Rare, Tightly-Bound, Multi-Cellular Clusters in the Pancreatic Ducts of Adult Mice Function Like Progenitor Cells and Survive and Proliferate After Acinar Cell Injury.

Pancreatic ductal progenitor cells have been proposed to contribute to adult tissue maintenance and regeneration after injury, but the identity of such ductal cells remains elusive. Here, from adult mice, we identify a near homogenous population of ductal progenitor-like clusters, with an average of 8 cells per cluster. They are a rare subpopulation, about 0.1% of the total pancreatic cells, and can be sorted using a fluorescence-activated cell sorter with the CD133highCD71lowFSCmid-high phenotype. They exhibit properties in self-renewal and tri-lineage differentiation (including endocrine-like cells) in a unique 3-dimensional colony assay system. An in vitro lineage tracing experiment, using a novel HprtDsRed/+ mouse model, demonstrates that a single cell from a cluster clonally gives rise to a colony. Droplet RNAseq analysis demonstrates that these ductal clusters express embryonic multipotent progenitor cell markers Sox9, Pdx1, and Nkx6-1, and genes involved in actin cytoskeleton regulation, inflammation responses, organ development, and cancer. Surprisingly, these ductal clusters resist prolonged trypsin digestion in vitro, preferentially survive in vivo after a severe acinar cell injury and become proliferative within 14 days post-injury. Thus, the ductal clusters are the fundamental units of progenitor-like cells in the adult murine pancreas with implications in diabetes treatment and tumorigenicity.

Regulation of EGF-stimulated activation of the PI-3K/AKT pathway by exocyst-mediated exocytosis.

The phosphoinositide-3 kinase (PI-3K)/AKT cell survival pathway is an important pathway activated by EGFR signaling. Here we show, that in addition to previously described critical components of this pathway, i.e., the docking protein Gab1, the PI-3K/AKT pathway in epithelial cells is regulated by the exocyst complex, which is a vesicle tether that is essential for exocytosis. Using live-cell imaging, we demonstrate that PI(3,4,5)P3 levels fluctuate at the membrane on a minutes time scale and that these fluctuations are associated with local PI(3,4,5)P3 increases at sites where recycling vesicles undergo exocytic fusion. Supporting a role for exocytosis in PI(3,4,5)P3 generation, acute promotion of exocytosis by optogenetically driving exocyst-mediated vesicle tethering up-regulates PI(3,4,5)P3 production and AKT activation. Conversely, acute inhibition of exocytosis using Endosidin2, a small-molecule inhibitor of the exocyst subunit Exo70 (also designated EXOC7), or inhibition of exocyst function by siRNA-mediated knockdown of the exocyst subunit Sec15 (EXOC6), impairs PI(3,4,5)P3 production and AKT activation induced by EGF stimulation of epithelial cells. Moreover, prolonged inhibition of EGF signaling by EGFR tyrosine kinase inhibitors results in spontaneous reactivation of AKT without a concomitant relief of EGFR inhibition. However, this reactivation can be negated by acutely inhibiting the exocyst. These experiments demonstrate that exocyst-mediated exocytosis-by regulating PI(3,4,5)P3 levels at the plasma membrane-subserves activation of the PI-3K/AKT pathway by EGFR in epithelial cells.

Academic publishing requires linguistically inclusive policies.

Scientific knowledge is produced in multiple languages but is predominantly published in English. This practice creates a language barrier to generate and transfer scientific knowledge between communities with diverse linguistic backgrounds, hindering the ability of scholars and communities to address global challenges and achieve diversity and equity in science, technology, engineering and mathematics (STEM). To overcome those barriers, publishers and journals should provide a fair system that supports non-native English speakers and disseminates knowledge across the globe. We surveyed policies of 736 journals in biological sciences to assess their linguistic inclusivity, identify predictors of inclusivity, and propose actions to overcome language barriers in academic publishing. Our assessment revealed a grim landscape where most journals were making minimal efforts to overcome language barriers. The impact factor of journals was negatively associated with adopting a number of inclusive policies whereas ownership by a scientific society tended to have a positive association. Contrary to our expectations, the proportion of both open access articles and editors based in non-English speaking countries did not have a major positive association with the adoption of linguistically inclusive policies. We proposed a set of actions to overcome language barriers in academic publishing, including the renegotiation of power dynamics between publishers and editorial boards.

