Journal of Bioinformatics and Systems Biology
Journal of Bioinformatics and Systems Biology is an open access, peer-reviewed, bimonthly online journal encourage researchers to publish new developments in genome bioinformatics and computational biology. Journal publishes all types of articles such as original research, review articles, case reports, short communications, editorials, letter to the editor, etc.
The New Naturalism extends scientific investigation into the previously abjured realm of Choice Causation. Mundane Proximate Teleology is carefully dichotomized from questions of ultimate transcendent metaphysical teleology. The many shortcomings of traditional physicalistic naturalism have been enumerated [1]. Mundane Proximate Teleology is needed to explore the Formalism > Physicality (F >P) Principle on earth [2, 3]. The everyday reality of purposeful controls needed to be addressed, as opposed to mere functionally-indifferent laws, constraints and forces [4]. The natural sciences of genomics, epigenomics, metabolomics and abiogenesis confronted us daily with the need to explain formal rather than mere physicodynamic causation of physical effects. Life in particular manifests undeniable engineering-like choice-based controls that prevail over every aspect of subcellular metabolomics. Homeostatic metabolism must be purposely steered toward functional success. Physicodynamics is blind to refined function and could not have pursued or generated it. Bench scientists should and can be free to study the repeatedly observable reality of intra-cosmic Choice Causation and its very real daily physical effects without paying the slightest attention to purely metaphysical questions of believed or disbelieved ultimate transcendent teleology.
Copyright (c) 2026 Journal of Bioinformatics and Systems Biology
Machine Learning Models and HCC Risk Prediction in Patients with HCV cACLD after SVR to DAA
Background and aims: For HCC detection after SVR to DAA treatment in patients with chronic HCV infection and cALD, a single baseline marker is not sufficient to define a risk profile. A combination of tools and evaluation of dynamic changes are needed to optimize accuracy prediction. We aimed to investigate whether developed ML models based on LSM, laboratory parameters and co-morbidities may help to predict HCC occurrence. Methods: A retrospective prospective cohort of patients who achieved SVR from 2014 to 2024 at a single tertiary referral hospital. Several classes of ML models, namely: LR, DT, XGB, RF, SVM, and NB were tested and compared. Results: The cohort included a total of 1082 patients, each of whom underwent at least one and up to three visits (T0, T1 and T2). Patients who had the first LSM (T1) later than 1 year and patients who had HCC diagnosis before T1, were excluded. The XGB model demonstrated that LSM results at T1 correlate with an increased risk of developing HCC. Higher baseline LSM values (T0), increased glucose (T1) and AST (T1) levels were also moderately associated with an increased risk, as was being of older age. Glucose was found to be a good predictor of increased risk, independently of baseline levels. Also, for LR, LSM results (both at T1 and T0) were one of the most predictive features for increased risk. Maim finding is that XGB and LR substantially outperformed traditional HCC risk scores in predictive performance. Conclusion: LR and XGB were significantly more accurate than traditional HCC risk scores. This work marks an important step toward precision hepatology, demonstrating how dynamic, data-driven models can reshape surveillance.
Most bench scientists would find this question to be absurd. What could possibly be more fundamental to science than the presupposition of at least a necessary “methodological naturalism.” Pre-assuming that everything arises from physicodynamic causation alone has served science well, hasn’t it? But then, there’s the scores of nagging problems that arise from limiting investigation of a perceived reality to nothing more than Monod’s Chance and Necessity. Tension begins with the fact that the scientific method itself would be precluded if the philosophy of physicalistic naturalism were consistently maintained. Next, there’s nothing physical about numerical constants such as pi and the speed of light. All of the physical laws that govern physicodynamics are abstract formal mathematical concepts. The manipulation of these equations is altogether “teleological.” Logic theory loses all substance and reliability in a purely physicalistic epistemology. Our argument for physicalism betrays itself in its necessary use of nonphysical formalisms in trying to make its case. Consciousness, perception, language, and interpretation of results cannot be trusted within a purely physicodynamic ontology. Evolutionary biologists continue to bury their heads in sand regarding the choice-based controls needed for synthetic organic chemistry. Chance and Necessity don’t know what “function” is, and could care less. No rational justification exists for blindly believing that inanimate nature programmed prescriptive infogenesis using representational code. On-the-fly live DNA reprogramming to achieve rapid adaptation has now been documented in multiple species. An RNA stochastic ensemble doesn’t DO anything useful even if it spontaneously “emerged.” But its half-life is only four hours. Eons of time and purely metaphysical multiverses provide no mechanisms. No explanation is offered for the particular chiral-induced spin selectivity so critical to life. The list of inadequacies of physicalism goes on and on [1].
