Unique Pattern Research Articles

Transcriptome analysis of the harmful alga Heterosigma akashiwo under a 24-hour light-dark cycle

The photoperiod, which is defined as the period of time within a 24-hour time frame that light is available, is an important environmental regulator of several physiological processes in phytoplankton, including harmful bloom-forming phytoplankton. The ichthyotoxic raphidophyte Heterosigma akashiwo is a globally distributed bloom-forming phytoplankton. Despite extensive studies on the ecological impact of H. akashiwo, the influence of the photoperiod on crucial biological processes of this species remains unclear. In this study, gene expression in H. akashiwo was analyzed over a 24-hour light-dark (14:10) treatment period. Approximately 36 % of unigenes in H. akashiwo were differentially expressed during this 24-hour treatment period, which is indicative of their involvement in the response to light-dark variation. Notably, the number of differentially expressed genes exhibited an initial increase followed by a subsequent decrease as the sampling time progressed (T0 vs. other time points). Unigenes associated with photosynthesis and photoprotection reached their peak expression levels after 2–4 h of illumination (T12-T14). In contrast, the expression of unigenes associated with DNA replication peaked at the starting point of the dark period (T0). Furthermore, although several unigenes annotated to photoreceptors displayed potential diel periodicity, genes from various photoreceptor families (such as phytochrome and cryptochrome) showed unique expression patterns. Collectively, our findings offer novel perspectives on the response of H. akashiwo to the light-dark cycle, serving as a valuable resource for investigating the physiology and ecology of this species.

Expression of FOXI1 and POU2F3 varies among different salivary gland neoplasms and is higher in Warthin tumor

PurposeSalivary gland tumors are histologically diverse. Ionocytes and tuft cells, rare epithelial cells found in normal salivary glands, might be associated with salivary tumors. Here, we explored the expression of FOXI1 and POU2F3, master regulators of ionocytes and tuft cells, respectively, for common salivary neoplasms using immunohistochemistry.MethodsWe analyzed normal salivary tissues and nine salivary gland tumors; Warthin tumors (WT), pleomorphic adenomas (PA), basal cell adenomas, and oncocytomas were benign, whereas mucoepidermoid, adenoid cystic, acinic cell, salivary duct carcinomas, and polymorphous adenocarcinomas were malignant.ResultsNormal salivary glands contained a few FOXI1- and POU2F3-positive cells in the ducts instead of the acini, consistent with ionocytes and tuft cells, respectively. Among the benign tumors, only WTs and PAs consistently expressed FOXI1 (10/10 and 9/10, respectively). The median H-score of WTs was significantly higher than that of PAs (17.5 vs. 4, P = 0.01). While WTs and PAs harbored POU2F3-positive cells (10/10 and 9/10, respectively), the median H-score was higher in WTs than in PAs (10.5 vs 4, respectively). Furthermore, WTs exhibited a unique staining pattern of FOXI1- and POU2F3-positive cells, which were present in luminal and abluminal locations, respectively. Whereas none of the malignant tumors expressed FOXI1, only adenoid cystic carcinoma consistently expressed POU2F3 (5/5), with a median H-score of 4.ConclusionThe expression patterns of the characteristic transcription factors found in ionocytes and tuft cells vary among salivary gland tumor types and are higher in WT, which might be relevant for understanding and diagnosing salivary gland neoplasms.

Opioid use disorder (OUD) and treatment for opioid problems among OUD symptom subtypes in individuals misusing opioids