TBP facilitates RNA Polymerase I transcription following mitosis.

The TATA-box binding protein (TBP) is the sole transcription factor common in the initiation complexes of the three major eukaryotic RNA Polymerases (Pol I, II and III). Although TBP is central to transcription by the three RNA Pols in various species, the emergence of TBP paralogs throughout evolution has expanded the complexity in transcription initiation. Furthermore, recent studies have emerged that questioned the centrality of TBP in mammalian cells, particularly in Pol II transcription, but the role of TBP and its paralogs in Pol I transcription remains to be re-evaluated. In this report, we show that in murine embryonic stem cells TBP localizes onto Pol I promoters, whereas the TBP paralog TRF2 only weakly associates to the Spacer Promoter of rDNA, suggesting that it may not be able to replace TBP for Pol I transcription. Importantly, acute TBP depletion does not fully disrupt Pol I occupancy or activity on ribosomal RNA genes, but TBP binding in mitosis leads to efficient Pol I reactivation following cell division. These findings provide a more nuanced role for TBP in Pol I transcription in murine embryonic stem cells.

Metabolic switch from glycogen to lipid in the liver maintains glucose homeostasis in neonatal mice.

Neonates strive to acquire energy when the continuous transplacental nutrient supply ceases at birth, whereas milk consumption takes hours to start. Using murine models, we report the metabolic switches in the first days of life, with an unexpected discovery of glucose as the universal fuel essential for neonatal life. Blood glucose quickly drops as soon as birth, but immediately rebounds even before suckling and maintains stable afterward. Meanwhile, neonatal liver undergoes drastic metabolic changes, from extensive glycogenolysis before suckling to dramatically induced fatty acid oxidation (FAO) and gluconeogenesis after milk suckling. Unexpectedly, blocking hepatic glycogenolysis only caused a transient hypoglycemia before milk suckling without causing lethality. Limiting lipid supply in milk (low-fat milk, [LFM]) using Cidea-/- mice, however, led to a chronic and severe hypoglycemia and consequently claimed neonatal lives. While fat replenishment rescued LFM-caused neonatal lethality, the rescue effects were abolished by blocking FAO or gluconeogenesis, pointing to a funneling of lipids and downstream metabolites into glucose as the essential fuel. Finally, glucose administration also rescued LFM-caused neonatal lethality, independent on FAO or gluconeogenesis. Therefore, our results show that the liver works as an energy conversion center to maintain blood glucose homeostasis in neonates, providing theoretical basis for managing infant hypoglycemia.

Different molecular enumeration influences in deep learning: an example using aqueous solubility.

Aqueous solubility is the key property driving many chemical and biological phenomena and impacts experimental and computational attempts to assess those phenomena. Accurate prediction of solubility is essential and challenging, even with modern computational algorithms. Fingerprint-based, feature-based and molecular graph-based representations have all been used with different deep learning methods for aqueous solubility prediction. It has been clearly demonstrated that different molecular representations impact the model prediction and explainability. In this work, we reviewed different representations and also focused on using graph and line notations for modeling. In general, one canonical chemical structure is used to represent one molecule when computing its properties. We carefully examined the commonly used simplified molecular-input line-entry specification (SMILES) notation representing a single molecule and proposed to use the full enumerations in SMILES to achieve better accuracy. A convolutional neural network (CNN) was used. The full enumeration of SMILES can improve the presentation of a molecule and describe the molecule with all possible angles. This CNN model can be very robust when dealing with large datasets since no additional explicit chemistry knowledge is necessary to predict the solubility. Also, traditionally it is hard to use a neural network to explain the contribution of chemical substructures to a single property. We demonstrated the use of attention in the decoding network to detect the part of a molecule that is relevant to solubility, which can be used to explain the contribution from the CNN.