Insights on Structural, Environmental and Dynamical Behaviour of Multi-Tryptophan Proteins by Analysis of Their Low Temperature Phosphorescence Spectra: A Review
The individual amino acids of proteins can experience a variation in their local environments because of the folded tertiary/quaternary structure of the former. These microenvironments are regions that differ from one another with respect to polarizability, polarity and lability of the residues. The three intrinsic fluorescent probes are Tyrosine (Tyr), Tryptophan (Trp) and Phenylalanine (Phe) which form a part of protein backbone. Fluorescence of Phe is weak and rarely used in protein studies. On the other hand, Trp fluorescence is strong. Therefore, most studies of intrinsic protein fluorescence focus on Trp residues, and it provides specific and sensitive information on the structure of proteins and their interaction with probes. In steady-state fluorescence spectra, the shift in λmax of Tryptophan to blue or red regions of the spectrum indicates the solvent accessibility of the former – a red-shifted band indicating significant solvent exposure. Though Trp fluorescence in proteins is an important tool to understand the structural and dynamical evolution of a protein, it is difficult to ascertain the contribution of individual Trp to the total fluorescence of the former. Usually steady-state fluorescence spectra are broad and even a protein with a single Trp residue can have a multi-exponential fluorescence decay upon time resolution. So, low temperature phosphorescence studies (LTP) at 77K in a suitable cryosolvent is an important tool from structural and biological aspects. LTP gives a structured spectra with definite (0-0) band characteristics of the Trp environment. In phosphorescence, a blue-shifted maximum is an indication of solvent exposed Trp whereas a red-shifted maximum indicates a buried Trp. Several proteins containing more than one Trp residues are known to exhibit multiple (0,0) bands. Usually, the no. of bands should correspond to the no. of Trp residues present in the protein. Non-correspondance between the two (no of Trp residues and no of bands) may be attributed to energy transfer to another residue, interaction with neighbouring residues or electron transfer from excited state. This review is an attempt to focus and analyze the low temperature phosphorescence spectra of multi-tryptophan proteins which will eventually throw light on the structure and function of such proteins or protein-substrate complexes. LTP of mutated proteins have also been reported. We have also documented the theoretical modeling studies which help us to understand the assignment of multiple (0-0) bands corresponding to different Trps in multi-Trp proteins.
At what point does a theoretical protocell/protobiont come to life [1-13]? A cell becomes alive by definition when it is observed to be actively Processing Efficacious Executable Choice-Commanded Causation and Control (PEECCCC) [14-22] . No inanimate entity has ever been observed to manifest this trait/capability. Every universally agreed-upon free-living cell without exception manifests this trait/capability. Viruses and viroids correspond best to non-living thumb drives. Prions are nothing more than misfolded proteins. Suspended animation is a very special case of discontinued active ongoing living process. This means that entities in suspended animation have the potential to come to life, but are not currently living. The dichotomy between life and non-life is not a gray-scale transition. This definition provides a digital, crystal-clear, yes-or-no absolute. A cell is either actively exercising this unique functional process, or it is not. PEECCCC is a fully testable and falsifiable working definition of “life.” It is all-encompassing of the entire array of living organisms, whether single-celled; multi-celled; sterile/nonreproducing (e.g., the “mule problem”); and currently evolving or not evolving. PEECCCC is applicable to Monera, Protista and on up to the remaining three Kingdoms of life. Interestingly enough, this definition is also fully applicable to NASA and astro/exobiology pursuits. “Biosignatures” include the functional processing devices designed and engineered by subcellular life to process its own undeniable cybernetic programming. PEECCCC is alone what produces life’s formal computational halting and its programming of extraordinary degrees of “end user freedom.”
What exactly distinguishes life from non-life? Is the dichotomy a black/white absolute, or a slow “gray scale” transition? We don’t normally consider life vs. death to be “relative.” Genomics and epigenomics continue to amass irrefutable evidence of causes and their effects that cannot be reduced to Monod's Chance and Necessity [1-6]. All known life is formally programmed using physical symbol vehicles in Material Symbol Systems (MSSs). The uniqueness of life is specifically defined by syntaxes of semantic Efficacious Executable Choice Command Controls (EECCCs). Cybernetic processing is accomplished only through agreed-upon conformance by programming, machinery and recipient to arbitrary formal rule conventions. Computation cannot be achieved by physicodynamic laws, forces, constraints, quantum mechanics or irreversible nonequilibrium thermodynamics. The prescription of biofunction successfully traverses Shannon channels across The Cybernetic Cut on the one-way narrow Configurable Switch (CS) Bridge from formalism into physicality[7-9]. The result is formal computational halting within the material world. Hundreds of conceptual integrated circuit components cooperate to achieve homeostatic metabolism far from equilibrium. What clearly defines life’s uniqueness is EECCC. The Physicodynamic Incompleteness Theorem [10] firmly predicts that no physicalistic model of abiogenesis will ever elucidate the causation of life’s EECCC on the near physicodynamic side of The Cybernetic Cut.
Typically ignored in evolutionary biology is the perfectly legitimate scientific question: “Mutations of WHAT?” What exactly is being altered? What programmed the initial genome? Are all polymorphisms pointless, or are some prescribed? The object acted upon by mutations was already programming and cybernetically executing extraordinary computational biofunction. Otherwise, no organism, let alone the fittest, would be alive to differentially survive and reproduce. What orchestrated the first symphony of homeostatic protometabolism far from equilibrium in a naturalistic, inanimate, prebiotic environment? Whatever this something is that is so subject to mutation even uses various superimposed, multi-dimensional sign/symbol systems to represent and process its semiotic commands. Shannon’s measure of Uncertainty is far from Szostak’s “functional information,” [1,2] and even further from Abel’s more refined “Prescriptive Information (PI)” [3-5]. We settle for measuring mere general, nonspecific statistical possibility in large phase spaces. We fail to appreciate that irreversible nonequilibrium thermodynamics lacks utilitarian direction, purpose, and efficaciousness. No explanation is provided for specific steering, control and the formal organization of life. The mere self-ordering phenomena of Prigogine does not begin to explain the integration of circuits or the formal orchestration of biosystems. What crafted initial protometabolic schemes? What was the source of Prescriptive Information (PI), recipe, commands and the execution of computational halting? What engineered the first subcellular nanocomputers and stunning molecular machines at the same time and place as the first programs? If mutations are so critical to evolution, why don’t we regard what is being mutated as being far more critical?.