BackgroundIn 2021, approximately 60 million individuals worldwide and 9 million individuals in the United States (US) reported opioid misuse. In the US, 2.5 million have OUD, of which only about a third receive any substance abuse treatment. OUD is often regarded as a monolithic disorder but different opioid problem subtypes may exist beyond DSM-IV/5 criteria. Understanding the characteristics of these subtypes could be useful for informing treatment and intervention strategies. MethodsLatent class analysis was used to identify OUD symptom subtypes among persons in the US who reported misusing prescription opioids or heroin in the 2015–2018 National Survey on Drug Use and Health (n=10,928). Regression analyses were utilized to determine associations between class membership and treatment receipt, as well as demographic characteristics and other comorbid conditions. ResultsFive classes were identified with unique OUD symptom patterns: Class 1: Asymptomatic (71.6%), Class 2: Tolerance/Time (14.5%), Class 3: Loss of Control/Pharmacological (LOC/Pharmacol) (5.7%), Class 4: Social Impairment (2.6%), and Class 5: Pervasive (5.6%). Nearly all persons in the LOC/Pharmacol, Social Impairment, and Pervasive classes met criteria for OUD (98–100%); however, they differed in receipt of past-year treatment for substance use (28%, 28%, 49%, respectively). Age, race, education, insurance status, and criminal activity were also associated with treatment receipt. ConclusionsThere were considerable differences in OUD symptom patterns and substance use treatment among individuals who misused opioids. The findings indicate a substantial unmet need for OUD treatment and point to patterns of heterogeneity within OUD that can inform development of treatment programs.

Aptamer-Based Multiparameter Analysis for Molecular Profiling of Hematological Malignancies.

The subtypes of hematological malignancies (HM) with minimal molecular profile differences display an extremely heterogeneous clinical course and a discrepant response to certain treatment regimens. Profiling the surface protein markers offers a potent solution for precision diagnosis of HM by differentiating among the subtypes of cancer cells. Herein, we report the use of Cell-SELEX technology to generate a panel of high-affinity aptamer probes that are able to discriminate subtle differences among surface protein profiles between different HM cells. Experimental results show that these aptamers with apparent dissociation constants (Kd) below 10 nM display a unique recognition pattern on different HM subtypes. By combining a machine learning model on the basis of partial least-squares discriminant analysis, 100% accuracy was achieved for the classification of different HM cells. Furthermore, we preliminarily validated the effectiveness of the aptamer-based multiparameter analysis strategy from a clinical perspective by accurately classifying complex clinical samples, thus providing a promising molecular tool for precise HM phenotyping.

Investigation of exosomal tetraspanin profile in sepsis patients as a promising diagnostic biomarker

Introduction Sepsis, a leading cause of mortality globally, has a complex and multifaceted pathophysiology which still requires elucidation. Therefore, this study aimed to analyze and quantify the number of exosomes in sepsis patients from a South African cohort using the ExoView (NanoView Biosciences, Boston, MA) platform. Methods Blood samples were collected from black South African patients attending the local Intensive Care Unit (ICU) hospital. Exosomes were isolated and characterize via TEM and CD63 ELISA kits. ExoView was used to determine particle count, particle size distribution and colocalization of different tetraspanin markers. Results Exosomal levels in sepsis patients were significantly higher compared to the control group (p < 0.05). Sepsis exosomes showed a homogenous size distribution ranging from 55 to 70 nm. Tetraspanin colocalization analysis revealed that sepsis exosomes have significantly higher CD63/CD9, CD63/CD81 and CD63/CD9/CD81 colocalization percentages than the control group. Conclusion This unique tetraspanin colocalization pattern of sepsis exosomes could serve as a potential sepsis biomarker. Further investigations are required to identify sepsis exosomal cargo signatures for further understanding of sepsis pathophysiology in order to develop effective diagnostics and treatments.

Codon Pattern and Context Analysis in Genes Triggering Alzheimer's Disease and Latent Tau Protein Aggregation Post-Anesthesia Exhibited Unique Molecular Patterns Associated with Functional Aspects.

Previous reports have demonstrated post-operative dementia and Alzheimer's disease (AD), and increased amyloid-β levels and tau hyperphosphorylation have been observed in animal models post-anesthesia. After surgical interventions, loss in memory has been observed that has been found linked with genes modulated after anesthesia. Present study aimed to study molecular pattern present in genes modulated post anesthesia and involved in characters progressing towards AD. In the present study, 17 transcript variants belonging to eight genes, which have been found to modulate post-anesthesia and contribute to AD progression, were envisaged for their compositional features, molecular patterns, and codon and codon context-associated studies. The sequences' composition was G/C rich, influencing dinucleotide preference, codon preference, codon usage, and codon context. The G/C nucleotides being highly occurring nucleotides, CpGdinucleotides were also preferred; however, CpG was highly disfavored at p3-1 at the codon junction. The nucleotide composition of Cytosine exhibited a unique feature, and unlike other nucleotides, it did not correlate with codon bias. Contrarily, it correlated with the sequence lengths. The sequences were leucine-rich, and multiple leucine repeats were present, exhibiting the functional role of neuroprotection from neuroinflammation post-anesthesia. The analysis pave the way to elucidate unique molecular patterns in genes modulated during anesthetic treatment and might help ameliorate the ill effects of anesthetics in the future.