Parallel evolution in human populations: A biocultural perspective.

Parallel evolution-where different populations evolve similar traits in response to similar environments-has been a topic of growing interest to biologists and biological anthropologists for decades. Parallel evolution occurs in human populations thanks to myriad biological and cultural mechanisms that permit humans to survive and thrive in diverse environments worldwide. Because humans shape and are shaped by their environments, biocultural approaches that emphasize the interconnections between biology and culture are key to understanding parallel evolution in human populations as well as the nuances of human biological variation and adaptation. In this review, we discuss how biocultural theory has been and can be applied to studies of parallel evolution and adaptation more broadly. We illustrate this through four examples of parallel evolution in humans: malaria resistance, lactase persistence, cold tolerance, and high-altitude adaptation.

Response to Toshihide Tsuda, Yumiko Miyano and Eiji Yamamoto [1].

BACKGROUND: In August 2021, we published in Environmental Health a Toolkit for detecting misused epidemiological methods with the goal of providing an organizational framework for transparently evaluating epidemiological studies, a body of evidence, and resultant conclusions. Tsuda et al., the first group to utilize the Toolkit in a systematic fashion, have offered suggestions for its modification. MAIN BODY: Among the suggested modifications made by Tsuda et al., we agree that rearrangement of Part A of the Toolkit to reflect the sequence of the epidemiological study process would facilitate its usefulness. Expansion or adaptation of the Toolkit to other disciplines would be valuable but would require the input of discipline-specific expertise. We caution against using the sections of the Toolkit to produce a tally or cumulative score, because none of the items are weighted as to importance or impact. Rather, we suggest a visual representation of how a study meets the Toolkit items, such as the heat maps used to present risk of bias criteria for studies included in Cochrane reviews. We suggest that the Toolkit be incorporated in the sub-specialty known as "forensic epidemiology," as well as in graduate training curricula, continuing education programs, and conferences, with the recognition that it is an extension of widely accepted ethics guidelines for epidemiological research. CONCLUSION: We welcome feedback from the research community about ways to strengthen the Toolkit as it is applied to a broader assemblage of research studies and disciplines, contributing to its value as a living tool/instrument. The application of the Toolkit by Tsuda et al. exemplifies the usefulness of this framework for transparently evaluating, in a systematic way, epidemiological research, conclusions relating to causation, and policy decisions. POSTSCRIPT: We note that our Toolkit has, most recently, inspired authors with discipline-specific expertise in the field of Conservation Biology to adapt it for use in the Biological Sciences.

Citizen science frontiers: Efficiency, engagement, and serendipitous discovery with human-machine systems.

Citizen science has proved to be a unique and effective tool in helping science and society cope with the ever-growing data rates and volumes that characterize the modern research landscape. It also serves a critical role in engaging the public with research in a direct, authentic fashion and by doing so promotes a better understanding of the processes of science. To take full advantage of the onslaught of data being experienced across the disciplines, it is essential that citizen science platforms leverage the complementary strengths of humans and machines. This Perspectives piece explores the issues encountered in designing human-machine systems optimized for both efficiency and volunteer engagement, while striving to safeguard and encourage opportunities for serendipitous discovery. We discuss case studies from Zooniverse, a large online citizen science platform, and show that combining human and machine classifications can efficiently produce results superior to those of either one alone and how smart task allocation can lead to further efficiencies in the system. While these examples make clear the promise of human-machine integration within an online citizen science system, we then explore in detail how system design choices can inadvertently lower volunteer engagement, create exclusionary practices, and reduce opportunity for serendipitous discovery. Throughout we investigate the tensions that arise when designing a human-machine system serving the dual goals of carrying out research in the most efficient manner possible while empowering a broad community to authentically engage in this research.

Ancient mtDNA sequences from the First Australians revisited.