Copyright (c) 2025 Journal of Bioinformatics and Systems Biology
Spontaneous Mutations vs. Prescribed Polymorphisms Review Paper
Spontaneous, undirected, pointless “mutations,” whether random or nonrandom, must be differentiated from “prescribed polymorphisms.” Mutations are one-time unique events that occur with no goal or steering toward utility. Purposeless Mutations may be caused by nonrandom physicodynamic factors such as ionizing radiation and carcinogens. But purposeless mutations, nonrandom included, are never “directed” toward any biofunction. This is also true of all natural forces, laws, quantum events, and irreversible nonequilibrium thermodynamics. None of these have ever been observed to pursue or generate nontrivial utility. Prescribed polymorphisms, on the other hand, are both nonrandom and “directed.” Abundant empirical evidence continues to mount that polymorphic programming refinements are quite intentional. The genome not only regularly expands its phase space of polymorphic options, but the genome also actively selects and controls which polymorphisms to employ to meet abrupt and severe environmental challenges. Programming decisions must be active, not passive (not just secondary and after-the-fact of later phenotypic fitness). Programming precedes and prescribes the computation of homeostatic metabolism and the phenotypic fitness of living organisms. Without algorithmic optimization at the genomic level, natural selection would have no fittest organisms to select. Evolution is way too slow to explain so many empirically rapid adaptations. Prescribed Polymorphic Adaptation (PPA) results from efficacious cybernetic commands executed from programming modules called up into upper memory in rapid response to environmental challenges.
DeepLung: A Novel Lung Cancer Recurrence Prediction Model Using Deep Learning.
Lung cancer recurrence represents a critical determinant of patient prognosis, posing a significant threat to survival outcomes. The development of reliable recurrence prediction tools is therefore clinically imperative to guide therapeutic decision-making and improve both survival and quality of life. The model comprehensively analyzes tissue features from all designated regions of interest (ROIs) identified in pathological reports to predict lung cancer relapse probability.Validation through timedependent receiver operating characteristic (ROC) analysis demonstrated robust predictive performance. Survival analysis using semi-parametric Cox proportional hazards models confirmed the model's superiority over conventional TNM staging, with statistically significant improvements in AUC values (p<0.05). This prediction model exhibits substantial clinical translational potential, providing a valuable foundation for personalized treatment strategies and emerging as a novel decision-support tool for prognostic management.
Rutin: A Novel A2AR Antagonist with Promising Therapeutic Potential for Cancer Immunotherapy
Tumor cells evade immune surveillance through various mechanisms, including the upregulation of inhibitory immune checkpoints like the A2AR receptor. Targeting these checkpoints to reactivate anti-tumor immunity has become a major focus of cancer immunotherapy research. Recent efforts have explored the potential of natural molecules to modulate these checkpoints, aiming to minimize the side effects and high costs associated with antibody-based immunotherapies. In this study, we investigated rutin and eugenol, two naturally occurring compounds with reported therapeutic benefits, as potential A2AR antagonists. Our in silico docking analysis reveals that rutin binds to A2AR with high affinity and strong binding force, engaging three key amino acid residues implicated in tumor progression. This suggests that rutin may function as a potent A2AR antagonist, potentially reversing immune suppression and hindering tumor development. Furthermore, in vitro experiments using human peripheral blood mononuclear cells (PBMCs) demonstrated that rutin is non-toxic up to 900μM and, notably, exhibits a dose-dependent effect on succinate dehydrogenase (SDH) activity, a marker of mitochondrial function and metabolic activity. These findings suggest that rutin not only binds strongly to A2AR but also influences PBMC activity, warranting further investigation of its therapeutic potential in cancer immunotherapy.
High Confidence Artificial Intelligence (AI) Predictions in Glaucoma Detection: A RIM ONE Database Study
Introduction and Objectives: Glaucoma is a progressive optic neuropathy that can lead to irreversible blindness. This study evaluates the use of neural networks in glaucoma prediction with high confidence. Patients or Materials and Methods: The RIM One dataset was used, training an EfficientNetV2B0 model on fundus images. A 95% threshold was set for high-confidence predictions. Results: Sensitivity was 91% and specificity was 99%. Applying the highconfidence threshold increased the AUC to 100%. Conclusions: This study demonstrates the feasibility of using highconfidence AI predictions for glaucoma diagnosis, improving clinical relevance.