Recognition of sounds by ensembles of proteinoids

Proteinoids are artificial polymers that imitate certain characteristics of natural proteins, including self–organization, catalytic activity, and responsiveness to external stimuli. This paper examines the acoustic response properties of proteinoids microspheres when exposed to auditory stimuli. We convert sounds of English alphabet into waveforms of electrical potential, feed the waveforms into proteinoid solutions and record electrical responses of the proteinoids. We also undertake a detailed comparison of proteinoids’ electrical responses (frequencies, periods, and amplitudes) with original input signals. We found that responses of proteinoids are less regular, lower dominant frequency, wider distribution of proteinoids and less skewed distribution of amplitudes compared with input signals. We found that resonant acoustic excitation of proteinoids generates unique electrical impulse patterns dependent on sound frequency and amplitude. The finding will be used in further designs of organic electronic devices, based on ensembles of proteinoids, for sound processing and speech recognition. Our findings provide the first quantitative investigation into the potential of thermal proteinoid microspheres for bio–inspired sound processing and recognition applications. Using controlled speaker excitation on proteinoid samples, we create reliable markers of productive acoustic response capacities, paving the way for future advancement.

A Deep Learning-Based Embedded System for Pest Bird Sound Detection and Proximity Estimation

Cultivating crops is vital for driving economies, and maintaining agricultural fields is crucial for sustaining food production. This initiative centers on addressing the issue of pest birds, specifically starlings, within vineyards. The proposed strategy employs sound signals to detect and distinguish starling birds within the vineyard environment. Through an analysis of audio inputs from the surroundings, the system can effectively recognize unique sound patterns associated with starling birds, utilizing deep learning techniques. Furthermore, this project incorporates ultrasonic sensors for distance estimation, enabling the calculation of the bird’s proximity from a fixed point within the vineyard. All of these detection and estimation processes are executed on a RP2040 microcontroller, specifically the Cortex-M0+ 133 MHz variant. Following the detection phase, an autonomous vehicle equipped with red diode lasers can be dispatched to the designated location to deter the pest birds and safeguard the vineyards from unwanted disruptions and crop losses.

Phylogenomics and plastomics offer new evolutionary perspectives on Kalanchoideae (Crassulaceae).

Kalanchoideae is one of three subfamilies within Crassulaceae and contains four genera. Despite previous efforts, the phylogeny of Kalanchoideae remains inadequately resolved with persistent issues including low support, unstructured topologies and polytomies. This study aimed to address two central objectives: (1) resolving the pending phylogenetic questions within Kalanchoideae by using organelle-scale 'barcodes' (plastomes) and nuclear data; and (2) investigating interspecific diversity patterns among Kalanchoideae plastomes. To explore the plastome evolution in Kalanchoideae, we newly sequenced 38 plastomes representing all four constituent genera (Adromischus, Cotyledon, Kalanchoe and Tylecodon). We performed comparative analyses of plastomic features, including GC and gene contents, gene distributions at the IR (inverted repeat) boundaries, nucleotide divergence, plastomic tRNA (pttRNA) structures and codon aversions. Additionally, phylogenetic inferences were inferred using both the plastomic dataset (79 genes) and nuclear dataset (1054 genes). Significant heterogeneities were observed in plastome lengths among Kalanchoideae, strongly correlated with LSC (large single copy) lengths. Informative diversities existed in the gene content at SSC/IRa (small single copy/inverted repeat a), with unique patterns individually identified in Adromischus leucophyllus and one major Kalanchoe clade. The ycf1 gene was assessed as a shared hypervariable region among all four genera, containing nine lineage-specific indels. Three pttRNAs exhibited unique structures specific to Kalanchoideae and the genera Adromischus and Kalanchoe. Moreover, 24 coding sequences revealed a total of 41 lineage-specific unused codons across all four constituent genera. The phyloplastomic inferences clearly depicted internal branching patterns in Kalanchoideae. Most notably, by both plastid- and nuclear-based phylogenies, our research offers the first evidence that Kalanchoe section Eukalanchoe is not monophyletic. This study conducted comprehensive analyses on 38 newly reported Kalanchoideae plastomes. Importantly, our results not only reconstructed well-resolved phylogenies within Kalanchoideae, but also identified highly informative unique markers at the subfamily, genus and species levels. These findings significantly enhance our understanding of the evolutionary history of Kalanchoideae.