The publication in 2001 by Adcock et al. [Adcock GJ, et al. (2001) Proc Natl Acad Sci USA 98(2):537-542] in PNAS reported the recovery of short mtDNA sequences from ancient Australians, including the 42,000-y-old Mungo Man [Willandra Lakes Hominid (WLH3)]. This landmark study in human ancient DNA suggested that an early modern human mitochondrial lineage emerged in Asia and that the theory of modern human origins could no longer be considered solely through the lens of the "Out of Africa" model. To evaluate these claims, we used second generation DNA sequencing and capture methods as well as PCR-based and single-primer extension (SPEX) approaches to reexamine the same four Willandra Lakes and Kow Swamp 8 (KS8) remains studied in the work by Adcock et al. Two of the remains sampled contained no identifiable human DNA (WLH15 and WLH55), whereas the Mungo Man (WLH3) sample contained no Aboriginal Australian DNA. KS8 reveals human mitochondrial sequences that differ from the previously inferred sequence. Instead, we recover a total of five modern European contaminants from Mungo Man (WLH3). We show that the remaining sample (WLH4) contains ∼1.4% human DNA, from which we assembled two complete mitochondrial genomes. One of these was a previously unidentified Aboriginal Australian haplotype belonging to haplogroup S2 that we sequenced to a high coverage. The other was a contaminating modern European mitochondrial haplotype. Although none of the sequences that we recovered matched those reported by Adcock et al., except a contaminant, these findings show the feasibility of obtaining important information from ancient Aboriginal Australian remains.

TNFα-stimulated gene-6 (TSG6) activates macrophage phenotype transition to prevent inflammatory lung injury.

TNFα-stimulated gene-6 (TSG6), a 30-kDa protein generated by activated macrophages, modulates inflammation; however, its mechanism of action and role in the activation of macrophages are not fully understood. Here we observed markedly augmented LPS-induced inflammatory lung injury and mortality in TSG6-/- mice compared with WT (TSG6+/+) mice. Treatment of mice with intratracheal instillation of TSG6 prevented LPS-induced lung injury and neutrophil sequestration, and increased survival in mice. We found that TSG6 inhibited the association of TLR4 with MyD88, thereby suppressing NF-κB activation. TSG6 also prevented the expression of proinflammatory proteins (iNOS, IL-6, TNFα, IL-1ß, and CXCL1) while increasing the expression of anti-inflammatory proteins (CD206, Chi3l3, IL-4, and IL-10) in macrophages. This shift was associated with suppressed activation of proinflammatory transcription factors STAT1 and STAT3. In addition, we observed that LPS itself up-regulated the expression of TSG6 in TSG6+/+ mice, suggesting an autocrine role for TSG6 in transitioning macrophages. Thus, TSG6 functions by converting macrophages from a proinflammatory to an anti-inflammatory phenotype secondary to suppression of TLR4/NF-κB signaling and STAT1 and STAT3 activation.

Development and assessment of an academic performance enrichment program for low-performing, first-year pharmacy students.

Pharmacy school applications have steadily declined over the past several years. Thus pharmacy schools are not only searching for effective means to increase enrollment of qualified candidates, but are also focusing on the development of programs to improve academic performance and retention of enrolled students. To address the needs of struggling first-year pharmacy students enrolled in an Integrated Biological Sciences (BSI) course, an academic performance enrichment program (APEP) was developed. The program was designed to improve academic success by engaging low-performing students with the aims of improving their time management skills, study skills, metacognition, and understanding of BSI course material. The APEP consisted of structured tutoring sessions twice per week, which were required for all students with a course grade ≤73.5% at any point during the semester. To assess program effectiveness, performance improvement on BSI exams by the APEP students were compared with that of non-APEP students in the same class and to those in the previous 3 yr. Student perceptions of the program were also evaluated via an online survey. The APEP was deemed effective in that a greater percentage of students were able to improve their exam scores and to a greater extent by attending the APEP sessions compared with non-APEP students in the same class and with low-performing students in previous years when the APEP did not exist. Furthermore, APEP students believed the program was effective in meeting its aims. In conclusion, the APEP was effective in improving academic performance of low-performing students in BSI.

Reusable learning objects for nurse education: development, evaluation, challenges and recommendations.

