Biological Hypotheses Research Articles

Release from aboveground enemies increases seedling survival in grasslands

Abstract Plant enemies can influence plant community assembly and structure. However, it is unclear how insect herbivores and fungal pathogens affect seedling recruitment. Complex interactions with competition and resource availability make it difficult to isolate the effect of enemies. This uncertainty can impede understanding of community assembly drivers, species coexistence and trophic interactions; and limits hypothesis testing such as of the enemy release hypothesis, a key hypothesis in invasion biology. Using a novel species‐specific approach, we examine how enemies affect seedling survival and recruitment of 16 grassland species. We planted seedlings of 16 native species from two functional groups (C4 grasses and non‐legume forbs) into two grassland sites (early and mid‐succession). We hand‐painted 1512 individual seedlings with pesticides (insecticide and fungicide) over the course of one growing season to enforce aboveground species‐specific release from enemies and tested whether it enhanced survival relative to untreated controls. Applying treatments to individuals allowed us to test for the effect of enemies on plant performance while controlling for contexts such as competition from the resident community and resource levels. Release from insects increased seedling survival by 56% on average, with no additional benefit of release from fungal pathogens. This effect was observed for both forbs and C4 grasses across both sites and was strongest in resource‐acquisitive species. There was no effect of the mean phylogenetic distance between our target species and the resident plant community, or of light availability or soil moisture. Synthesis. The significant positive effect of release from insect herbivores on survival early in colonisation—a trend that held across functional groups and types of resident community—suggests insects play an important regulatory role in community assembly, especially for resource‐acquisitive species. As variation between species could be explained by traits but not by phylogeny, we emphasise the importance of trait‐based approaches to plant community ecology. Our results also support the key mechanism (increased performance following release from enemies) underlying the enemy release hypothesis. Enemy release may therefore aid initial recruitment of plants during the invasion process.

Sparse Firing in a Hybrid Central Pattern Generator for Spinal Motor Circuits.

Central pattern generators are circuits generating rhythmic movements, such as walking. The majority of existing computational models of these circuits produce antagonistic output where all neurons within a population spike with a broad burst at about the same neuronal phase with respect to network output. However, experimental recordings reveal that many neurons within these circuits fire sparsely, sometimes as rarely as once within a cycle. Here we address the sparse neuronal firing and develop a model to replicate the behavior of individual neurons within rhythm-generating populations to increase biological plausibility and facilitate new insights into the underlying mechanisms of rhythm generation. The developed network architecture is able to produce sparse firing of individual neurons, creating a novel implementation for exploring the contribution of network architecture on rhythmic output. Furthermore, the introduction of sparse firing of individual neurons within the rhythm-generating circuits is one of the factors that allows for a broad neuronal phase representation of firing at the population level. This moves the model toward recent experimental findings of evenly distributed neuronal firing across phases among individual spinal neurons. The network is tested by methodically iterating select parameters to gain an understanding of how connectivity and the interplay of excitation and inhibition influence the output. This knowledge can be applied in future studies to implement a biologically plausible rhythm-generating circuit for testing biological hypotheses.

A stochastic model of ant trail formation and maintenance in static and dynamic environments

Colonies of ants can complete complex tasks without the need for centralised control as a result of interactions between individuals and their environment. Particularly remarkable is the process of path selection between the nest and food sources that is essential for successful foraging. We have designed a stochastic model of ant foraging in the absence of direct communication. The motion of ants is governed by two components - a random change in direction of motion that improves ability to explore the environment, and a non-random global indirect interaction component based on pheromone signalling. Our model couples individual-based off-lattice ant simulations with an on-lattice characterisation of the pheromone diffusion. Using numerical simulations we have tested three pheromone-based model alternatives: (1) a single pheromone laid on the way toward the food source and on the way back to the nest; (2) single pheromone laid on the way toward the food source and an internal imperfect compass to navigate toward the nest; (3) two different pheromones, each used for one direction. We have studied the model behaviour in different parameter regimes and tested the ability of our simulated ants to form trails and adapt to environmental changes. The simulated ants behaviour reproduced the behaviours observed experimentally. Furthermore we tested two biological hypotheses on the impact of the quality of the food source on the dynamics. We found that increasing pheromone deposition for the richer food sources has a larger impact on the dynamics than elevation of the ant recruitment level for the richer food sources.