Reconstructing the transcriptional regulatory network of probiotic L. reuteri is enabled by transcriptomics and machine learning.

We have studied Limosilactobacillus reuteri, a beneficial probiotic microbe that plays a significant role in our health and production of sustainable foods, a type of foods that are nutritionally dense and healthier and have low-carbon emissions compared to traditional foods. Similar to how humans adapt their lifestyles to different environments, this microbe adjusts its behavior by modulating the expression of genes. We applied machine learning to analyze large-scale data sets on how these genes behave across diverse conditions. From this, we identified 35 unique patterns demonstrating how L. reuteri adjusts its genes based on 50 unique environmental conditions (such as various sugars, salts, microbial cocultures, human milk, and fruit juice). This research helps us understand better how L. reuteri functions, especially in processes like breaking down certain nutrients and adapting to stressful changes. More importantly, with our findings, we become closer to using this knowledge to improve how we produce more sustainable and healthier foods with the help of microbes.

Exploring gender disparities in the disease and economic tobacco-attributable burden in Latin America.

Tobacco use has significant health consequences in Latin America, and while studies have examined the overall impact, the gender-specific effects have not been thoroughly researched. Understanding these differences is crucial for effective tobacco control policies. The objective of this study was to explore the differences in tobacco-attributable disease and economic burden between men and women in Argentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, Mexico, and Peru. We used a previously validated economic model to quantify the impact of tobacco-related illnesses, including morbidity, mortality, healthcare costs, productivity losses, informal care expenses, and DALYs, by gender and age. We utilized data from national surveys, records, studies, and expert opinions to populate the model. In 2020, there were 351,000 smoking-attributable deaths. Men accounted for 69% and women 31%. Ecuador and Mexico had the highest male-to-female death ratio, while Peru and Chile had the smallest disparities. 2.3 million tobacco-related disease events occurred, with 65% in men and 35% in women. Ecuador and Mexico had higher disease rates among men, while Peru had a more balanced ratio. Regarding DALYs, men lost 6.3 million due to tobacco, while women lost 3.3 million, primarily from COPD, cardiovascular disease, and cancer. Brazil and Mexico had the highest DALY losses for both genders. Costa Rica had a lower male-to-female tobacco use prevalence ratio but ranked second in deaths, disease events, and DALYs attributed to tobacco. Colombia had a unique pattern with a male-to-female death ratio of 2.08 but a higher ratio for disease events. The health systems spent $22.8 billion to treat tobacco-attributable diseases, with a male-to-female cost ratio 2.15. Ecuador showed the greatest gender cost difference, while Peru had the lowest. Productivity loss due to tobacco was $16.2 billion, with Ecuador and Mexico exhibiting the highest gender disparities and Peru the lowest. Informal care costs amounted to $10.8 billion, with men incurring higher costs in Ecuador, Costa Rica, and Mexico. Tobacco causes significant health and economic burdens in Latin America, with gender-based differences. There is a need for gender-disaggregated data to improve tobacco control policies.

Quantitative measurement of geodiversity uniqueness: research implications and conservation applications

Quantitative approaches are needed to complement qualitative explorations to identify sites with unique geodiversity and thereby guide geoconservation and geoheritage programmes. Here, we introduce the concept and associated index of ‘geodiversity uniqueness’. This index is based on a numerical analysis of geofeatures and allows the identification of sites with unique geodiversity in a study area. We applied this approach to geofeature data from three areas in Finland. Our results showed that patterns of geodiversity uniqueness varied profoundly among the three study areas and across sites within each area. This was due to different sets of geofeatures and distinct characteristics of each study area. More importantly, the approach presented here was robust across the datasets and selection criteria for sets of sites, showing potential for geoconservation in each study area. The geodiversity uniqueness approach is a promising starting point to identify and map sites with unique geodiversity that can be further verified using field observations. To improve our knowledge of geodiversity variation, complementary approaches providing objective information on contributions to total beta geodiversity are needed to advance geoconservation programmes across areas and different spatial scales. This article is part of the Theo Murphy meeting issue ‘Geodiversity for science and society’.

