Unraveling the role of long non-coding RNAs in therapeutic resistance in acute myeloid leukemia: New prospects & challenges.

Acute Myeloid Leukemia (AML) is a fatal hematological disease characterized by the unchecked proliferation of immature myeloid blasts in different tissues developed by various mutations in hematopoiesis. Despite intense chemotherapeutic regimens, patients often experience poor outcomes, leading to substandard remission rates. In recent years, long non-coding RNAs (lncRNAs) have increasingly become important prognostic and therapeutic hotspots, due to their contributions to dysregulating many functional epigenetic, transcriptional, and post-translational mechanisms leading to alterations in cell expressions, resulting in increased chemoresistance and reduced apoptosis in leukemic cells. Through this review, I highlight and discuss the latest advances in understanding the major mechanisms through which lncRNAs confer therapy resistance in AML. In addition, I also provide perspective on the current strategies to target lncRNA expressions. A better knowledge of the critical role that lncRNAs play in controlling treatment outcomes in AML will help improve existing medications and devise new ones.

Pivotal functions and impact of long con-coding RNAs on cellular processes and genome integrity.

Recent advances in uncovering the mysteries of the human genome suggest that long non-coding RNAs (lncRNAs) are important regulatory components. Although lncRNAs are known to affect gene transcription, their mechanisms and biological implications are still unclear. Experimental research has shown that lncRNA synthesis, subcellular localization, and interactions with macromolecules like DNA, other RNAs, or proteins can all have an impact on gene expression in various biological processes. In this review, we highlight and discuss the major mechanisms through which lncRNAs function as master regulators of the human genome. Specifically, the objective of our review is to examine how lncRNAs regulate different processes like cell division, cell cycle, and immune responses, and unravel their roles in maintaining genomic architecture and integrity.

Investigating the role of vitamin D in asthma.

Results in mice suggest that vitamin D reduces the symptoms of asthma by controlling an immune response that leads to inflammation of the airways.

Assessment of Tunable Resistive Pulse Sensing (TRPS) Technology for Particle Size Distribution in Vaccine Formulations - A Comparative Study with Dynamic Light Scattering.

PURPOSE: A comparative assessment was performed to evaluate the potential of particle sizing by an ensemble based conventional dynamic light scattering (DLS) technique and an emerging technology based on tunable resistive pulse sensing (TRPS) using particle by particle approach by evaluating three different types of vaccine formulations representing three case studies and showing the limitation of each technique, instrument variability, sensitivity, and the resolution in mixed population. METHODS: Three types of in-house vaccine formulations- a protein antigen, an outer membrane vesicle and viral particles were simultaneously evaluated by TRPS based Exoid and two DLS instruments-Zetatrac and Zetasizer for particle size distribution, aggregates, and resolution of polydisperse species. RESULTS: The data from first case study show the risk of possible size overestimation and size averaging in polydisperse samples in DLS measurements which can be addressed by the TRPS analysis. It also shows how TRPS may be utilized only to large size antigens due to its limited size range. The second case study highlights the difference in the sensitivities of two DLS instruments working on the same principle. The third case study show that how TRPS can better resolve the large aggregate species compare to DLS in polydisperse samples. CONCLUSION: This analysis shows that TRPS can be used as an orthogonal technique in addition to conventional DLS based methods for more precise and in-depth characterization. Both techniques are efficient in size characterization and produce comparable results, however the choice will depend on the type of formulation and size range to be evaluated.

DeePNAP: A Deep Learning Method to Predict Protein-Nucleic Acid Binding Affinity from Their Sequences.

Predicting the protein-nucleic acid (PNA) binding affinity solely from their sequences is of paramount importance for the experimental design and analysis of PNA interactions (PNAIs). A large number of currently developed models for binding affinity prediction are limited to specific PNAIs while also relying on the sequence and structural information of the PNA complexes for both training and testing, and also as inputs. As the PNA complex structures available are scarce, this significantly limits the diversity and generalizability due to the small training data set. Additionally, a majority of the tools predict a single parameter, such as binding affinity or free energy changes upon mutations, rendering a model less versatile for usage. Hence, we propose DeePNAP, a machine learning-based model built from a vast and heterogeneous data set with 14,401 entries (from both eukaryotes and prokaryotes) from the ProNAB database, consisting of wild-type and mutant PNA complex binding parameters. Our model precisely predicts the binding affinity and free energy changes due to the mutation(s) of PNAIs exclusively from their sequences. While other similar tools extract features from both sequence and structure information, DeePNAP employs sequence-based features to yield high correlation coefficients between the predicted and experimental values with low root mean squared errors for PNA complexes in predicting KD and ΔΔG, implying the generalizability of DeePNAP. Additionally, we have also developed a web interface hosting DeePNAP that can serve as a powerful tool to rapidly predict binding affinities for a myriad of PNAIs with high precision toward developing a deeper understanding of their implications in various biological systems. Web interface: http://14.139.174.41:8080/.

TetR and OmpR family regulators in natural product biosynthesis and resistance.

