Biology subjects

Ali, S.

Publications and source records attributed to Ali, S..

At least 55 records · Page 3Linked to original sources

bioRxiv2024

Human-like APOBEC3 gene expression and anti-viral responses following replacement of mouse Apobec3 with the 7-gene human APOBEC3 locus

The seven human APOBEC3 (hA3) genes encode polynucleotide cytidine deaminases that play vital roles in restricting replication of viruses and retrotransposons. However, off-target A3 deamination of the cellular genome is a major source of somatic mutations in human cancer. The ability to study A3 biology in vivo is hindered by the fact that the solitary murine Apobec3 gene (mA3) encodes a cytoplasmic enzyme, with no apparent mutagenic activity. Transgenic expression of individual hA3 genes in mice has helped to confirm their oncogenic potential but important questions including which hA3 genes are active in different tissue contexts and how they function in concert when under control of their cognate promoters cannot be addressed using these models. Here we describe humanization of the mouse mA3 locus by integration of a modified BAC clone encompassing the entire 7-gene hA3 locus from human chromosome 22 replacing mA3 on mouse chromosome 15. APOBEC3 mice are viable and fertile and hA3 gene expression in cells and tissues correlates strongly with expression in corresponding human cells and tissues, indicating human-like regulation of hA3 gene expression in the mice. Splenocytes from this line display a functional human A3 response to Friend Murine Leukaemia Virus (F-MLV) infection. We propose that the Hs-APOBEC3 mouse will uniquely model the function of the complete hA3 locus in a living organism and that it will serve as a useful background upon which to model human cancer, as well as assisting drug discovery efforts.

cancer biology↗

bioRxiv2024

Estrogen Receptor alpha/14-3-3 molecular glues as alternative treatment strategy for endocrine resistant breast cancer

Endocrine resistance in breast cancer treatment is a major clinical hurdle, causing an urgent need for alternative treatment modalities. The suppressive protein-protein interaction (PPI) between Estrogen Receptor alpha (ER) and the adaptor protein 14-3-3 offers such a strategy. Here, we report the biological impact of small-molecule molecular glues of this ER/14-3-3 PPI by using both fusicoccin-derived semi-synthetic natural products and fully synthetic covalent drug-like molecules. We show that the ER/14-3-3 PPI is stabilized by both the natural- and synthetic glues, resulting in a suppression of ER transcriptional activity and a blockade of breast cancer cell proliferation, both in cell lines and in organoids derived from endocrine therapy resistant breast cancer patients. Importantly, the molecular glues effectively blocked ER action even in case of constitutively active clinical ER mutations, providing the foundations for developing alternative classes of ER targeting compounds to improve treatment of patients with endocrine-therapy resistant breast cancer. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/591105v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@173545corg.highwire.dtl.DTLVardef@a78ab7org.highwire.dtl.DTLVardef@183f4b6org.highwire.dtl.DTLVardef@371606_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

A dynamin-like protein in the human malaria parasite plays an essential role in mitochondrial homeostasis and fission during asexual blood stages

Malaria parasite harbors a single mitochondrion and its proper segregation during the parasite multiplication is crucial for propagation of the parasite within the host. Mitochondrial fission machinery consists of a number of proteins that associate with mitochondrial membrane during segregation. Here, we have identified a dynamin-like protein in P. falciparum, PfDyn2, and deciphered its role in mitochondrial division, segregation and homeostasis. GFP targeting approach combined with high resolution microscopy studies showed that the PfDyn2 associates with mitochondrial membrane to form a clip/hairpin loop like structure around it at specific sites during mitochondrial division. The C-terminal degradation tag mediated inducible knock-down (iKD) of PfDyn2 resulted in significant inhibition of parasite growth. PfDyn2-iKD hindered mitochondrial development and functioning, decreased mtDNA replication, and induced mitochondrial oxidative-stress, ultimately causing parasite death. Further, treatment of parasites with dynamin specific inhibitors disrupted the recruitment of PfDyn2 on the mitochondria, blocked mitochondrial development, and induced oxidative stress. Regulated overexpression of a phosphorylation mutant of PfDyn2 (Ser-612-Ala) had no effect on the recruitment of PfDyn2 on the mitochondria; normal mitochondrial division and parasite growth showed that phosphorylation/dephosphorylation of this conserved serine residue (Ser612) may not be responsible for regulating recruitment of PfDyn2 to the mitochondrion. Overall, we show essential role of PfDyn2 in mitochondrial dynamics and fission as well as in maintaining its homeostasis during asexual cycle of the parasite.