Extracellular Vesicle Protein Expression in Doped Bioactive Glasses: Further Insights Applying Anomaly Detection.

Proteomic analysis of extracellular vesicles presents several challenges due to the unique nature of these small membrane-bound structures. Alternative analyses could reveal outcomes hidden from standard statistics to explore and develop potential new biological hypotheses that may have been overlooked during the initial evaluation of the data. An analysis sequence focusing on deviating protein expressions from donors' primary cells was performed, leveraging machine-learning techniques to analyze small datasets, and it has been applied to evaluate extracellular vesicles' protein content gathered from mesenchymal stem cells cultured on bioactive glass discs doped or not with metal ions. The goal was to provide additional opportunities for detecting details between experimental conditions that are not entirely revealed with classic statistical inference, offering further insights regarding the experimental design and assisting the researchers in interpreting the outcomes. The methodology extracted a set of EV-related proteins whose differences between conditions could be partially explainable with statistics, suggesting the presence of other factors involved in the bioactive glasses' interactions with tissues. Outlier identification of extracellular vesicles' protein expression levels related to biomaterial preparation was instrumental in improving the interpretation of the experimental outcomes.

Structural maturation of myofilaments in engineered 3D cardiac microtissues characterized using small angle x-ray scattering

Understanding the structural and functional development of human-induced pluripotent stem-cell-derived cardiomyocytes (hiPSC-CMs) is essential to engineering cardiac tissue that enables pharmaceutical testing, modeling diseases, and designing therapies. Here we use a method not commonly applied to biological materials, small angle x-ray scattering, to characterize the structural development of hiPSC-CMs within three-dimensional engineered tissues during their preliminary stages of maturation. An x-ray scattering experimental method enables the reliable characterization of the cardiomyocyte myofilament spacing with maturation time. The myofilament lattice spacing monotonically decreases as the tissue matures from its initial post-seeding state over the span of 10 days. Visualization of the spacing at a grid of positions in the tissue provides an approach to characterizing the maturation and organization of cardiomyocyte myofilaments and has the potential to help elucidate mechanisms of pathophysiology, and disease progression, thereby stimulating new biological hypotheses in stem cell engineering.

Leveraging electronic health records and knowledge networks for Alzheimer’s disease prediction and sex-specific biological insights

Identification of Alzheimer’s disease (AD) onset risk can facilitate interventions before irreversible disease progression. We demonstrate that electronic health records from the University of California, San Francisco, followed by knowledge networks (for example, SPOKE) allow for (1) prediction of AD onset and (2) prioritization of biological hypotheses, and (3) contextualization of sex dimorphism. We trained random forest models and predicted AD onset on a cohort of 749 individuals with AD and 250,545 controls with a mean area under the receiver operating characteristic of 0.72 (7 years prior) to 0.81 (1 day prior). We further harnessed matched cohort models to identify conditions with predictive power before AD onset. Knowledge networks highlight shared genes between multiple top predictors and AD (for example, APOE, ACTB, IL6 and INS). Genetic colocalization analysis supports AD association with hyperlipidemia at the APOE locus, as well as a stronger female AD association with osteoporosis at a locus near MS4A6A. We therefore show how clinical data can be utilized for early AD prediction and identification of personalized biological hypotheses.

Effects of dietary supplements, foods, and dietary patterns in Parkinson's disease: meta-analysis and systematic review of randomized and crossover studies.

Diet significantly impacts Parkinson's disease (PD) with plausible biological hypotheses. Although the thesis has been explored in several human clinical trials, no current meta-analyses or reviews summarize the results. We examined the effect of intervention of dietary supplements, foods, and dietary patterns in treating PD. We conducted a meta-analysis and systematic review of randomized and crossover studies published between 1989 and 26 June 2022, searching from PubMed, Embase, Medline, Scopus, Cochrane Library databases, and Chinese databases. Twenty-four studies were included in this review. The meta-analysis results show that dietary supplements intervention significantly increased the quantitative insulin sensitivity check index (QUICKI) [MD = 0.02, 95% CI (0.01, 0.02), p < 0.00001]. Dietary supplement intervention does not significantly affect the total Unified Parkinson Disease Rating Scale (UPDRS) score and six-min walk test (6MWT) distance. We did not find evidence that dietary supplements or food intervention may minimize the UPDRS III score. However, systematic review results indicated that the Mediterranean, low-fat, and ketogenic diets significantly reduced the total UPDRS score; low-protein diets meaningfully mitigated motor symptoms. This meta-analysis result displays that diet and diet supplements had a very modest but statistically significant impact on QUICKI but no effect on motor and non-motor symptoms in PD. The systematic review concludes that dietary patterns intervention may positively attenuate the overall symptoms of PD, including both motor and non-motor symptoms.