Kidney-specific methylation patterns correlate with kidney function and are lost upon kidney disease progression

BackgroundChronological and biological age correlate with DNA methylation levels at specific sites in the genome. Linear combinations of multiple methylation sites, termed epigenetic clocks, can inform us the chronological age and predict multiple health-related outcomes. However, why some sites correlating with lifespan, healthspan, or specific medical conditions remain poorly understood. Kidney fibrosis is the common pathway for chronic kidney disease, which affects 10% of European and US populations.ResultsHere we identify epigenetic clocks and methylation sites that correlate with kidney function. Moreover, we identify methylation sites that have a unique methylation signature in the kidney. Methylation levels in majority of these sites correlate with kidney state and function. When kidney function deteriorates, all of these sites regress toward the common methylation pattern observed in other tissues. Interestingly, while the majority of sites are less methylated in the kidney and become more methylated with loss of function, a fraction of the sites are highly methylated in the kidney and become less methylated when kidney function declines. These methylation sites are enriched for specific transcription-factor binding sites. In a large subset of sites, changes in methylation patterns are accompanied by changes in gene expression in kidneys of chronic kidney disease patients.ConclusionsThese results support the information theory of aging, and the hypothesis that the unique tissue identity, as captured by methylation patterns, is lost as tissue function declines. However, this information loss is not random, but guided toward a baseline that is dependent on the genomic loci.Significance statementDNA methylation at specific sites accurately reflects chronological and biological age. We identify sites that have a unique methylation pattern in the kidney. Methylation levels in the majority of these sites correlate with kidney state and function. Moreover, when kidney function deteriorates, all of these sites regress toward the common methylation pattern observed in other tissues. Thus, the unique methylation signature of the kidney is degraded, and epigenetic information is lost, when kidney disease progresses. These methylation sites are enriched for specific and methylation-sensitive transcription-factor binding sites, and associated genes show disease-dependent changes in expression. These results support the information theory of aging, and the hypothesis that the unique tissue identity, as captured by methylation patterns, is lost as tissue function declines.

Hybrid aliphatic and aromatic diisocyanates forming mixed urea segments for high-performance polyurea

Polyurea is gaining more traction in the realm of protective coatings on metal surfaces from corrosion. The properties of polyurea can be controlled by the judicious selection of its reactants. Herein, a series of polyurea based on hybrid aliphatic/aromatic diisocyanates are synthesized from isophorone diisocyanate (IP) and polycarbodiimide-modified diphenylmethane diisocyanate (ISO). The formulation composed of 37.5:62.5 (IP: ISO) weight ratio yields the highest tensile strength at 47.80 MPa, elongation at 500 %, and hardness of 95 HA. The findings are validated through field emission scanning and atomic force microscopic analysis, which reveals phase mixing surface and ductile fracture containing wide-ranging stretching lines due to enhanced resistance to deformation. Furthermore, synthesized polyureas present a unique stress relaxation pattern with gradual increase in stress under constant deformation, due to hydrogen bond reformation and dislocation creations during chain alignment. IP37.5-ISO62.5 demonstrates outstanding corrosion resistance after a 28-day exposure in salt spray test.