cell biology↗

bioRxiv2024

Deep learning methods to forecasting human embryo development in time-lapse videos

BackgroundIn assisted reproductive technology, evaluating the quality of the embryo is crucial when selecting the most viable embryo for transferring to a woman. Assessment also plays an important role in determining the optimal transfer time, either in the cleavage stage or in the blastocyst stage. Several AI-based tools exist to automate the assessment process. However, none of the existing tools predicts upcoming video frames to assist embryologists in the early assessment of embryos. In this paper, we propose an AI system to forecast the dynamics of embryo morphology over a time period in the future. MethodsThe AI system is designed to analyze embryo development in the past two hours and predict the morphological changes of the embryo for the next two hours. It utilizes a predictive model incorporating Convolutional LSTM layers, to predict the future video frame by analyzing prior morphological changes within the embryos video sequence. The system uses the predictions recursively and forecasts up to 23 hours of embryo development. ResultsThe results demonstrated that the AI system could accurately forecast embryo development at the cleavage stage on day 2 and the blastocyst stage on day 4. The system provided valuable information on the cell division processes on day 2 and the start of the blastocyst stage on day 4. The system focused on specific developmental features effective across both the categories of embryos. The embryos that were transferred to the female, and the embryos that were discarded. However, in the transfer category, the forecast had a clearer cell membrane and less distortion as compared to the avoid category. ConclusionThis study assists in the embryo evaluation process by providing early insights into the quality of the embryo for both the transfer and avoid categories of videos. The embryologists recognize the ability of the forecast to depict the morphological changes of the embryo. Additionally, enhancement in image quality has the potential to make this approach relevant in clinical settings. Author summaryThe emergence of assisted reproductive technology has significantly improved infertility treatments. It involves fertilization of an egg outside the body, and the resultant embryos are developed in time-lapse incubators. The embryologists manually evaluate embryos using time-lapse videos and rank each embryo on the basis of several criteria including the dynamics of embryo cell stages, such as the start of the blastocyst stage. Traditional manual analysis is subjective and time-consuming, and AI tools are introduced to automate and enhance embryo selection efficiency. However, current AI tools do not generate video frames that forecast changes in embryo morphology. This study fills this gap by introducing an AI system that forecasts upcoming frames of a time-lapse video. In this approach, several hours were predicted ahead of the last video frame. The system was evaluated on crucial days of embryo evaluation. Our approach was effective in both good quality (transfer) and poor quality (avoid) video categories, and the forecast revealed crucial insights about embryo cell division and the start of the blastocyst stage. Despite some image quality issues, the proposed AI system demonstrated the potential for early and accurate assessment of embryo quality.

cell biology↗

bioRxiv2024

Genetic polymorphisms of Leukocyte Immunoglobulin-Like Receptor B3 (LILRB3) gene in African American kidney transplant recipients are associated with post-transplant graft failure

BackgroundAfrican American (AA) kidney transplant recipients exhibit a higher rate of graft loss compared to other racial and ethnic populations, highlighting the need to identify causative factors underlying this disparity. MethodWe analyzed RNA sequences of pretransplant whole blood from subjects followed in three kidney transplant cohorts to identify single nucleotide polymorphisms (SNPs) associated with death censored graft loss (DCGL). We employed a meta-analysis to uncover key transcriptional signatures and pathways associated with the identified SNPs and used single cell RNA to define cellular specificity. We characterized SNP functions using in vitro immunological and survival assays and tested for associations between the identified SNPs and other immune-related diseases using a [~]30,100 subject, electronic health record (EHR)-linked database. ResultsWe uncovered a cluster of four consecutive missense SNPs in the Leukocyte Immunoglobulin-Like Receptor B3 (LILRB3, a negative immune response regulator) gene that strongly associated with DCGL. This LILRB3-4SNPs cluster encodes missense mutations at amino acids 617-618 proximal to a SHP-1/2 phosphatase-binding ITIM motif. LILRB3-4SNPs is specifically enriched within subjects of AA ancestry (8.6% prevalence vs 2.3% in Hispanic and 0.1% in European populations), is not linked to APOL1 G1/G2 alleles, and exhibited a strong association with DCGL. Analysis of PBMC and transplant biopsies from recipients with LILRB3-4SNPs showed evidence of enhanced adaptive immune responsiveness and ferroptosis-associated death in monocytes. Overexpression of the variant allele in THP-1 cells (macrophage line) induced augmented inflammation and ferroptosis, which were attenuated by a ferroptosis inhibitor, verifying a causal link. The LILRB3-4SNPs also associated with multiple systemic and organ-specific immune-related diseases in AAs, consistent with conferring a broadly relevant immune function. Conclusionthe LILRB3-4SNPs represent a functionally important, distinct genetic risk factor for kidney transplant outcome and development/severity of other immune-related diseases in patients of AA ancestry. Pharmacological targeting of ferroptosis should be tested to prevent or treat these disease processes in AA recipients carrying LILRB3-4SNPs.

