goal: The goal of this Project is to develop a biosensor based on optical fibers using a simple fabrication method and explore the possibility of measuring analytes in simulated (artificial) biological fluids for a non-invasive analysis

Project description: Project description: For contemporary biological applications, such as the detection of biomarkers, biosensors are developing into a highly effective and crucial diagnostic and analytical tool. Measuring biomarkers in secreted human physiological fluids such as urine or tear using these tools is a non-invasive way of diagnosis which has a potential to become a promising healthcare technology convenient for both the patient and healthcare professionals. Using optical fiber as a transducer in a biosensor offers such advantages as having low limit of detection, low cost, chemical inertness, wide range of surface modification techniques that can be applied and the potential to be used for remote sensing. But often high complexity of the optical design (interferometric or grating-like resonant structures) that are difficult to fabricate and nearly impossible to replicate repeatedly into an industrial process, are the main barriers to the widespread application of optical fiber technology in the clinical settings. Building all-fiber transducers having no complicated architectures produced by a simplified fabrication process is thus an important task

Project facilitators:
PI: Aliya Bekmurzayeva
Kuanysh Seitkamal
Alina Adilkahnova
Zhuldyz Myrkhiyeva
Zhannat Ashikbayeva
Gulzat Demeuova
Aliya Kurbanova

2023-2025

Expexted results:
- Developed fiber optic sensors
- Results of investigation of sensitivity of the sensors to refractive index change
- Synthesized and characterized nanoparticles
- Fiber optic sensors functionalized with ligands specific to biomarker
- Results of the analysis of surface functionalization
- Results of measuring biomarker levels in artificial biological fluid and/or serum using the biosensor
- Results of specificity studies of biosensor
- Final report

Methodology: Fiber optic splicing, Optical Backscatter Reflectometry, Sensor interrogation, Silanization, AFM (Atomic Force Microscope), SEM (Scanning Electron Microscope), TEM (Transmission Electron Microscope), XRD (X-ray Diffraction), Raman Spectroscopy, FTIR (Fourier Transform Infrared Spectroscopy), TGA (Thermogravimetric Analysis) and DSC (Differential Scanning Calorimetry).

Co-financing: - LLP "AGNKS KAZAHSTAN"

Contacts: abekmurzayeva@nu.edu.kz

Project goal: Improvement the treatment results of diabetic ulcers and chronic wounds by targeted delivery of growth factors and antibiotics to the affected areas

Project description: Diabetic foot ulcers are common cause of amputation of lower extremities, significant morbidity, mortality due to protracted inflammatory process, metabolic diabetic dysregulation, impaired angiogenesis, and immunosuppression. Growth factors regulate inflammatory reactions, stimulate angiogenesis, tissue granulation, formation and remodeling of extracellular matrix.
Compared with acute wounds, chronic wounds have deficiency of growth factors, decrease in level of bFGF, PDGF, EGF and TGF-β. However, in clinical setting, exogenous application of growth factors has limitations due to low stability, absorption difficulty through wound surface, and elimination by wound exudation.
Despite many clinical studies demonstrating positive effect of topical growth factor application, it is impossible to stimulate healing of chronic wounds, without consideration bioavailability and possibility of their accumulation into wound area than systemic circulation.
Selective distribution of growth factors by targeted delivery can provide greatest effect in diabetic wound healing, local high concentration in pathological tissues without affecting healthy organs.
For effective use of growth factors for treatment of chronic wounds, artificial delivery systems (scaffolds, nanoparticles, liposomes, polymers) are used. However, most of proposed carriers are not widely used due to complexity of creation, drug binding, high cost, toxicity, immunogenic incompatibility. Proposed creation of cellular transport containers based on autologous erythrocyte ghosts and preclinical study effectiveness of targeted delivery of growth factors to accelerate diabetic wound healing.
Targeted transport of growth factors can lead to significant acceleration of diabetic wound healing, significantly reduce dose, toxicity, treatment duration and cost, have significant socio-economic effect.
Targeted delivery of promising healing mediators to affected area with protracted inflammatory process and impaired angiogenesis will provide much-needed data and may serve as basis for development of new strategic approaches to treatment of diabetic wounds.