Measuring differential fitness costs and interactions between genetic cassettes using fluorescent spectrophotometry.

In this article, we present a method for designing, executing, and analyzing data from a microbial competition experiment. We use fluorescent reporters to label different competing strains and resolve individual growth curves using a fluorescent spectrophotometer. Our comprehensive data analysis pipeline integrates multiple experiments to simultaneously infer sources of variation, extract selection coefficients, and estimate the genetic contributions to fitness for various synthetic genetic cassettes (SGCs). To demonstrate the method, we employ a synthetic biological system based on Escherichia coli. Strains carry 1 of 10 different plasmids and one of three genomically integrated fluorescent markers. All strains are co-cultured to obtain real-time measurements of optical density (total population density) and fluorescence (sub-population densities). We identify challenges in calibrating between fluorescence and density and of fluorescent proteins maturing at different rates. To resolve these issues, we compare two methods of fluorescence calibration and correct for maturation by measuring in vivo maturation times. We provide evidence of genetic interactions occurring between our SGCs and further show how to use our statistical model to test some hypotheses about microbial growth and the costs of protein expression.IMPORTANCEFluorescently labeled co-cultures are becoming increasingly popular. The approach proposed here offers a high standard for experimental design and data analysis to measure selection coefficients and growth rates in competition. Measuring competitive differences is useful in many laboratory studies, allowing for fitness cost-correction of growth rates and ecological interactions and testing hypotheses in synthetic biology. Using time-resolved growth curves, rather than endpoint measurements, for competition assays allows us to construct a detailed scientific model that can be used to ask questions about fine-grained phenomena, such as bacterial growth dynamics, as well as higher-level phenomena, such as the interactions between synthetic cassette expression.

Hypothesis Description: Enemy Release Hypothesis

This paper provides a brief overview of a major hypothesis in invasion biology: the enemy release hypothesis. Building on a summary of different previous definitions, we provide the following revised definition: “A reduced pressure by enemies in the non-native range contributes to invasion success.” Further, we suggest formalizing the hypothesis in the basic form ‘subject - relationship - object’ to allow for disambiguating the different existing meanings and enhancing their usability by machines.

Transcriptome and machine learning analysis of the impact of COVID-19 on mitochondria and multiorgan damage.

The effects of coronavirus disease 2019 (COVID-19) primarily concern the respiratory tract and lungs; however, studies have shown that all organs are susceptible to infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 may involve multiorgan damage from direct viral invasion through angiotensin-converting enzyme 2 (ACE2), through inflammatory cytokine storms, or through other secondary pathways. This study involved the analysis of publicly accessible transcriptome data from the Gene Expression Omnibus (GEO) database for identifying significant differentially expressed genes related to COVID-19 and an investigation relating to the pathways associated with mitochondrial, cardiac, hepatic, and renal toxicity in COVID-19. Significant differentially expressed genes were identified and ranked by statistical approaches, and the genes derived by biological meaning were ranked by feature importance; both were utilized as machine learning features for verification. Sample set selection for machine learning was based on the performance, sample size, imbalanced data state, and overfitting assessment. Machine learning served as a verification tool by facilitating the testing of biological hypotheses by incorporating gene list adjustment. A subsequent in-depth study for gene and pathway network analysis was conducted to explore whether COVID-19 is associated with cardiac, hepatic, and renal impairments via mitochondrial infection. The analysis showed that potential cardiac, hepatic, and renal impairments in COVID-19 are associated with ACE2, inflammatory cytokine storms, and mitochondrial pathways, suggesting potential medical interventions for COVID-19-induced multiorgan damage.

CRISPR-DIPOFF: an interpretable deep learning approach for CRISPR Cas-9 off-target prediction.