This article provides a comprehensive review and sequence-structure analysis of transcription regulator (TR) families, TetR and OmpR/PhoB, involved in specialized secondary metabolite (SSM) biosynthesis and resistance. Transcription regulation is a fundamental process, playing a crucial role in orchestrating gene expression to confer a survival advantage in response to frequent environmental stress conditions. This process, coupled with signal sensing, enables bacteria to respond to a diverse range of intra and extracellular signals. Thus, major bacterial signaling systems use a receptor domain to sense chemical stimuli along with an output domain responsible for transcription regulation through DNA-binding. Sensory and output domains on a single polypeptide chain (one component system, OCS) allow response to stimuli by allostery, that is, DNA-binding affinity modulation upon signal presence/absence. On the other hand, two component systems (TCSs) allow cross-talk between the sensory and output domains as they are disjoint and transmit information by phosphorelay to mount a response. In both cases, however, TRs play a central role. Biosynthesis of SSMs, which includes antibiotics, is heavily regulated by TRs as it diverts the cell's resources towards the production of these expendable compounds, which also have clinical applications. These TRs have evolved to relay information across specific signals and target genes, thus providing a rich source of unique mechanisms to explore towards addressing the rapid escalation in antimicrobial resistance (AMR). Here, we focus on the TetR and OmpR family TRs, which belong to OCS and TCS, respectively. These TR families are well-known examples of regulators in secondary metabolism and are ubiquitous across different bacteria, as they also participate in a myriad of cellular processes apart from SSM biosynthesis and resistance. As a result, these families exhibit higher sequence divergence, which is also evident from our bioinformatic analysis of 158 389 and 77 437 sequences from TetR and OmpR family TRs, respectively. The analysis of both sequence and structure allowed us to identify novel motifs in addition to the known motifs responsible for TR function and its structural integrity. Understanding the diverse mechanisms employed by these TRs is essential for unraveling the biosynthesis of SSMs. This can also help exploit their regulatory role in biosynthesis for significant pharmaceutical, agricultural, and industrial applications.

Conserved functions of prion candidates suggest a primeval role of protein self-templating.

Amyloid-based prions have simple structures, a wide phylogenetic distribution, and a plethora of functions in contemporary organisms, suggesting they may be an ancient phenomenon. However, this hypothesis has yet to be addressed with a systematic, computational, and experimental approach. Here we present a framework to help guide future experimental verification of candidate prions with conserved functions to understand their role in the early stages of evolution and potentially in the origins of life. We identified candidate prions in all high-quality proteomes available in UniProt computationally, assessed their phylogenomic distributions, and analyzed candidate-prion functional annotations. Of the 27 980 560 proteins scanned, 228 561 were identified as candidate prions (~0.82%). Among these candidates, there were 84 Gene Ontology (GO) terms conserved across the three domains of life. We found that candidate prions with a possible role in adaptation were particularly well-represented within this group. We discuss unifying features of candidate prions to elucidate the primeval roles of prions and their associated functions. Candidate prions annotated as transcription factors, DNA binding, and kinases are particularly well suited to generating diverse responses to changes in their environment and could allow for adaptation and population expansion into more diverse environments. We hypothesized that a relationship between these functions and candidate prions could be evolutionarily ancient, even if individual prion domains themselves are not evolutionarily conserved. Candidate prions annotated with these universally occurring functions potentially represent the oldest extant prions on Earth and are therefore excellent experimental targets.

An open source computational workflow for the discovery of autocatalytic networks in abiotic reactions.

A central question in origins of life research is how non-entailed chemical processes, which simply dissipate chemical energy because they can do so due to immediate reaction kinetics and thermodynamics, enabled the origin of highly-entailed ones, in which concatenated kinetically and thermodynamically favorable processes enhanced some processes over others. Some degree of molecular complexity likely had to be supplied by environmental processes to produce entailed self-replicating processes. The origin of entailment, therefore, must connect to fundamental chemistry that builds molecular complexity. We present here an open-source chemoinformatic workflow to model abiological chemistry to discover such entailment. This pipeline automates generation of chemical reaction networks and their analysis to discover novel compounds and autocatalytic processes. We demonstrate this pipeline's capabilities against a well-studied model system by vetting it against experimental data. This workflow can enable rapid identification of products of complex chemistries and their underlying synthetic relationships to help identify autocatalysis, and potentially self-organization, in such systems. The algorithms used in this study are open-source and reconfigurable by other user-developed workflows.

Automated Exploration of Prebiotic Chemical Reaction Space: Progress and Perspectives.

Prebiotic chemistry often involves the study of complex systems of chemical reactions that form large networks with a large number of diverse species. Such complex systems may have given rise to emergent phenomena that ultimately led to the origin of life on Earth. The environmental conditions and processes involved in this emergence may not be fully recapitulable, making it difficult for experimentalists to study prebiotic systems in laboratory simulations. Computational chemistry offers efficient ways to study such chemical systems and identify the ones most likely to display complex properties associated with life. Here, we review tools and techniques for modelling prebiotic chemical reaction networks and outline possible ways to identify self-replicating features that are central to many origin-of-life models.