Project facilitators:
PI: Kulzhan Berikkhanova
Zhaxybay Zhumadilov
Alexander Gulyaev
Aliya Bekmurzayeva
Askhat Zhilkaidarov
Yernur Zakirov
Nurgul Danieyeva
Gulnar Magauina

2023-2025

Expexted results:

1. Cellular transport containers for targeted delivery of growth factors and antibiotic will be obtained by encapsulation them into autologous erythrocyte ghosts.
2. Biopharmaceutical studies of erythrocyte transport containers will be conducted by equilibrium dialysis with determination of association/dissociation constants and dynamic stability in vitro.
3. Pharmacokinetics study erythrocyte transport containers with growth factors and antibiotic in laboratory animals will be conducted.
4. Preclinical study of effectiveness of accelerating diabetic wound healing by targeted delivery of growth factors and antibiotic to affected areas in experimental animals will be conducted.
5. Application for Kazakhstan patent will be submitted.
6. Reports will be presented at international conferences.
7. Research results will be published as articles and/or reviews in peer-reviewed scientific publications indexed in Science Citation Index Expanded of WebofScience database and/or having a CiteScore percentile in Scopus database.

Methodology: The inclusion of drugs in autologous erythrocyte ghosts is carried out using the method of hypo osmotic hemolysis, used to obtain transport systems for the targeted delivery of drugs based on autologous erythrocytes. The characteristics of the results obtained are carried out using the database of the contractor's laboratory, including the method of spectrophotometry on the Thermo scientific "Evolution 201" spectrophotometer and the Nazarbayev University database based on the general use of "Core facilities", which favorably affects the speed and quality of the work performed (HPLC, Transmission Electron Microscope (TEM, Jeol JEM-1400 Plus) and Scanning Electron Microscope (SEM, Zeiss Crossbeam 540, confocal laser scanning microscope LSM780, Zeiss fluorescent inverted microscope). A preclinical study of the effectiveness of accelerating the healing of diabetic wounds in experimental animals In vivo is conducted at the vivarium of the National Center for Biotechnology.

Co-financing: - Limited Liability Partnership "R&D engineering".

AP19676334 Study of the DNA strands inheritance in human mitochondrial DNA

Project goal: The aim of the present project is to investigate the DNA strand equivalence of mitochondrial genome of human and yeast cells by applying the logic of Meselson-Stahl experiment.

Project description: Mitochondria are membrane-bound cell organelles that contain their own genomic DNA, referred to as mitochondrial (mt) DNA with unique replication, transcription and translational machinery. Human cell contains several thousand copies of mtDNA, which is organised as a small closed circular DNA duplex of 16,569 bp. An intriguing peculiarity of the vertebrate mitochondrial genome is an unusual misbalance of nucleotide composition on the two strands of the mtDNA, which results in the separation into a heavy (H) G+T-rich and a light (L) C+A-rich DNA strand upon ultracentrifugation in an alkaline cesium chloride gradient. DNA replication in human mitochondria was investigated for several decades; nevertheless, at present, its mechanism and possible relationship to the skewed nucleotide composition remain subject of debate. Previously, we proposed a model of asymmetric DNA strand inheritance or non-equivalence of DNA strands, which provides a simple, heuristic explanation to the highly biased pattern of mutations in vertebrate mtDNA. Our model is consistent with the strand-asynchronous models of mtDNA replication described previously, but contains several new important features, which apparently have not been discussed before. We propose that the H-strand of mtDNA is used as a template for replication more often than the L-strand. Consequently, H-strand breeds the majority of mtDNA progeny, whereas the L-strand of mtDNA is served mainly for transcription and undergoes only one cycle of replication thus leaving few progeny behind. This model provides insight into the origin of skewed nucleotide composition of human mitochondrial genome and to the strand bias in the somatic mutations observed in the mtDNA from aging brain and cancer cells.

Project facilitators: PI: Sabira TAIPAKOVA, Murat SAPARBAEV, Alexander ISHCHENKO, Co-PI: Bakhyt MATKARIMOV, Zhanat KOSHENOV, Eldar BAIKEN, Diana MANAPKYZY, Botagoz DJOLDYBAEVA, Aizhan ALIKUL

Realisation period: 2023-2025

Expected results: Science-wise, the major impacts through the research activities of this project are:

(A) Identification of the preferential bias for the replication of H-strand of mtDNA in human cells, which might explain the mutation pattern and unusual nucleotide composition of vertebrate mtDNA;

(B) Demonstration of the DNA strand equivalence in nucDNA of human and yeast cells, which might explain intra-strand DNA symmetry and patterns of mutations in cellular organisms;

(C) Study of mtDNA replication in post-mitotic cells and in cells exposed to DNA damage will shed light on the mechanisms of regulation of DNA strand equivalence and inheritance.

As far as technological impact of this project is concerned, we will develop and validate the method to measure DNA strand equivalence in mtDNA in living cells. This new tool could be used as a diagnostic marker for mitochondrial diseases. The socioeconomic benefits of the present project would be to define a novel therapeutic paradigm. The modulation of mtDNA replication may have different impacts: increasing the preference for replication of H-strand may increase purifying selection and reduce heteroplasmy; whereas more balanced replication of H- and L-strands may increase the rate of deleterious mtDNA mutations and this could be used in anti-cancer therapy. The knowledge produced in this project on the mechanisms of mtDNA strand inheritance will help to understand patterns of somatic mutations in nuclear and mtDNA, this in turn will help to identify novel diagnostic and therapeutic strategies to combat age-related and mitochondrial diseases.