genetics↗

bioRxiv2024

Peptide-based capture-and-release purification of extracellular vesicles and statistical algorithm enabled quality assessment

Circulating extracellular vesicles (EVs) have gained significant attention for discovering tumor biomarkers. However, isolating EVs with well-defined homogeneous populations from complex biological samples is challenging. Different isolation methods have been found to derive different EV populations carrying different molecular contents, which confounds current investigations and hinders subsequent clinical translation. Therefore, standardizing and building a rigorous assessment of isolated EV quality associated with downstream molecular analysis is essential. To address this need, we introduce a statistical algorithm (ExoQuality Index, EQI) by integrating multiple EV characterizations (size, particle concentration, zeta potential, total protein, and RNA), enabling direct EV quality assessment and comparisons between different isolation methods. We also introduced a novel capture-release isolation approach using a pH-responsive peptide conjugated with NanoPom magnetic beads (ExCy) for simple, fast, and homogeneous EV isolation from various biological fluids. Bioinformatic analysis of next-generation sequencing (NGS) data of EV total RNAs from pancreatic cancer patient plasma samples using our novel EV isolation approach and quality index strategy illuminates how this approach improves the identification of tumor associated molecular markers. Results showed higher human mRNA coverage compared to existing isolation approaches in terms of both pancreatic cancer pathways and EV cellular component pathways using gProfiler pathway analysis. This study provides a valuable resource for researchers, establishing a workflow to prepare and analyze EV samples carefully and contributing to the advancement of reliable and rigorous EV quality assessment and clinical translation.

bioengineering↗

bioRxiv2024

Genome-wide CRISPR screen identifies AC9 as a key regulator of ER calcium homeostasis involved in neuronal differentiation.

Endoplasmic reticulum (ER) calcium (Ca2+) homeostasis is essential for maintaining normal cellular physiological functions. Its disturbance is strongly linked to the onset and progression of human diseases, including cancer, developmental defects, and neurodegenerative disorders. The lack of sensitive ratiometric ER Ca2+ indicators, nevertheless, hinders systematic investigation of ER Ca2+ modulators and the underlying mechanisms. Capitalizing on two ultra-sensitive ER Ca2+ indicators and CRISPR-based genome-wide screening, we identified a set of proteins capable of reducing the ER Ca2+ content. Further comparative analysis and qPCR validation pinpointed adenylate cyclase 9 (AC9), which is upregulated during neuronal differentiation, as a key ER-Ca2+-reducing regulator. Mechanistically, AC9-mediated production of cAMP is not essential for its ability to reduce ER Ca2+ content. Instead, AC9 inhibits store operated calcium entry (SOCE) by acting on Orai1, ultimately causing attenuation of ER Ca2+ level. More physiologically relevant, upregulation of AC9 in neurons is essential for reducing ER Ca2+ levels during Drosophila brain development. Collectively, this study lays a solid groundwork for further in-depth exploration of the regulatory mechanisms dictating ER Ca2+ homeostasis during neuronal differentiation and brain development.