CRISPR Cas-9 is a groundbreaking genome-editing tool that harnesses bacterial defense systems to alter DNA sequences accurately. This innovative technology holds vast promise in multiple domains like biotechnology, agriculture and medicine. However, such power does not come without its own peril, and one such issue is the potential for unintended modifications (Off-Target), which highlights the need for accurate prediction and mitigation strategies. Though previous studies have demonstrated improvement in Off-Target prediction capability with the application of deep learning, they often struggle with the precision-recall trade-off, limiting their effectiveness and do not provide proper interpretation of the complex decision-making process of their models. To address these limitations, we have thoroughly explored deep learning networks, particularly the recurrent neural network based models, leveraging their established success in handling sequence data. Furthermore, we have employed genetic algorithm for hyperparameter tuning to optimize these models' performance. The results from our experiments demonstrate significant performance improvement compared with the current state-of-the-art in Off-Target prediction, highlighting the efficacy of our approach. Furthermore, leveraging the power of the integrated gradient method, we make an effort to interpret our models resulting in a detailed analysis and understanding of the underlying factors that contribute to Off-Target predictions, in particular the presence of two sub-regions in the seed region of single guide RNA which extends the established biological hypothesis of Off-Target effects. To the best of our knowledge, our model can be considered as the first model combining high efficacy, interpretability and a desirable balance between precision and recall.

Chronogram: an R package for data curation and analysis of infection and vaccination cohort studies.

Observational cohort studies that track vaccine and infection responses offer real-world data to inform pandemic policy. Translating biological hypotheses, such as whether different patterns of accumulated antigenic exposures confer differing antibody responses, into analysis code can be onerous, particularly when source data is dis-aggregated. The R package chronogram introduces the class chronogram, where metadata is seamlessly aggregated with sparse infection episode, clinical and laboratory data. Each experimental modality is added sequentially, allowing the incorporation of new data, such as specialized time-consuming research assays, or their downstream analyses. Source data can be any rectangular data format, including database tables (such as structured query language databases). This supports annotations that aggregate data types/sources, for example, combining symptoms, molecular testing, and sequencing of one or more infectious episodes in a pathogen-agnostic manner. Chronogram arranges observational data to allow the translation of biological hypotheses into their corresponding code via a shared vocabulary. Chronogram is implemented R and available under an MIT licence at: https://www.github.com/FrancisCrickInstitute/chronogram; a user manual is available at: https://franciscrickinstitute.github.io/chronogram/.

CoGTEx: Unscaled system-level coexpression estimation from GTEx data forecast novel functional gene partners.

Coexpression estimations are helpful for analysis of pathways, cofactors, regulators, targets, and human health and disease. Ideally, coexpression estimations should consider as many diverse cell types as possible and consider that available data is not uniform across tissues. Importantly, the coexpression estimations accessible today are performed on a "tissue level", which is based on cell type standardized formulations. Little or no attention is paid to overall gene expression levels. The tissue-level estimation assumes that variance expression levels are more important than mean expression levels. Here, we challenge this assumption by estimating a coexpression calculation at the "system level", which is estimated without standardization by tissue, and show that it provides valuable information. We made available a resource to view, download, and analyze both, tissue- and system-level coexpression estimations from GTEx human data. GTEx v8 expression data was globally normalized, batch-processed, and filtered. Then, PCA, clustering, and tSNE stringent procedures were applied to generate 42 distinct and curated tissue clusters. Coexpression was estimated from these 42 tissue clusters computing the correlation of 33,445 genes by sampling 70 samples per tissue cluster to avoid tissue overrepresentation. This process was repeated 20 times, extracting the minimum value provided as a robust estimation. Three metrics were calculated (Pearson, Spearman, and G-statistic) in two data processing modes, at the system-level (TPM scale) and tissue levels (z-score scale). We first validate our tissue-level estimations compared with other databases. Then, by specific analyses in several examples and literature validations of predictions, we show that system-level coexpression estimation differs from tissue-level estimations and that both contain valuable information reflected in biological pathways. We also show that coexpression estimations are associated to transcriptional regulation. Finally, we present CoGTEx, a valuable resource for viewing and analyzing coexpressed genes in human adult tissues from GTEx v8 data. We introduce our web resource to list, view and explore the coexpressed genes from GTEx data. We conclude that system-level coexpression is a novel and interesting coexpression metric capable of generating plausible predictions and biological hypotheses; and that CoGTEx is a valuable resource to view, compare, and download system- and tissue- level coexpression estimations from GTEx data. The web resource is available at http://bioinformatics.mx/cogtex.

Mathematical models of TCR initial triggering.