Leveraging Eye Fundus Images and Metadata Fusion for Glaucoma Detection with Artificial Intelligence
Background/Aims: Accurate diagnosis of glaucoma relies on precise imaging techniques and comprehensive clinical data. While deep learning methods hold potential for enhancing diagnostic accuracy, the incorporation of clinical data into these models remains relatively unexplored. Methods: In this study, we utilized the PAPILA dataset to investigate the integration of clinical data into machine learning models for glaucoma diagnosis. Two neural network architectures were compared: one trained solely on retinal fundus images and another incorporating both images and clinical data. The performance of these models was evaluated using standard metrics, including the DeLong test for statistical significance. Results: Our findings reveal that the inclusion of clinical data resulted in a modest improvement in classification performance. However, the difference in performance between models using only images and those incorporating clinical data was not statistically significant according to the DeLong test. Conclusion: Integrating clinical data into machine learning models for glaucoma diagnosis holds promise for enhancing diagnostic accuracy. While our study demonstrates a positive trend in classification performance with the inclusion of clinical data, further research is warranted to fully understand its impact and explore additional avenues for improvement.
Predicting Intraocular Pressure using Neural Networks: Incorporating Eye Fundus Images and Clinical Data from PAPILA Dataset
Importance: Glaucoma, a leading cause of irreversible blindness, necessitates accurate intraocular pressure (IOP) prediction for early detection and management. The integration of deep learning algorithms with clinical data presents a novel approach to enhance diagnostic accuracy and patient care in ophthalmology, addressing a critical gap in current diagnostic methodologies. Objective: To assess the effectiveness of deep learning models, specifically Segformer and EfficientNetV2B0, in predicting IOP when combined with clinical data and eye fundus images, aiming to improve diagnostic accuracy and management of glaucoma. Design, Setting, and Participants: This cross-sectional study utilized the PAPILA database. The study employed publicly available databases G1020, ORIGA, and PAPILA, incorporating retinal fundus images from patients diagnosed with glaucoma. It focused on leveraging clinical data such as central corneal thickness, age, gender, axial length, and refractive defect for predictive analysis. Exposure(s): Participants were exposed to deep learning algorithm-based analysis, integrating clinical data with retinal fundus images to predict IOP. Main Outcome(s) and Measure(s): The primary outcome was the accuracy of the IOP predictions, evaluated using Mean Absolute Error (MAE), Coefficient of Determination (R-squared), and Root Mean Squared Error (RMSE). The model's performance was assessed based on its ability to accurately predict actual measured IOP values. Results: The study analyzed images and clinical data from patients within the PAPILA database. The deep learning model achieved an MAE of 2.52, indicating moderate accuracy in predicting IOP. The R-squared value was reported at 0.10, reflecting the model's limited capacity to explain variance in IOP values among the study population. Conclusions and Relevance: The findings suggest that deep learning algorithms, when integrated with clinical data, have the potential to predict IOP with a moderate level of accuracy. This innovative approach could significantly impact the management and diagnosis of glaucoma. This study underscores the potential of AI in revolutionizing ophthalmic diagnostics, particularly for glaucoma, although further validation and improvement of these models are necessary before clinical application.
Theoretical 2D protein gel of Helicobacter pylori
Two-dimensional (2D) gel electrophoresis affords us a qualitative view of the proteome of an organism, which offers a rich understanding of the molecular weight and isoelectric points of proteins expressed under given environmental and nutritional conditions. But, clustering of proteins of similar molecular weight or isoelectric point or both makes separation of proteins difficult. To this end, a theoretical 2D protein gel calculated based on experimental proteome of an organism would provide important clues to the ability of 2D gel electrophoresis to separate two target proteins. More importantly, a theoretical 2D gel is a visualisation of the distribution of isoelectric points and molecular weights of all proteins in the species, which is important to gaining an approximate but high-level understanding of the physiological properties of the organism. This work presents the theoretical 2D protein gel of Helicobacter pylori.
The Common Denominator of All Known Lifeforms
All lifeforms are invariably mediated by Formal Controls. Controls direct physicodynamic events toward integrated biofunction and successful computation. Constraints and laws have no perception of, interest in, or ability to pursue function. Physicodynamic boundary conditions do not steer physicality toward “usefulness.” Neither does irreversible nonequilibrium thermodynamics or quantum mechanics. All aspects of Chance and Necessity are blind to utility. To achieve Function, Controls must be chosen from among real physical options. Life is universally programmed at bona fide “decision nodes,” not mere “bifurcation points.” Programming is a form of Prescriptive Information (PI). The latter cannot be reduced to probabilism. Life’s formal functions depend squarely upon active programming selections made prior to computational “halting.” Secondary passive selection (natural selection) plays no role in molecular evolution. Even the protocellular metabolomic models of ProtoBioCybernetics require the steering of purposeful Choice Causation rather than mere Chance and Necessity. Life values, pursues and maintains being alive. Atomic theory offers no explanation for life’s Controls or Function. Choice Causation is the third fundamental category of reality that comprises the common denominator of all lifeforms at the subcellular and intercellular level. All Sustained Functional Systems (SFS) require Maxwell’s demon’s desire, intent and choices to temporarily circumvent the 2nd Law. The science of biology cannot be crammed into purely physicalistic fanaticism.