Analysis of Social Interaction of Deaf Children in Special Schools

&#x0D; &#x0D; &#x0D; &#x0D; Deaf is a term used to describe the symptoms and symptoms of someone who is hearing impaired or deaf. Deaf children interact through a variety of methods, including sign language, facial expressions, and body, as well as the use of hearing aid technology. They can also utilize visual communication approaches, peer support, and participation in group activities to strengthen social skills. Social interaction is a dynamic in which individuals or groups communicate, act, or influence each other in a particular environment or situation. Social interaction involves the exchange of information, emotions, ideas, or behaviors between individuals or groups. This research makes a valuable contribution to improving our understanding of the benefits of social interaction for deaf children in Special Schools, opening up opportunities for the development of more inclusive educational strategies and supporting their holistic development. This study aimed to analyze social interactions between deaf children. An observational approach and interview, this study explores the dynamics of communication, information exchange, and emotional understanding in interactions between both groups of children. Qualitative analysis methods are used to identify unique communication patterns and understand the challenges faced by deaf children in communicating. The subjects in this study were interviewing homeroom teachers and counseling teachers at the school, the number of subjects to be studied were children with deafness. The results highlight adaptation efforts and communication strategies developed by deaf children to interact. The results of these findings can provide valuable insights for educators, parents, and health practitioners in supporting the development of social skills of deaf children so that they can engage more effectively in society.&#x0D; &#x0D; &#x0D; &#x0D;

Innovative Neuroimaging Biomarker Distinction of Major Depressive Disorder and Bipolar Disorder through Structural Connectome Analysis and Machine Learning Models.

Major depressive disorder (MDD) and bipolar disorder (BD) share clinical features, which complicates their differentiation in clinical settings. This study proposes an innovative approach that integrates structural connectome analysis with machine learning models to discern individuals with MDD from individuals with BD. High-resolution MRI images were obtained from individuals diagnosed with MDD or BD and from HCs. Structural connectomes were constructed to represent the complex interplay of brain regions using advanced graph theory techniques. Machine learning models were employed to discern unique connectivity patterns associated with MDD and BD. At the global level, both BD and MDD patients exhibited increased small-worldness compared to the HC group. At the nodal level, patients with BD and MDD showed common differences in nodal parameters primarily in the right amygdala and the right parahippocampal gyrus when compared with HCs. Distinctive differences were found mainly in prefrontal regions for BD, whereas MDD was characterized by abnormalities in the left thalamus and default mode network. Additionally, the BD group demonstrated altered nodal parameters predominantly in the fronto-limbic network when compared with the MDD group. Moreover, the application of machine learning models utilizing structural brain parameters demonstrated an impressive 90.3% accuracy in distinguishing individuals with BD from individuals with MDD. These findings demonstrate that combined structural connectome and machine learning enhance diagnostic accuracy and may contribute valuable insights to the understanding of the distinctive neurobiological signatures of these psychiatric disorders.

Auditory event-related potential differentiates girls with Rett syndrome from their typically-developing peers with high accuracy: Machine learning study

Rett Syndrome (RTT) is a rare neurodevelopmental disorder caused by mutation in the MECP2 gene. No cures are still available, but several clinical trials are ongoing. Here we examine neurophysiological correlates of auditory processing for ability to differentiate patients with RTT from typically developing (TD) peers applying standard machine learning (ML) methods and pipelines. Capitalized on the available event-related potential (ERP) data recorded in response to tone presented at different rates (stimulus onset asynchrony 900, 1800 and 3600 ms) from 24 patients with RTT and 27 their TD peer. We considered the most common ML models that are widely used for classification tasks. These include both linear models (logistic regression, support-vector machine with linear kernel) and tree-based nonlinear models (random forest, gradient boosting). Based on these methods we were able to differentiate RTT from TD children with high accuracy (with up to 0.94 ROC-AUC score), which was evidently higher at the fastest presentation rate. Importance analysis and perturbation importance pointed out that the most important feature for classification is P2-N2 peak-to-peak amplitude, consistently across the approaches and blocks with different presentation rate. The results suggest the unique pattern of ERP characteristics for RTT and points to features of importance. The results might be relevant for establishing outcome measures for clinical trials.

Sex-specific nociceptor modulation of the apical periodontitis transcriptome.