cell biology↗

bioRxiv2024

BATF controls IFN I production via DC-SCRIPT in plasmacytoid dendritic cells

The basic leucine zipper ATF-like transcription factor (BATF) plays a pivotal role in coordinating various aspects of lymphoid cell biology, yet essential functions in dendritic cells (DCs) have not been reported. Here we demonstrate that BATF deficiency leads to increased interferon (IFN) I production in Toll-like receptor 9 (TLR9)-activated plasmacytoid dendritic cells (pDCs), while BATF overexpression has an inhibitory effect. BATF-deficient mice exhibit elevated IFN I serum levels early in lymphocytic choriomeningitis virus (LCMV) infection. Through ATAC-Seq analysis, BATF emerges as a pioneer transcription factor, regulating approximately one third of the known transcription factors in pDCs. Integrated transcriptomics and ChIP-Seq approaches identified the transcriptional regulator DC-SCRIPT as a direct target of BATF that suppresses IFN I promoter activity by interacting with the interferon regulatory factor 7 (IRF7). Genome-wide association study (GWAS) analyses further implicate BATF in pDC-mediated human diseases. Our findings establish a novel negative feedback axis in IFN I regulation in pDCs during anti-viral immune responses orchestrated by BATF and DC-SCRIPT, with broader implications for pDC and IFN I-mediated autoimmunity.

immunology↗

bioRxiv2023

Early life regulatory T cells modulate PPARγ-dependent skin pigmentation

The maintenance of adult tissue homeostasis is dependent on the functional cross-talk between stem cells (SCs) and tissue-resident immune cells. This reciprocal relationship is also essential for tissue organogenesis during early life. The skin harbors a relatively large population of Regulatory T cells (Tregs) that accumulate within the first two weeks after birth. A functional role for early skin seeding Tregs (ETregs) during the first week of life is currently unexplored. Here, we show that skin Tregs are detected early as postnatal day 3 (P3) where they localize to hair follicle (HF) structures and enter a dynamic flux of activation marker expression. Punctual ETreg depletion from P6-P8, but not later, results in defective HF melanocyte SC (MeSC) mediated skin pigmentation in juvenile life. Transcriptomic analysis of the whole skin on P9 exhibited immediate and pronounced changes in MeSC markers and perturbation of Peroxisome proliferator-activated receptor-{gamma} (PPAR{gamma}) target genes in the HF. Accordingly, punctual ETreg depletion combined with short-term PPAR{gamma} agonization restored skin pigmentation. Single cell profiling of P9 skin revealed that PPAR{gamma} signalling activity is preferentially diminished in the HF epithelium upon loss of ETregs. Finally, we explored changes in the single cell transcriptome of the human tissue disorder, vitiligo, characterized by a lack of melanin and consequent skin depigmentation. These analyses showed that the HF cells from lesional vitiligo skin exhibited a significant downregulation in PPAR{gamma} pathway activation, relative to heathy controls. Overall, ETregs in neonatal skin are critical for sustaining HF PPAR{gamma} signaling, which is vital for facilitating MeSC mediated skin pigmentation during postnatal development. One Sentence SummaryPPAR{gamma} pathway functions downstream of neonatal Tregs to regulate melanocyte stem cell function.

immunology↗

bioRxiv2023

Application of CRISPR-Cas12a technology in zebrafish to simulate beta-globin gene (HBB) mutations

BackgroundThe human {beta}-globin (HBB) gene translated into the hemoglobin subunit beta ({beta}) of the hemoglobin protein. When mutated, it can lead to the blood disorder {beta}-thalassemia. Recently, the CRISPR-Cas12 technology has shown promising potential in treating different genetic abnormalities. ObjectivesThis study aimed to evaluate the feasibility of zebrafish hbbe1.1 gene editing via CRISPR-Cas12a gene-technology. MethodsIn current study, thalassemic mutations were replicated in zebrafish to improve gene editing. the embryonic hemoglobin gene hbbe1.1 was preferred over adult hemoglobin hb{beta}-a1 due to its early detection at larval stages. The CRISPR-Cas12 technology was utilised in combination with phosphorothioated "rescue template" (ssDNA L33P) to introduce base edits to the DNA sequence of zebrafish. ResultsThese experiments indeed resulted in the alteration of a single amino acid at the protein level. With the help of this genetic editing method, we were able to generate a novel zebrafish strain that carried specific amino acid alterations resembling the pathogenic mutations found in HBB for {beta}-thalassemia disease. However, in several cases additional indels or base alterations were observed. ConclusionOur findings suggests that MMEJ double stranded break repair mechanism causes more knock-in events and germline inheritance than HR-mediated events. Modifying the technique could improve results by reducing MMEJ frequency.