All known life is: • Multi-dimensionally programmed • Instructed by Functional Informational • Representationally symbol-system prescribed • Coded • Transcribed and translated • Successfully cybernetically processed (“halts”) • Is literally computed • Algorithmically orchestrated • Processed by ingeniously integrated circuits These are all formal concepts, not mere mass/energy interactions. Thus, life is fundamentally abstract, conceptual and formal. Its formalisms are instantiated into mass/energy physicality using physicodynamically-inert configurable switch-settings. Representational Material Symbol Systems (MSS) are also used to prescribe logical functions instructed by Prescriptive Information (PI). Physical symbol vehicles (tokens) are sequenced according to rules, not laws. These rules are shared by source and destination governed only by arbitrary pre-agreements, not by laws or physical constraints. Most of life’s instructions are prescribed by coded programming which is often superimposed and multi-dimensional. This programming must be cybernetically processed by nanocomputers. Formal functional work, not just the work of physics, is done by ingenious molecular machines. Life exists in the form of Sustained Functional Systems (SFS) that continually compute interdependent, highly cooperative haltings. Integrated circuits orchestrate into physicality homeostatic metabolism. Active selections alone achieve sustained far from equilibrium defiance of 2nd Law thermodynamic propensities. Life is not a thermodynamic state. Life consists of prescribed computational processes.
CDC42 Regulatory Patterns Related To Inflammatory Bowel Disease and Hyperglycemia
As a member of the rat sarcoma virus homolog (Rho) guanosine triphosphatases (GTPases) family, Cdc42 represents a “switch“ molecule, by changing from inactive (GDP-associated) to active form (GTPassociated) and vice versa. Cdc42 is activated by the guanine nucleotide exchange factors (GEFs), in contrast to GTPase-activating proteins (GAPs) which are responsable for formation of GDP-binding, inactive form of Cdc42. Some of the fundamental cellular functions are regulated by Cdc42 such as cytosceleton dynamics, cell cycling, transcription and cellular trafficking. In the gastrointestinal system, Cdc42 participates in maintenance of the functional epithelial barrier by controling intestinal epithelial cell polarity and interconnections. In addition, Cdc42 expression in pancreatic β-cells is of great importance for glucose-stimulated insulin secretion. From the pathophysiological point of view, literature data provide some evidence for Cdc42 sigaling in inflammatory bowel disease, as well as in hyperglycemic conditions related to diabetes mellitus. However, whether and by which mechanism Cdc42 contributes to the IBD patophysiology in hyperglycemic conditions is still not fully understood. Therefore, we performed bioinformatics analysis to predict transcriptional factor-gene interactions related to Cdc42 signaling in inflammatory bowel disease in hyperglycemic conditions. In silico analysis predicts various interactions between input genes and output transcriptional factors, and therefore reveals the molecules with the highest predicted effect on particular genes. Based on the predictive interactions with the intracellular molecules, carefully designed in vitro or in vivo studies are required to get better insight in the pathways of interest. Better understanding of Cdc42 molecular pathway in inflammatory bowel disease and hyperglycemia will help identifying potential targets for therapeutical modifications in clinical setting resulting in better control of the disease progression.
Genomics to Notebook (g2nb): Extending the Electronic Notebook to Address the Challenges of Bioinformatics Analysis
We present Genomics to Notebook (g2nb), an environment that combines the JupyterLab notebook system with widely-used bioinformatics platforms. The analyses and visualizations within those platforms are presented as cells in a notebook, making thousands of genomics methods available within the notebook metaphor and allowing notebooks to contain workflows utilizing multiple software packages on remote servers, all without the need for programming.
CytoBatchNorm: An R package with Graphical Interface for Batch Effects Correction of Cytometry Data
Innovation in cytometry propelled it to an almost “omic” dimension technique during the last decade. The application fields concomitantly enlarged, resulting in generation of high-dimensional high-content data sets which have to be adequately designed, handled and analyzed. Experimental solutions and detailed data processing pipelines were developed to reduce both the staining conditions variability between samples and the number of tubes to handle. However, an unavoidable variability appears between samples, barcodes, series and instruments (in multicenter studies) contributing to "batch effects" that must be properly controlled. Computer aid to this aim is necessary, and several methods have been published so far, but configuring and carrying out batch normalization remains unintuitive for scientists with a purely biological university education. To address this challenge, we developed an R package called CytoBatchNorm that offers an intuitive and user-friendly graphical interface. Although the processing is based on the script by Schuyler et al., the graphical interface revolutionizes its use. CytoBatchNorm enables users to define a specific correction for each marker in a single run. It provides a visualization that guides you through quickly setting the correction for each marker. It allows corrections to be previewed and inter-marker effects to be checked as the settings are updated. CytoBatchNorm will help the cytometry community to adequately scale data between batches, reliably reducing batch effects and improving subsequent dimension reduction and clustering.