Online resources are expected within healthcare education, and a plethora of online or technology-based delivery methods are available. Reusable learning objects (RLOs) are a form of digitally supported education that can be used multiple times in various locations; they are especially favoured by nurses. Little is understood about the issues involved in their creation. This article examines the development of an RLO in respiratory physiology for first-year nurses and how those creating it worked together. Feedback during the development of the RLO was gathered over 1 year from academics, technologists and students. Issues that arose included variations and misunderstanding regarding terminology and academics' not appreciating the time it took to develop the resource and its potential. Practical matters included sourcing royalty-free or in-house images, record-keeping and version control, and addressing production logic in case developers moved to other projects. It is important to include students during the design process rather than in just evaluating the final product because user experience and navigation have to be considered together with pedagogical content. Addressing these issues when developing an RLO will help streamline the process and generate a student-focused output.

A method for integrating neuroimaging into genetic models of learning performance.

Specific learning disorders (SLD) are an archetypal example of how clinical neuropsychological (NP) traits can differ from underlying genetic and neurobiological risk factors. Disparate environmental influences and pathologies impact learning performance assessed through cognitive examinations and clinical evaluations, the primary diagnostic tools for SLD. We propose a neurobiological risk for SLD with neuroimaging biomarkers, which is integrated into a genome-wide association study (GWAS) of learning performance in a cohort of 479 European individuals between 8 and 21 years of age. We first identified six regions of interest (ROIs) in temporal and anterior cingulate regions where the group diagnosed with learning disability has the least overall variation, relative to the other group, in thickness, area, and volume measurements. Although we used the three imaging measures, the thickness was the leading contributor. Hence, we calculated the Euclidean distances between any two individuals based on their thickness measures in the six ROIs. Then, we defined the relative similarity of one individual according to the averaged ranking of pairwise distances from the individuals to those in the SLD group. The inverse of this relative similarity is called the neurobiological risk for the individual. Single nucleotide polymorphisms in the AGBL1 gene on chromosome 15 had a significant association with learning performance at a genome-wide level. This finding was supported in an independent cohort of 2,327 individuals of the same demographic profile. Our statistical approach for integrating genetic and neuroimaging biomarkers can be extended into studying the biological basis of other NP traits.

Heightened potency of human pluripotent stem cell lines created by transient BMP4 exposure.

Human pluripotent stem cells (PSCs) show epiblast-type pluripotency that is maintained with ACTIVIN/FGF2 signaling. Here, we report the acquisition of a unique stem cell phenotype by both human ES cells (hESCs) and induced pluripotent stem cells (iPSCs) in response to transient (24-36 h) exposure to bone morphogenetic protein 4 (BMP4) plus inhibitors of ACTIVIN signaling (A83-01) and FGF2 (PD173074), followed by trypsin dissociation and recovery of colonies capable of growing on a gelatin substratum in standard medium for human PSCs at low but not high FGF2 concentrations. The self-renewing cell lines stain weakly for CDX2 and strongly for NANOG, can be propagated clonally on either Matrigel or gelatin, and are morphologically distinct from human PSC progenitors on either substratum but still meet standard in vitro criteria for pluripotency. They form well-differentiated teratomas in immune-compromised mice that secrete human chorionic gonadotropin (hCG) into the host mouse and include small areas of trophoblast-like cells. The cells have a distinct transcriptome profile from the human PSCs from which they were derived (including higher expression of NANOG, LEFTY1, and LEFTY2). In nonconditioned medium lacking FGF2, the colonies spontaneously differentiated along multiple lineages, including trophoblast. They responded to PD173074 in the absence of both FGF2 and BMP4 by conversion to trophoblast, and especially syncytiotrophoblast, whereas an A83-01/PD173074 combination favored increased expression of HLA-G, a marker of extravillous trophoblast. Together, these data suggest that the cell lines exhibit totipotent potential and that BMP4 can prime human PSCs to a self-renewing alternative state permissive for trophoblast development. The results may have implications for regulation of lineage decisions in the early embryo.

Behavioral diversity in microbes and low-dimensional phenotypic spaces.