genetics↗

bioRxiv2023

CRISPR-Cas9 Extracellular Vesicles for Treating Hearing Loss

Clinical translation of gene therapy has been challenging, due to limitations in current delivery vehicles such as traditional viral vectors. Herein, we report the use of gRNA:Cas9 ribonucleoprotein (RNP) complexes engineered extracellular vesicles (EVs) for in vivo gene therapy. By leveraging a novel high-throughput microfluidic droplet-based electroporation system (DES), we achieved 10-fold enhancement of loading efficiency and more than 1000-fold increase in processing throughput on loading RNP complexes into EVs (RNP-EVs), compared with conventional bulk electroporation. The flow-through droplets serve as enormous bioreactors for offering millisecond pulsed, low-voltage electroporation in a continuous-flow and scalable manner, which minimizes the Joule heating influence and surface alteration to retain natural EV stability and integrity. In the Shaker-1 mouse model of dominant progressive hearing loss, we demonstrated the effective delivery of RNP-EVs into inner ear hair cells, with a clear reduction of Myo7ash1 mRNA expression compared to RNP-loaded lipid-like nanoparticles (RNP-LNPs), leading to significant hearing recovery measured by auditory brainstem responses (ABR). One sentence summaryThe scalable microfluidic electroporation system enables the loading of gRNA:Cas9 ribonucleoprotein (RNP) complexes into extracellular vesicles, which leads to clinical translation potential employed in hearing disease gene therapy.

bioengineering↗

bioRxiv2023

TGF-β blockade drives a transitional effector phenotype in T cells reversing SIV latency and decreasing SIV reservoirs in vivo

HIV-1 persistence during ART is due to the establishment of long-lived viral reservoirs in resting immune cells. Using an NHP model of barcoded SIVmac239 intravenous infection and therapeutic dosing of the anti-TGFBR1 inhibitor galunisertib (LY2157299), we confirmed the latency reversal properties of in vivo TGF-{beta} blockade, decreased viral reservoirs and stimulated immune responses. Eight SIV-infected macaques on ART were treated with four 2-week cycles of galunisertib. ART was discontinued 3 weeks after the last dose, and macaques euthanized 6 weeks after ART-interruption (ATI). 7 out of 8 macaques rebounded between week 2 and 6 post-ATI. Galunisertib led to viral reactivation as indicated by plasma viral load and immunoPET/CT with 64Cu-DOTA-F(ab)2-p7D3-probe. A decrease in cell-associated (CA-)SIV DNA was detected in lymph nodes, gut and PBMC, while intact pro-virus in PBMC decreased by 3-fold. No systemic increase in inflammatory cytokines was observed. High-dimensions cytometry, bulk, and single-cell (sc)RNAseq revealed a shift toward an effector phenotype in T and NK cells characterized by a progressive downregulation in TCF1. In summary, we demonstrated that galunisertib, a clinical stage TGF-{beta} inhibitor, reverses SIV latency and decreases SIV reservoirs by driving T cells toward an effector phenotype, enhancing immune responses in vivo in absence of toxicity. One-sentence summaryTGF-{beta} blockade drives an effector phenotype in immune cells leading to SIV latency reversal and enhanced immune responses in vivo.

immunology↗

bioRxiv2023

Hist2Vec: Kernel-Based Embeddings for Biological Sequence Classification

Biological sequence classification is vital in various fields, such as genomics and bioinformatics. The advancement and reduced cost of genomic sequencing have brought the attention of researchers for protein and nucleotide sequence classification. Traditional approaches face limitations in capturing the intricate relationships and hierarchical structures inherent in genomic sequences, while numerous machine-learning models have been proposed to tackle this challenge. In this work, we propose Hist2Vec, a novel kernel-based embedding generation approach for capturing sequence similarities. Hist2Vec combines the concept of histogram-based kernel matrices and Gaussian kernel functions. It constructs histogram-based representations using the unique k-mers present in the sequences. By leveraging the power of Gaussian kernels, Hist2Vec transforms these representations into high-dimensional feature spaces, preserving important sequence information. Hist2Vec aims to address the limitations of existing methods by capturing sequence similarities in a high-dimensional feature space while providing a robust and efficient framework for classification. We employ kernel Principal Component Analysis (PCA) using standard machine-learning algorithms to generate embedding for efficient classification. Experimental evaluations on protein and nucleotide datasets demonstrate the efficacy of Hist2Vec in achieving high classification accuracy compared to state-of-the-art methods. It outperforms state-of-the-art methods by achieving > 76% and > 83% accuracies for DNA and Protein datasets, respectively. Hist2Vec provides a robust framework for biological sequence classification, enabling better classification and promising avenues for further analysis of biological data.