Copyright (c) 2024 Journal of Bioinformatics and Systems Biology
Machine Learning Reveals Two Distinct Transcriptomics Subtypes of Acute Myeloid Leukaemia with Differences in Disease Outcomes and Genetic Landscape
Background: Accurately subtyping diseases, particularly in cancer research, is crucial for enhancing the precision of treatment decisions and improving outcomes across various cancers, including hematological malignancies like acute myeloid leukaemia (AML). Methods: Consequently, we utilised an unsupervised K-means clustering on transcriptomics data from 173 acute myeloid leukaemia samples profiled by The Cancer Genome Atlas (TCGA). In our analysis, we categorised patients into two distinct groups: Subtype-1, comprising 68 individuals, and subtype-2, encompassing 105 individuals. Results: Analysis revealed that individuals within subtype-2 experienced a markedly prolonged period of disease-free survival compared to those in subtype-1, as evidenced by the Log-rank test (p = 0.00273). Furthermore, it was observed that patients in subtype-1 presented with elevated white blood cell counts, suggesting a potential biomarker of disease progression within this subgroup. We also identified differentially expressed genes linked to poor survival, prognosis, and chemoresistance, involving pathways like Aminoacyl-tRNA biosynthesis, apoptosis, NF-kappa B and HIF-1, through bioinformatics analysis of subtype-1. Our findings show that AML patients categorised within subtype-1 exhibit a more aggressive form of the disease compared to those allocated to subtype-2. Conclusion: Consequently, these observations underscore the feasibility of employing subtype-specific precision treatments for AML patients, offering a tailored therapeutic approach based on the distinct disease characteristics of different patient subtypes.
Glophyt: a User-Friendly, General-Purpose Program for Nonlinear and Multidimensional Curve Fitting via a Hybrid Stochastic and Deterministic Approach
Model validation depends on the agreement between the predicted and experimental data. However, finding solutions to problems, described by equations with many parameters, where even their orders of magnitude are not known, is a difficult task. This makes curve fitting very difficult in case of multidimensional and nonlinear data. This article presents a graphical user interface-based program employing a hybrid stochastic and deterministic approach, which allows for easy and reliable determination of model parameters by minimizing the differences between measured data and those calculated on the basis of a mathematical expression. The program has been extensively used in several laboratories and has proven to be efficient in determining model parameters in many different fields, such as pharmacological studies of ligand?receptor binding, entomological studies of populations, bacterial growth, photosynthesis, toxicology, differential scanning calorimetry, isothermal titration calorimetry and nuclear magnetic resonance spectroscopy. It is an effective solution for researchers facing the problem of estimating model parameters from multidimensional and nonlinear data where the orders of magnitude of parameters are not known.
Cas9AEY (Cas9-facilitated Homologous Recombination Assembly of Non- Specific Escherichia Coli Yeast Vector) Method of Assembling Large-Sized DNA.
Saccharomyces cerevisiae is widely used in DNA assembly due to their efficient homologous recombination [1], but DNA assembly through yeast recombination in vivo usually requires the vector to have the ability to replicate in yeast. The CRISPR-Cas9 system can efficiently edit DNA [2,3], and the system can also be used for DNA editing of plasmids. In this paper, a yeast universal element is selected, which can be inserted into the vector, so that the vector can replicate in yeast cells, and then the intermediate plasmid containing yeast universal element can be obtained by recombination in yeast. At the same time, a pCas-SmR plasmid was designed in this paper. After Donor DNA is added, the CRISPR-Cas9 system can accurately and efficiently knock out the yeast universal element in the intermediate plasmid, remove the pCas-SmR plasmid through sucrose screening, and finally obtain a pure plasmid. Saccharomyces cerevisiae cells are widely used in DNA assembly due to their efficient homologous recombination [1], but DNA assembly through yeast recombination in vivo usually requires the vector to have the ability to replicate in yeast. The CRISPR-Cas9 system can efficiently edit DNA [2,3], and the system can also be used for DNA editing of plasmids. In this paper, a yeast universal element is selected, which can be inserted into the vector, so that the vector could replicate in yeast cells, and then the intermediate plasmid containing yeast universal element can be obtained by recombination in yeast. At the same time, a pCas-SmR plasmid was designed in this paper. After Donor DNA is added, the CRISPR-Cas9 system can accurately and efficiently knock out the yeast universal element in the intermediate plasmid, remove the pCas-SmR plasmid through sucrose screening, and finally obtain a pure knocked out plasmid.
Gene Cluster Expression Index for IgA Nephropathy
from microarray and next-generation sequencing (NGS) have made possible for large-scale data analysis for biomarker discovery and pathogenesis. IgA nephropathy (IgAN) is the most popular nephritis in eastern Asia and in the world, but its pathogenesis has not been fully elucidated. We hereby study the gene cluster expression abnormality of IgAN compared to other kidney diseases and healthy controls using tissue microarray data. Methods: Publicly available needle biopsy tissue gene expression data sets for IgAN, other kidney diseases, and healthy control (HC) were used to develop the gene cluster expression analysis for nine representative pathways (clusters). By combining single variate prediction using ROC (receiver operating characteristic) and keyword search of gene function, nine gene clusters were heuristically determined. The gene expression status (up, down, normal) of a given gene was firstly determined by ROC, and then the gene cluster expression status, called the gene cluster expression index (GCEI), was determined by the percentages of abnormally expressed members. At last IgAN risks were assessed in the spectrum of GCEIs. Results: Samples were classified into normal (GCEI=0) or abnormal (GCEI=1) for nine predefined gene clusters respectively, and were classified into one of 10 combinatory cGCEI groups. The percentages of IgAN differed dramatically among the groups. The percentages in the abnormal groups (GCEI=1) ranged from 40% to 70% while the percentages were from 16% to 30% in the normal groups (GCEI=0) in both training and testing data sets. The percentages of IgAN corresponding to cGCEIs trended up when the number of abnormal clusters went from 0 to 9, starting from 8.06% to 88.24%. Conclusions: The binary GCEI is proposed to indicate whether a cluster of genes is normal (0) or abnormal (1) in terms of gene expression, and the categorical combinatory cGCEI represents the number of abnormal gene clusters. They are highly correlated to different IgAN risks so that they can be used as novel disease molecular sub-typing tools for future IgA nephropathy management and treatments.