Introduction: Apical periodontitis (AP) is a painful disease that develops quickly following dental infections and is primarily characterized by robust inflammation surrounding the tissues of the affected tooth, resulting in disruption of bone homeostasis and periradicular bone loss. Moreover, there are distinct clinical presentations, symptoms, and responses to AP treatment between male and female subjects, creating a desperate need to further understand the sex-specific mechanisms of AP. Methods: With the growing evidence that nociceptors modulate AP development, we utilized RNA sequencing in nociceptor-ablated (Nav1.8 cre+/-, diphtheria toxin Alox+/-) transgenic mice to study the nociceptor regulation of the periapical lesion transcriptome using a rodent model of AP in female mice over 14days. Results: Overall, we found that female mice exhibit unique patterns of differentially expressed genes throughout AP infection compared to male mice and that the expression of these genes is regulated by nociceptors. Additionally, nociceptor ablation results in a more significant enrichment of biological processes related to immune responses earlier compared to cre-control (Nav1.8 cre+/-) females and greater expression of genes involved in inflammatory processes and osteolytic activity. Discussion: Therefore, while nociceptor ablation augments inflammatory and bone resorption responses in both males and females in a mouse model of AP, transcriptomic analyses demonstrate that the mechanisms through which nociceptors modulate AP are distinct between sexes. These studies will provide the foundation needed to study further mechanisms of sex differences in AP, an area with a desperate need for investigation to treat current AP patients. Understanding these mechanisms can ultimately inform treatment options to alleviate suffering for millions of patients suffering from AP.

Catalytic divergencies in the mechanism of L-arginine hydroxylating nonheme iron enzymes.

Many enzymes in nature utilize a free arginine (L-Arg) amino acid to initiate the biosynthesis of natural products. Examples include nitric oxide synthases, which generate NO from L-Arg for blood pressure control, and various arginine hydroxylases involved in antibiotic biosynthesis. Among the groups of arginine hydroxylases, several enzymes utilize a nonheme iron(II) active site and let L-Arg react with dioxygen and α-ketoglutarate to perform either C3-hydroxylation, C4-hydroxylation, C5-hydroxylation, or C4-C5-desaturation. How these seemingly similar enzymes can react with high specificity and selectivity to form different products remains unknown. Over the past few years, our groups have investigated the mechanisms of L-Arg-activating nonheme iron dioxygenases, including the viomycin biosynthesis enzyme VioC, the naphthyridinomycin biosynthesis enzyme NapI, and the streptothricin biosynthesis enzyme OrfP, using computational approaches and applied molecular dynamics, quantum mechanics on cluster models, and quantum mechanics/molecular mechanics (QM/MM) approaches. These studies not only highlight the differences in substrate and oxidant binding and positioning but also emphasize on electronic and electrostatic differences in the substrate-binding pockets of the enzymes. In particular, due to charge differences in the active site structures, there are changes in the local electric field and electric dipole moment orientations that either strengthen or weaken specific substrate C-H bonds. The local field effects, therefore, influence and guide reaction selectivity and specificity and give the enzymes their unique reactivity patterns. Computational work using either QM/MM or density functional theory (DFT) on cluster models can provide valuable insights into catalytic reaction mechanisms and produce accurate and reliable data that can be used to engineer proteins and synthetic catalysts to perform novel reaction pathways.

Unveiling Polysomal Long Non-Coding RNA Expression on the First Day of Adipogenesis and Osteogenesis in Human Adipose-Derived Stem Cells.

Understanding the intricate molecular mechanisms governing the fate of human adipose-derived stem cells (hASCs) is essential for elucidating the delicate balance between adipogenic and osteogenic differentiation in both healthy and pathological conditions. Long non-coding RNAs (lncRNAs) have emerged as key regulators involved in lineage commitment and differentiation of stem cells, operating at various levels of gene regulation, including transcriptional, post-transcriptional, and post-translational processes. To gain deeper insights into the role of lncRNAs' in hASCs' differentiation, we conducted a comprehensive analysis of the lncRNA transcriptome (RNA-seq) and translatome (polysomal-RNA-seq) during a 24 h period of adipogenesis and osteogenesis. Our findings revealed distinct expression patterns between the transcriptome and translatome during both differentiation processes, highlighting 90 lncRNAs that are exclusively regulated in the polysomal fraction. These findings underscore the significance of investigating lncRNAs associated with ribosomes, considering their unique expression patterns and potential mechanisms of action, such as translational regulation and potential coding capacity for microproteins. Additionally, we identified specific lncRNA gene expression programs associated with adipogenesis and osteogenesis during the early stages of cell differentiation. By shedding light on the expression and potential functions of these polysome-associated lncRNAs, we aim to deepen our understanding of their involvement in the regulation of adipogenic and osteogenic differentiation, ultimately paving the way for novel therapeutic strategies and insights into regenerative medicine.