bioinformatics↗

bioRxiv2023

Efficient Sequence Embedding For SARS-CoV-2 Variants Classification

Kernel-based methods, such as Support Vector Machines (SVM), have demonstrated their utility in various machine learning (ML) tasks, including sequence classification. However, these methods face two primary challenges:(i) the computational complexity associated with kernel computation, which involves an exponential time requirement for dot product calculation, and (ii) the scalability issue of storing the large n x n matrix in memory when the number of data points(n) becomes too large. Although approximate methods can address the computational complexity problem, scalability remains a concern for conventional kernel methods. This paper presents a novel and efficient embedding method that overcomes both the computational and scalability challenges inherent in kernel methods. To address the computational challenge, our approach involves extracting the k-mers/nGrams (consecutive character substrings) from a given biological sequence, computing a sketch of the sequence, and performing dot product calculations using the sketch. By avoiding the need to compute the entire spectrum (frequency count) and operating with low-dimensional vectors (sketches) for sequences instead of the memory-intensive n x n matrix or full-length spectrum, our method can be readily scaled to handle a large number of sequences, effectively resolving the scalability problem. Furthermore, conventional kernel methods often rely on limited algorithms (e.g., kernel SVM) for underlying ML tasks. In contrast, our proposed fast and alignment-free spectrum method can serve as input for various distance-based (e.g., k-nearest neighbors) and non-distance-based (e.g., decision tree) ML methods used in classification and clustering tasks. We achieve superior prediction for coronavirus spike/Peplomer using our method on real biological sequences excluding full genomes. Moreover, our proposed method outperforms several state-of-the-art embedding and kernel methods in terms of both predictive performance and computational runtime.

bioinformatics↗

bioRxiv2023

Unveiling the Robustness of Machine Learning Models in Classifying COVID-19 Spike Sequences

In the midst of the global COVID-19 pandemic, a wealth of data has become available to researchers, presenting a unique opportunity to investigate the behavior of the virus. This research aims to facilitate the design of efficient vaccinations and proactive measures to prevent future pandemics through the utilization of machine learning (ML) models for decision-making processes. Consequently, ensuring the reliability of ML predictions in these critical and rapidly evolving scenarios is of utmost importance. Notably, studies focusing on the genomic sequences of individuals infected with the coronavirus have revealed that the majority of variations occur within a specific region known as the spike (or S) protein. Previous research has explored the analysis of spike proteins using various ML techniques, including classification and clustering of variants. However, it is imperative to acknowledge the possibility of errors in spike proteins, which could lead to misleading outcomes and misguide decision-making authorities. Hence, a comprehensive examination of the robustness of ML and deep learning models in classifying spike sequences is essential. In this paper, we propose a framework for evaluating and benchmarking the robustness of diverse ML methods in spike sequence classification. Through extensive evaluation of a wide range of ML algorithms, ranging from classical methods like naive Bayes and logistic regression to advanced approaches such as deep neural networks, our research demonstrates that utilizing k-mers for creating the feature vector representation of spike proteins is more effective than traditional one-hot encoding-based embedding methods. Additionally, our findings indicate that deep neural networks exhibit superior accuracy and robustness compared to non-deep-learning baselines. To the best of our knowledge, this study is the first to benchmark the accuracy and robustness of machine-learning classification models against various types of random corruptions in COVID-19 spike protein sequences. The benchmarking framework established in this research holds the potential to assist future researchers in gaining a deeper understanding of the behavior of the coronavirus, enabling the implementation of proactive measures and the prevention of similar pandemics in the future.

bioinformatics↗

bioRxiv2023

Advancing Protein-DNA Binding Site Prediction: Integrating Sequence Models and Machine Learning Classifiers