Overview of SARS COVID-2: An Emerging Spike Mutations in Variants and Immune Evasion
Severe acute respiratory syndrome Coronavirus 2 is a novel coronavirus strain that causes coronavirus illness was originally connected to a serious respiratory illness in late 2019 and since March 2020, has brought about a pandemic. The Coronaviridae family includes the beta coronavirus referred to as SARS-CoV-2. The family is made up of single-stranded (+) ribonucleic acid viruses. There are four coronavirus genera, with viruses known to damage humans located in the alpha and beta genera. These zoonotic viruses can spread from animals to humans. Coronaviruses' spike proteins help in the binding of ACE2 receptors and viral entrance into hosting cells. Although spike protein aids the entrance of viruses via receptors, additionally, it is crucial as an immunogen since it is the part that is easiest to access the virus structure. The four structural proteins of severe acute respiratory coronavirus are N (nucleocapsid), M (membrane), E (envelope), as well as S (spike). The original Wuhan strain was replaced by variants with a wide range of mutations, while 4000 mutations in the alterations in the Coronavirus S protein were recently discovered. Several additional varieties have subsequently evolved and become widely distributed, which include Alpha (B.1.1.7), which was initially discovered in the United Kingdom, Beta (B.1.351) in South Africa, Gamma (P.1 & P.2) in Brazil, Delta (B.1.617.2) in India, and Omicron in South Africa. Eight variants of interest have been identified by the World health organization since the start of the pandemic, including Epsilon (B.1.427 and B.1.429), Zeta (P.2), Eta (B.1.525), Theta (P.3), Iota (B.1.526), Kappa (B.1.617.1), Lambda (C.37), and Mu (B.1.621). Efficiency of vaccinations could be hindered by the quick emergence and expansion of SARS-Coronavirus-2 strain induced by receptor-binding domain and N-terminal domain alterations in the Beta spike protein, which could evade neutralizing antibodies and/or cell-mediated immunity. Although several COVID-19 vaccines, among these, are viral vector and mRNA vaccines, have been designed, more potent vaccines are still required to meet the needs on a global level. Modifying the medicine Remdesivir and Oseltamivir is the best method for creating potent treatments to eliminate COVID-19 and other viral human infections. The SARS-Coronavirus-2 S protein's most prominent alterations are reviewed in this article.
Deciphering Collagen Phenotype Dynamics Regulators: Insights from In- Silico Analysis
Collagen (Col) types I and III are integral components in wound healing and tissue regeneration, influencing tissue development, homeostasis, and related pathologies. Col I and Col III expression changes during different stages of wound healing and understanding the regulation of collagen phenotype determination is crucial for unraveling the complexities of these processes. Transcription factors and microRNAs, directly and indirectly, play a critical role in regulating collagen expression, however, a comprehensive understanding of the factors regulating Col I and III phenotypes remains elusive. This critically analyzed published reports with focuses on various factors regulating the expression of Col I and Col III at the transcriptional and translational levels. We performed bioinformatics analysis with an input of proinflammatory mediators, growth factors, elastases, and matrix metalloproteinases and predicted transcription factors and microRNAs involved in the regulation of collagen expression. Network analysis revealed an interaction between genes, transcription factors, and microRNAs and provided a holistic view of the regulatory landscape governing collagen expression and unveils intricate interconnections. This analysis lays a founda-tional framework for guiding future research and therapeutic interventions to promote extracellular matrix remodeling, wound healing, and tissue regeneration after an injury by modulating collagen expression. In essence, this scientific groundwork offers a comprehensive exploration of the regulatory dynamics in collagen synthesis, serving as a valuable resource for advancing both basic research and clinical interventions in tissue repair.
Molecular Dynamics Simulation of TDP-43 RRM in the Presence and Absence of RNA
Structural characterization of the prion prone TAR DNA Binding protein (TDP)-43 has been challenging since its intrinsically disordered regions represent 15-30% of the total protein. TDP-43 is a nucleic acid binding protein with an N-terminal domain, two RNA Recognition Motifs (RRM1 and RRM2) and the C-terminal domain. In this study, we seek to define possible new targetable sites on the apo structure of TDP-43 RRM domains. To do so, we used molecular dynamic (MD) simulations on the NMR solved TDP-43RRM1-2 structure bound to RNA to predict the apo structure. Contact analysis of TDP-43 showed that while the integrity of the individual domains was maintained upon RNA removal, a decrease in interdomain contacts was observed. Moreover, we compared apo TDP- 43 structures obtained from MD to AlphaFold 2 (AF2) predicted TDP-43 structures and found differences in loop regions. Sitemap on predicted apo RRM and RNA bound structures revealed fewer pockets in the RNA-free compared to the RNA-bound structures.