According to results of the project at least 3 (three) articles and (or) reviews in peer-reviewed scientific publications in the scientific direction of the project, indexed in the Science Citation Index Expanded and included in the 1st (first), 2nd (second) and (or) 3rd (third) quartile by impact factor in the Web of Science database and (or) having a CiteScore percentile in Scopus database of at least 50 (fifty); or at least 2 (two) articles and (or) reviews in peer-reviewed scientific publications indexed in the Science Citation Index Expanded and included in the 1st (first) and (or) 2nd (second) quartile by impact factor in the Web of Science database and (or) having a CiteScore percentile in the Scopus database of at least 65 (sixty-five); or at least 1 (one) article or review in a peer-reviewed scientific publication indexed in the Science Citation Index Expanded and included in the 1st (first) quartile in the Web of Science database or having a CiteScore percentile in the Scopus database of at least 95 (ninety-five).

In addition, at least 1 (one) article or review in a peer-reviewed foreign or domestic publication recommended by the KOKSNVO.

Methodology: Human and yeast cell culturing, cellular DNA isolation, highly sensitive LC-MS. Exonuclease III mediated conversion of dsDNA to ssDNA, Denaturing gel electrophoresis, Southern blot, affinity chromatography. Human and yeast cell culture, cellular DNA isolation, DNA strand separation, LC-MS and/or LC-MSn. Cells treatment with DNA damaging agents, mtDNA purification, DNA strands separation, HPLC-MSn analysis of nucleosides from ssDNA.

Contacts: sabira.taipakova@gmail.com

Development of a software package for high/low risk cancer stratification using machine learning

Project goal: In this project, we propose to develop a software package for the quantitative analysis of immunohistological images using machine learning. The developed tool will make it possible to conduct a more accurate and standardized diagnosis of IHC samples, excluding factors such as bias and subjective assessments of specialists. Also, the program can potentially be used in monitoring the course of the disease and testing the effectiveness of therapy.

Project description: Immunohistochemical examination (IHC) is a tissue analysis method used to diagnose various diseases, including cancers, when standard histology is insufficient or when specific molecular tumor parameters need clarification. It helps identify tumor types by detecting specific proteins, assessing treatment effectiveness, and determining drug sensitivity or hereditary disease predisposition. Routine histology often fails to provide a final diagnosis or identify tumor markers for early cancer detection and treatment selection. IHC results are typically interpreted qualitatively and subjectively, though quantitative assessment provides more accurate marker percentages. Factors like paraffin block quality and pathologist expertise affect result accuracy, as IHC lacks full standardization. Artificial intelligence (AI) enhances IHC by enabling fast, accurate, and standardized analysis of protein expression in tissues. In Kazakhstan, quantitative IHC analysis is not yet used diagnostically, but its implementation could improve diagnostic accuracy and treatment strategies

Project facilitators:
Аршат Уразбаев - PI
Асхат Мынбай
Елдар Байкен
Замарт Рамазанова

Realisation period: 2023-2025

Expected results: We will develop a software package using machine learning for quantitative analysis of immunohistological images, enabling accurate and standardized diagnosis by minimizing bias and subjective pathologist assessments. The tool will monitor disease progression, evaluate therapy effectiveness, and determine optimal therapeutic strategies for patients. We will identify the most efficient machine learning method to analyze IHC samples and train the AI to accurately detect stained areas based on pathologist interpretations. The program will undergo preliminary quantitative analysis of IHC samples and build predictive models. It will be tested with pathologists, therapists, and diagnostic centers, comparing results with clinical parameters like blood/urine tests and expert conclusions. This approach will enhance the program’s accuracy and efficiency, with results informing personalized therapy strategies. Project progress will be published in Scopus-indexed Q1/Q2/Q3 scientific journals

Methodology: The project methodology was implemented by developing a software package for quantitative analysis of immunohistochemical (IHC) images using machine learning, based on the laboratory's experience in tumor segmentation on lung CT. IHC samples were collected from patients with their informed consent at the "Hospital of the Medical Center of the Presidential Administration of the Republic of Kazakhstan", using only high-quality images. The analysis process was divided into three stages: tissue type classification using persistent diagrams to determine topological characteristics, image segmentation using adapted methods such as STARDIST and our own algorithm based on intensity gradient, and cell classification by color and shape using a neural network. Various preprocessing methods were applied for segmentation depending on the tissue type, and cell classification was based on labeled data divided into categories (labeled, unlabeled, false). A neural network with a 100x100 input matrix and three output neurons was trained on two-thirds of the data, and a third was used for control. The segmentation and classification results were tested for cell counting and diagnosis.