Predicting protein-DNA binding sites is a challenging computational problem in the field of bioinformatics. Identifying the specific residues where proteins bind to DNA is of paramount importance, as it enables the modeling of their interactions and facilitates downstream studies. Nevertheless, the development of accurate and efficient computational methods for this task remains a persistent challenge. Accurate prediction of protein-DNA binding sites has far-reaching implications for understanding molecular mechanisms, disease processes, drug discovery, and synthetic biology applications. It helps bridge the gap between genomics and functional biology, enabling researchers to uncover the intricacies of cellular processes and advance our knowledge of the biological world. The method used to predict DNA binding residues in this study is a potent combination of conventional bioinformatics tools, protein language models, and cutting-edge machine learning and deep learning classifiers. On a dataset of protein-DNA binding sites, our model is meticulously trained, and it is then rigorously examined using several experiments. As indicated by higher predictive behavior with AUC values on two benchmark datasets, the results show superior performance when compared to existing models. The suggested model has a strong capacity for generalization and shows specificity for DNA-binding sites. We further demonstrated the adaptability of our model as a universal framework for binding site prediction by training it on a variety of protein-ligand binding site datasets. In conclusion, our innovative approach for predicting protein-DNA binding residues holds great promise in advancing our understanding of molecular interactions, thus paving the way for several groundbreaking applications in the field of molecular biology and genetics. Our approach demonstrated efficacy and versatility underscore its potential for driving transformative discoveries in biomolecular research.

bioinformatics↗

bioRxiv2023

Enhancing t-SNE Performance for Biological Sequencing Data through Kernel Selection

The genetic code for many different proteins can be found in biological sequencing data, which offers vital insight into the genetic evolution of viruses. While machine learning approaches are becoming increasingly popular for many "Big Data" situations, they have made little progress in comprehending the nature of such data. One such area is the t-distributed Stochastic Neighbour Embedding (t-SNE), a generalpurpose approach used to represent high dimensional data in low dimensional (LD) space while preserving similarity between data points. Traditionally, the Gaussian kernel is used with t-SNE. However, since the Gaussian kernel is not data-dependent, it determines each local bandwidth based on one local point only. This makes it computationally expensive, hence limited in scalability. Moreover, it can misrepresent some structures in the data. An alternative is to use the isolation kernel, which is a data-dependent method. However, it has a single parameter to tune in computing the kernel. Although the isolation kernel yields better performance in terms of scalability and preserving the similarity in LD space, it may still not perform optimally in some cases. This paper presents a perspective on improving the performance of t-SNE and argues that kernel selection could impact this performance. We use 9 different kernels to evaluate their impact on the performance of t-SNE, using SARS-CoV-2 "spike" protein sequences. With three different embedding methods, we show that the cosine similarity kernel gives the best results and enhances the performance of t-SNE.

bioinformatics↗

bioRxiv2023

ERBB signalling contributes to immune evasion in KRAS-driven lung adenocarcinoma

Immunotherapy is increasingly viewed as treatment of choice for lung cancer, however, clinical responses to immune checkpoint blockade remain highly unpredictable and are largely transient. A deeper mechanistic understanding of the dynamics of tumour:immune interactions is needed to drive rational development of improved treatment strategies. Progress is hampered by a paucity of autochthonous model systems in which to interrogate the 2-way interactions of immune responses to evolving tumours and vice-versa. Specifically, commonly used genetically engineered mouse models typically lack the genetic diversity needed to drive an adaptive immune response. APOBEC mutagenesis signatures are prominent in lung cancer and APOBEC activity is predicted to drive immune visibility through Cytidine deaminase activity, coupled with inaccurate DNA-repair responses. We therefore generated a CRE-inducible APOBEC3B allele, interbred with multiple oncogenic drivers of lung adenocarcinoma, and used the resulting mice to investigate the response to PD1 blockade at single cell resolution. SIGNIFICANCE Using our novel immune-visible model of KRas-driven autochthonous lung adenocarcinoma, we uncovered a surprising increase in tumour-cell expression of EGFR/ERBB ligands following treatment with -PD1 and present evidence that transient ERBB blockade can restore immune surveillance in KRas mutant LuAd and combine effectively with immune checkpoint blockade.

cancer biology↗