A Method for Filtering Abnormal Modified Base Calling in Oxford Nanopore Technologies Sequencing
Nanopore Technologies (ONT) provides a cost-effective option for gene synthesis quality control. Despite the advantage of using long reads, however, accurate base calling is influenced by modified bases. Results: We introduce a method for filtering abnormal modified base calling in Oxford Nanopore Technologies sequencing. This method is based on the mapping results and perform an exact binomial test on the proportion of single base forward and reverse chain depth to determine the presence of abnormal modified base calling. Based on Sanger sequencing results, this method detected 96.79% of effective abnormal modified base calling, and the accuracy of gene synthesis sequencing increased from 99.40% before calibration to 99.98% after calibration, significantly reducing the proportion of requiring secondary gene synthesis sequencing. Conclusion: This method can accurately filter abnormally modified base calling sites, reduce the proportion of using Sanger for secondary sequencing, thereby saving costs, and improving efficiency.
Discovery of a Novel Merbecovirus DNA Clone Contaminating Agricultural Rice Sequencing Datasets from Wuhan, China
HKU4-related coronaviruses belong to the same merbecovirus subgenus as Middle Eastern Respiratory Syndrome coronavirus (MERSCoV), which causes severe respiratory illness in humans with a mortality rate of over 30%. The high genetic similarity between HKU4-related coronaviruses and MERS-CoV makes them an attractive subject of research for modeling potential zoonotic spillover scenarios. In this study, we identify a novel coronavirus contaminating agricultural rice RNA sequencing datasets from Wuhan, China. The datasets were deposited with NCBI by the Huazhong Agricultural University in early 2020. We were able to assemble the complete viral genome of a novel HKU4-related merbecovirus. The assembled genome is 98.38% identical to the closest known full genome sequence, Tylonycteris pachypus bat isolate BtTp- GX2012. Using in silico modeling, we show that the novel HKU4-related coronavirus spike protein likely binds to human dipeptidyl peptidase 4 (DPP4), the receptor used by MERS-CoV. We further show that the novel HKU4-related coronavirus genome has been inserted into a bacterial artificial chromosome in a format consistent with previously published coronavirus infectious clones. Additionally, we have found a near complete read coverage of the spike gene of the MERS-CoV reference strain, and identify the likely presence of a HKU4-related-MERS chimera in the datasets.
A New Method and Application of PacBio Sequencing for Low Copy and Difficulty Preparation Plasmids
The Single Molecule Real Time (SMRT) system developed by Pacific Biosciences applies the principle of synthesis while sequencing and uses the SMRT chip as the sequencing carrier. The high starting amount and good integrity of DNA required by PacBio library construction has always been a headache. Generally, the total loading volume of PacBio sequencing is 10μg, and the concentration is not less than 200ng/μL, which is a huge challenge for low-copy plasmids. Tight plasmid means that when the bacterial chromosome replicates once, the plasmid replicates once, and each bacterium only contains 1 to 2 plasmids. The replicon of the plasmid determines the copy number of the plasmid. Low copy plasmids should produce 0.2-1 μg DNA per ml of LB culture. Low-copy plasmids play a key role in gene synthesis. When vectors are used for expression or other purposes, low-copy plasmids are used to reduce resource consumption caused by plasmid expansion. Low-copy plasmids are also used when plasmids have lethal gene clones. In order to solve the problem of low copy plasmid library database construction in PacBio, we used Phi29 polymerase to perform multiple substitution amplification of low copy plasmid, so as to obtain a large number of high molecular weight DNA to meet the computer requirements of PacBio. In addition, this study also established a PacBio sequencing method for bacterial fluids without the need for plasmid extraction steps, thereby reducing time and money costs.
Analysis and Modeling of Early Estradiol-induced GREB1 Single Allele Gene Transcription at the Population Level
Single molecule fluorescence in situ hybridization (smFISH) can be used to visualize transcriptional activation at the single allele level. We and others have applied this approach to better understand the mechanisms of activation by steroid nuclear receptors. However, there is limited understanding of the interconnection between the activation of target gene alleles inside the same nucleus and within large cell populations. Using the transcriptional coactivator GREB1 gene as an early estrogen receptor (ER) response target, we applied smFISH to track E2-activated GREB1 allelic transcription over early time points to evaluate potential dependencies between alleles within the same nucleus. We compared two types of experiments where we altered the initial status of GREB1 basal transcription by treating cells with and without the elongation inhibitor flavopiridol (FV). E2 stimulation changed the frequencies of active GREB1 alleles in the cell population, and this was independent of FV pre-treatment. In FV treated cells, the response time to hormone was delayed, albeit still reaching at 90 minutes the same levels as in cells not treated by FV. We show that the joint frequencies of GREB1 activated alleles observed at the cell population level imply significant dependency between pairs of alleles within the same nucleus. We identify probabilistic models of joint alleles activations by applying a principle of maximum entropy. For pairs of alleles, we have then quantified statistical dependency between their GREB1 activations by computing their mutual information. To further analyze the time course of GREB1 activation observable at the population level, we have introduced a stochastic model compatible with allelic statistical dependencies, and we have fitted this model to our data by intensive simulations. This provided estimates of the average lifetime for degradation of GREB1 introns and of the mean time between two successive transcription rounds. Our approach informs on how to extract information on single allele regulation by the estrogen receptor from within a large population of cells, and should be applicable to many other genes.