Systematic studies of phenotypic diversity--required for understanding evolution--lag behind investigations of genetic diversity. Here we develop a quantitative approach to studying behavioral diversity, which we apply to swimming of the ciliate Tetrahymena. We measure the full-lifetime behavior of hundreds of individual organisms at high temporal resolution, over several generations and in diverse nutrient conditions. To characterize population diversity and temporal variability we introduce a unique statistical framework grounded in the notion of a phenotypic space of behaviors. We show that this space is effectively low dimensional with dimensions that correlate with a two-state "roaming and dwelling" model of swimming behavior. Temporal variability over the lifetime of an individual is correlated with the fraction of time spent roaming whereas diversity between individuals is correlated with the speed of roaming. Quantifying the dynamics of behavioral variation shows that behavior over the lifetime of an individual is strongly nonstationary. Analysis of behavioral dynamics between generations reveals complex patterns of behavioral heritability that point to the importance of considering correlations beyond mothers and daughters. Our description of a low-dimensional behavioral space should enable the systematic study of the evolutionary and ecological bases of phenotypic constraints. Future experimental and theoretical studies of behavioral diversity will have to account for the possibility of nonstationary and environmentally dependent behavioral dynamics that we observe.

tDCS-induced alterations in GABA concentration within primary motor cortex predict motor learning and motor memory: a 7 T magnetic resonance spectroscopy study.

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique that alters cortical excitability in a polarity specific manner and has been shown to influence learning and memory. tDCS may have both on-line and after-effects on learning and memory, and the latter are thought to be based upon tDCS-induced alterations in neurochemistry and synaptic function. We used ultra-high-field (7 T) magnetic resonance spectroscopy (MRS), together with a robotic force adaptation and de-adaptation task, to investigate whether tDCS-induced alterations in GABA and Glutamate within motor cortex predict motor learning and memory. Note that adaptation to a robot-induced force field has long been considered to be a form of model-based learning that is closely associated with the computation and 'supervised' learning of internal 'forward' models within the cerebellum. Importantly, previous studies have shown that on-line tDCS to the cerebellum, but not to motor cortex, enhances model-based motor learning. Here we demonstrate that anodal tDCS delivered to the hand area of the left primary motor cortex induces a significant reduction in GABA concentration. This effect was specific to GABA, localised to the left motor cortex, and was polarity specific insofar as it was not observed following either cathodal or sham stimulation. Importantly, we show that the magnitude of tDCS-induced alterations in GABA concentration within motor cortex predicts individual differences in both motor learning and motor memory on the robotic force adaptation and de-adaptation task.

Global burden of head and neck cancers from 1990 to 2019.

Head and neck cancer (HNC) exerts a significant healthcare burden worldwide. Insufficient data impedes a comprehensive understanding of its global impact. Through analysis of the 2019 Global Burden of Disease (GBD) database, our secondary investigation unveiled a surging global incidence of HNC, yet a decline in associated mortality and disability-adjusted life years (DALYs) owing to enhanced prognosis. Particularly noteworthy is the higher incidence of escalation among females compared to males. Effective resource allocation, meticulous control of risk factors, and tailored interventions are imperative to curtail mortality rates among young individuals afflicted with HNC in underprivileged regions, as well as in elderly individuals grappling with thyroid cancer.

Revealing neural dynamical structure of C. elegans with deep learning.

Caenorhabditis elegans serves as a common model for investigating neural dynamics and functions of biological neural networks. Data-driven approaches have been employed in reconstructing neural dynamics. However, challenges remain regarding the curse of high-dimensionality and stochasticity in realistic systems. In this study, we develop a deep neural network (DNN) approach to reconstruct the neural dynamics of C. elegans and study neural mechanisms for locomotion. Our model identifies two limit cycles in the neural activity space: one underpins basic pirouette behavior, essential for navigation, and the other introduces extra Ω turns. The combination of two limit cycles elucidates predominant locomotion patterns in neural imaging data. The corresponding energy landscape explains the switching strategies between two limit cycles, quantitatively, and provides testable predictions on neural functions and circuit roles. Our work provides a general approach to study neural dynamics by combining imaging data and stochastic modeling.