Contacts: arshat@gmail.com

Computational methods for collateral mutations analysis

Project goal: This project aims to develop and implement computational pipelines to identify and characterize collateral mutations and consists of three complementary objectives: (i) collection and analysis of NGS/ONT whole-genome sequencing datasets; and new software packages for analysis of (ii) collateral mutations and (iii) evolution of the integral characteristics of genomes.

Project description: Whole-genome sequencing is one of the main drivers of genetics research, producing a huge amount of digital data and creating challenging tasks for effective computer-aided data processing and analysis. This project is aimed to develop and implement computational pipelines to identify and characterize collateral mutations and consists of three complementary objectives: (i) collection and analysis of NGS/ONT whole-genome sequencing datasets; and new software packages for analysis of (ii) collateral mutations and (iii) evolution of the integral characteristics of genomes. This project will accent on cancer collateral mutations studies, especially skin cancers with high UV radiation, as well as on studies of mutational signatures, associated with patient’s DNA repair deficiency, especially Xeroderma Pigmentosum group. Our preliminary results show that clustered mutations provide reliable and intriguing information on the underlying biochemical mechanisms of mutational processes and may help to develop new visions on protection from the sunlight and recommendations for prediction, prevention, and treatment of skin cancer. In this project proposal we are raising new bioinformatics tasks on the analysis of cancer collateral mutations as well as effective data processing: from whole-genome sequencing data high-throughput processing to computational analysis of genomes integral characteristics evolution. This project continues a long-term collaboration between National Laboratory Astana, Nazarbayev University, Kazakhstan, and Gustave Roussy Cancer Campus, France, and starting research collaboration with School of Life Sciences, École Polytechnique Fédérale de Lausanne, Switzerland. On the level of the World-wide studies, we emphasize, that our group is the first one who (i) explores genome-wide mutational patterns in Xeroderma Pigmentosum group C patients beyond cutaneous malignancies (for non-skin cancers), (ii) develop original methodology and software packages to analyze and simulate cancer clustered mutations. First results were published in 2020 in high-impact journal Nature communications. Our bioinformatics software package is open for research community at GitHub code repository, https://github.com/genkvg/clusmut. The most important advantage of the proposed project is a close access to Gustave Roussy, a leading cancer center in Europe, with the unique opportunity to collect and analyze sequencing data for patients with rare genetic disorders in DNA repair. Cutting edge technologies, whole-genome sequencing with modified base detection, allow us to perform quantitative and qualitative assessments of the mutagenesis and other types of genomic instability at whole genome scale. Improved understanding of UV mutagenesis genome-wide on the base of effective computational data processing and analysis, which will be the result of this study, would affect the recommendations for the protection from the sunlight, for the prevention of skin cancer and sunburns. This will be particularly relevant for the patients with Xeroderma Pigmentosum group V, which have a 1’000-fold increased risk of skin cancer as compared to the general population. This study may provide insights for new recommendations for prediction, prevention, and treatment of skin cancer, as well as can open new visions on protection from the sunlight. This project is an interdisciplinary fundamental research; all results will be published in research journals with preferably open access option, commercialization of project results is not expected.

Project facilitators: Pi: B. Matkarimov,
Y. Baiken,
Z. Ramazanova,
A. Imashev,
A. Adilkhanov
B. Nygmetzhanov,
C. Shakenov,
D. Kontsevoi,
M. Kolyvanova

Project partners: Gustave Roussy Cancer Campus, France

Realisation period: 2024-2026

Expected results: "- 30+ complete datasets for human samples, sequenced both by Illumina and Oxford Nanopore Technologies with raw data;

- Oxford Nanopore sequencing dataset with base modifications annotations on raw data.

- Transformer architecture model for Oxford nanopore base calling fine-tuned for modified base calling;

- Processing and analysis of 200+ samples from external datasets and 30+ samples, sequenced in Kazakhstan and partner sites; - Signatures of collateral mutations for skin cancers;

- Estimations of mutations enrichment near putative and observed UV lesions;

- Statistics of regular n-mer patterns on 20+ model organism’s genomes;

- Statistics and classification of observed mutations for processed whole-genome datasets;

- Mathematical model of the genome’s integral characteristics evolution."

Methodology: Data collection and analysis, programming, development and implementation of algorithms and computational methods for analysis and simulation of collateral mutations

Contacts: Bakhyt Matkarimov, bmatkarimov@nu.edu.kz