T cell receptors (TCRs) play crucial roles in regulating T cell response by rapidly and accurately recognizing foreign and non-self antigens. The process involves multiple molecules and regulatory mechanisms, forming a complex network to achieve effective antigen recognition. Mathematical modeling techniques can help unravel the intricate network of TCR signaling and identify key regulators that govern it. In this review, we introduce and briefly discuss relevant mathematical models of TCR initial triggering, with a focus on kinetic proofreading (KPR) models with different modified structures. We compare the topology structures, biological hypotheses, parameter choices, and simulation performance of each model, and summarize the advantages and limitations of them. Further studies on TCR modeling design, aiming for an optimized balance of specificity and sensitivity, are expected to contribute to the development of new therapeutic strategies.

Protein structures unravel the signatures and patterns of deep time evolution.

The formulation and testing of hypotheses using 'big biology data' often lie at the interface of computational biology and structural biology. The Protein Data Bank (PDB), which was established about 50years ago, catalogs three-dimensional (3D) shapes of organic macromolecules and showcases a structural view of biology. The comparative analysis of the structures of homologs, particularly of proteins, from different species has significantly improved the in-depth analyses of molecular and cell biological questions. In addition, computational tools that were developed to analyze the 'protein universe' are providing the means for efficient resolution of longstanding debates in cell and molecular evolution. In celebrating the golden jubilee of the PDB, much has been written about the transformative impact of PDB on a broad range of fields of scientific inquiry and how structural biology transformed the study of the fundamental processes of life. Yet, the transforming influence of PDB on one field of inquiry of fundamental interest-the reconstruction of the distant biological past-has gone almost unnoticed. Here, I discuss the recent advances to highlight how insights and tools of structural biology are bearing on the data required for the empirical resolution of vigorously debated and apparently contradicting hypotheses in evolutionary biology. Specifically, I show that evolutionary characters defined by protein structure are superior compared to conventional sequence characters for reliable, data-driven resolution of competing hypotheses about the origins of the major clades of life and evolutionary relationship among those clades. Since the better quality data unequivocally support two primary domains of life, it is imperative that the primary classification of life be revised accordingly.

Bottom-up data integration in polymer models of chromatin organisation

Cellular functions crucially depend on the precise execution of complex biochemical reactions taking place on the chromatin fiber in the tightly packed environment of the cell nucleus. Despite the availability of large data sets probing this process from multiple angles, bottom-up frameworks which allow the incorporation of the sequence-specific nature of biochemistry in a unified model of 3D chromatin structure remain scarce. Here we propose SEMPER (Sequence Enhanced Magnetic PolymER), a novel stochastic polymer model which naturally incorporates observational data about sequence-driven biochemical processes, such as binding of transcription factor proteins, in a 3D model of chromatin structure. We introduce a novel approximate Bayesian algorithm to quantify a posteriori the relative importance of various factors, including the polymeric nature of DNA, in determining chromatin epigenetic state, thus providing a transparent way to generate biological hypotheses. While accurate prediction of contact frequencies (a problem already extensively studied in the literature) is not our main aim, as a byproduct of the inference procedure and without additional input from the genome 3D structure, our model can predict with reasonable accuracy some notable and nontrivial conformational features of chromatin folding within the nucleus. Our work highlights the importance of introducing physically realistic statistical models for predicting chromatin states from epigenetic data and opens the way to a new class of more systematic approaches to interpreting epigenomic data.

Trace Element Composition of Placer Gold Across the Okanagan Fault, Kelowna, British Columbia, Canada

For 100 years, placer gold has been important to the settlement, economic development, and, recently, recreational geology of the Kelowna, British Columbia, area. It is best-known to occur in modern-day, Mission Creek and Lambly Creek sedimentary rocks, as well as a paleoplacer occurrence in Miocene sediments of the historical Winfield mine. The Mission Creek and Winfield localities are east of the west-dipping, low-angle, normal Okanagan Fault, which has been active since the Eocene. Lambly Creek is west of the fault. Late Paleozoic to Eocene igneous and metasedimentary rocks occur in the Lambly Creek catchment but Eocene gneiss units, unroofed by the fault, occur on the Okanagan Valley’s east side. This study tests the hypothesis that native placer gold compositions vary across the Okanagan Fault reflecting different sources and histories for the gold. A modest number of Au and Ag analyses (23 analyses) in usefully representative placer gold samples were determined on a scanning electron microscope with an energy dispersive spectrometer (SEM-EDS). Spots analyzed for Au and Ag were also analyzed for 19 trace elements using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). Mercury was semi-quantitatively determined in ‘unknown’ gold grains by first estimating its concentration (~3.69 ppm) in the AuRM2 external standard. Proportions of Au:Ag:Cu in grain cores indicate all the gold came from mesothermal/hypogene or possibly Au porphyry bedrock deposits though primary signatures may have been obscured by metamorphism or weathering. Winfield and Mission Creek grains tend to have higher siderophile Fe, Ni, Pd and Pt and chalcophile elements As, Se, Te, Hg, Pb and Bi but lower Cu and Sb concentrations than Lambly Creek gold. Mercury is distinctly higher in Winfield and Mission Creek gold than in Lambly Creek gold from the west side of the valley; the element appears particularly useful for ‘fingerprinting’ gold. Lambly Creek gold compositions indicate derivation from two orogenic/hypogene sources from greenstone and plutonic/hydrothermal rocks present in the catchment area. Modern day Mission Creek and Miocene paleoplacer Winfield grains have a similar hypogene trace element signature but there are no known local bedrock gold sources. The Mission Creek and Winfield gold grain cores are surrounded by &lt; 10 µm, Au-rich, Ag- and trace element-poor, rims. Lambly Creek grains lack such rims. The Au-rich rims on modern day Mission Creek and Miocene Winfield gold may reflect prolonged near-surface exposure with surficial electrochemical dissolution of hypogene trace elements or the biological precipitation of gold. Low Ag and red colouration on the surface of grains support the biological precipitation hypothesis. The shared trace element signature, together with the Au-rich rims indicate that modern day placer gold in Mission Creek was multiply reworked from Miocene paleoplacers similar to the Winfield occurrence as a result of uplift and erosion of rocks on the east side of Okanagan Fault.

Mutations in the RNA Splicing Factors U2AF1 and SRSF2 are Context-Dependent in Myeloproliferative Neoplasms

RNA splicing factor gene mutations are recurrently found in BCR::ABL1 negative myeloproliferative neoplasms (MPN). To understand the interactions between spliceosome gene mutations and MPN phenotypic driver mutations ( JAK2, CALR and MPL), we investigated their patterns of co-occurrence or mutual exclusivity in a cohort of 990 MPN patients with comprehensive molecular profiling and clinical information from the Hematologic Malignancies Data Repository (HMDR). As expected, MPN driver mutations ( JAK2, CALR and MPL) were mutually exclusive (N=658, N=168, N=53 respectively, Log2 OR≤-3.5 and Padj. ≤0.005 using pairwise Fisher's exact tests with Benjamin Hochberg multiple testing correction). Additionally, cases with co-occurring mutations in two RNA splicing factors, SF3B1, U2AF1 and SRSF2 (N=44, N=49, N=44) respectively were uncommon (2/135 splicing factor mutated cases) . We further found that U2AF1 and SRSF2 mutations co-occurred with JAK2 (log2 OR=1.7, Padj.=0.009) and MPL (log2 OR=2.1, Padj.=0.006) respectively, but rarely with CALR mutations (N=2 CALR-U2AF1 log2 OR=-2.3 Padj.=0.025, N=2 CALR-SRSF2 log2 OR=-2.2 Padj.=0.048). In contrast, CALR-SF3B1 mutations occurred at the expected frequency (Padj.=0.11). To explore the molecular settings where these mutations co-occur, we investigated the variant allele frequencies (VAFs) of CALR, U2AF1, SRSF2 and concomitant mutations in the CALR-U2AF1 (N=2) and CALR-SRSF2 (N=2) patients. The VAFs for the mutations detected in the first patient with CALR-U2AF1 co-mutation were: CALR 0.26, U2AF1 0.45, and TET2 0.41. Since CALR and splicing factor mutations are typically heterozygous, this suggests the CALR mutation was sub-clonal to the U2AF1 mutation and that U2AF1 and TET2 mutations were likely present in the same cell. In the second CALR-U2AF1 co-mutant case, VAFs were: CALR 0.31, U2AF1 0.27, and TP53 0.16. It is possible that in this case, the CALR and U2AF1 mutations may have occurred in independent clones or may have occurred in the same cell in the absence of other pathogenic mutations. For the two CALR-SRSF2 patients, data from three samples revealed VAFs of 0.23-0.46 for CALR and 0.21-0.58 for SRSF2 mutations. Considering their typical heterozygous occurrence, we inferred those individual cells harbored both CALR and SRSF2 mutations. Notably, in samples with high VAFs (&amp;gt;0.37) for both CALR and SRSF2 in two separate patients, additional high VAF pathogenic mutations were observed: 0.35 for ASXL1 and 0.46 for RUNX1 respectively. As ASXL1 and RUNX1 mutations also typically occur in a heterozygous manner, we hypothesized that when CALR and SRSF2 mutations are present in the same cell, another pathogenic co-mutation may be required for survival of the clone. To test our hypothesis, we FACS-sorted single lineage negative CD34 positive bone marrow cells from the first CALR- SRSF2 co-mutant case into 96 wells plates containing methylcellulose and hematopoietic growth factors (CFU-GEMM). DNA was harvested from single-cell colonies and genotyped for the following mutations using polymerase chain reaction (PCR) followed by gel electrophoresis and Sanger sequencing: CALR c.1154_1155insTTGTC p.K385fs*, SRSF2 c.284G&amp;gt;T p.P95H, ASXL1 c.1926_1927insG p.G642fs*, EZH2 c.2188_2212delAAAAAACAGCTCTTCGCCAGTCTGG p.F729fs*. Bulk NGS VAFs were 0.46, 0.38, 0.35, and 0.17, respectively. Out of 73 colonies analyzed, 71 colonies had the CALR insertion 5 mutation detected (see figure). Among these 71 CALR-mutated colonies, SRSF2 was found to co-occur in 35 colonies. Interestingly, all 35 colonies were accompanied by an ASXL1 mutation, and 29 of them had an additional EZH2 mutation. These findings support our hypothesis that CALR and SRSF2 mutations may co-occur in the same cell in the presence of another pathogenic mutation. Our findings offer molecular insights into the context-dependent behavior of splicing factor mutations in MPN. While they can co-occur with JAK2 and MPL mutations, U2AF1 and SRSF2 mutations are largely mutually exclusive with CALR mutations. In addition, our findings generate two main biological hypotheses: (i) a synthetic lethal relationship may exist between CALR mutations and SRSF2 or U2AF1 mutations, and (ii) this synthetic lethality may be overcome by the presence of additional pathogenic mutation(s) in the cell. Validation studies are ongoing to address these hypotheses. AM and AEM contributed equally.

Человек в контексте биологической гипотезы сознания

The article analyzes the problem of human consciousness, which is commonly called the main mystery of the Universe. The authors proceed from the fact that traditional views on it do not permit to properly investigate the essence of human consciousness. According to the authors of the article, the allocation of human conscious-ness as a special autonomous object of cognition does not correspond to the idea of genetic organization of living matter between different levels. Of particular difficulty is the task of explaining the birth of mental events from neurophysiological processes occurring in the human brain. In this connection, the authors propose a new formulation of the human problem in the context of the biological hypothesis of consciousness, according to which the latter is defined as the result of the activity of special microorganisms inside man, the essence of which he knows little about, since he is accustomed to consider himself the “crown of nature” as per the con-cept of European culture. Reference is made to the consistency of the assertion that man in general and his consciousness in particular should be subject to the same biological laws of existence as other living organ-isms, since they are all part of a single physical reality.

Enhancing nicotinamide N-methyltransferase bisubstrate inhibitor activity through 7-deazaadenosine and linker modifications

Nicotinamide N-methyltransferase (NNMT) catalyzes the transfer of a methyl group from S-adenosylmethionine (SAM) to nicotinamide (NAM) and other pyridine-related compounds and is involved in various metabolic processes in the human body. In addition, abnormal expression of NNMT occurs under various pathological conditions such as cancer, diabetes, metabolic disorders, and neurodegenerative diseases, making it a promising drug target worthy of in-depth research. Small-molecule NNMT inhibitors with high potency and selectivity are necessary chemical tools to test biological hypotheses and potential therapies. In this study, we developed a series of highly active NNMT inhibitors by modifying N7 position of adenine. Among them, compound 3–12 (IC50 = 47.9 ± 0.6 nM) exhibited potent inhibitory activity and also had an excellent selectivity profile over a panel of human methyltransferases. We showed that the N7 position of adenine in the NNMT bisubstrate inhibitor was a modifiable site, thus offering insights into the development of NNMT inhibitors.