bluuebunny/crossref_metadata_2025 huggingface dataset:
Bundle sheath cell-dependent chloroplast movement in mesophyll cells of C4plants analyzed using live leaf-section imaging
Authors: Kato Yuta , Oi Takao , Sato Yoshikatsu , Taniguchi Mitsutaka
Published: September 2024

In C4plants, mesophyll (M) chloroplasts aggregate toward bundle sheath (BS) cells in response to environmental stress, which would contribute to C4photosynthetic cycle adjustment between M and BS cells. However, it remains unclear whether mesophyll chloroplast movement is an intercellular response mediated by BS cells. One major challenge to resolving this is the difficulty in observing how M chloroplasts aggregate toward adjacent BS cells due to scattering and absorption of observation light in live-leaf tissues. We established a live leaf-section imaging technique that enables the long-term observation of sections of chemically unfixed leaf blades, with which we quantitatively analyzed M chloroplast movements. Another challenge in clarifying the contribution of BS cells to M chloroplast movement is the selective ablation of BS cells without impairing their function of M cells. To investigate the necessity of BS cells for M chloroplast movement, we developed a method to remove BS cells only based on differences in shape and size between M and BS cells. We also found that chloroplasts in M cells without adjacent BS cell contents did not show typical aggregative movement but showed a light-avoidance response. This indicates that the M chloroplast aggregative movement occurs during communication with BS cells.

Highlight

We established live leaf-section imaging to observe individual chloroplast movements in multi-layered cells and found that bundle sheath cells are involved in the aggregative movement of mesophyll chloroplasts.
Small DNA elements that act as both insulators and silencers in plants
Authors: Jores Tobias , Mueth Nicholas A. , Tonnies Jackson , Char Si Nian , Liu Bo , Grillo-Alvarado Valentina , Abbitt Shane , Anand Ajith , Deschamps Stéphane , Diehn Scott , Gordon-Kamm Bill , Jiao Shuping , Munkvold Kathy , Snowgren Heather , Sardesai Nagesh , Fields Stanley , Yang Bing , Cuperus Josh T. , Queitsch Christine
Published: September 2024

Insulators arecis-regulatory elements that separate transcriptional units, whereas silencers are elements that repress transcription regardless of their position. In plants, these elements remain largely uncharacterized. Here, we use the massively parallel reporter assay Plant STARR-seq with short fragments of eight large insulators to identify more than 100 fragments that block enhancer activity. The short fragments can be combined to generate more powerful insulators that abolish the capacity of the strong viral 35S enhancer to activate the 35S minimal promoter. Unexpectedly, when tested upstream of weak enhancers, these fragments act as silencers and repress transcription. Thus, these elements are capable of both insulating or repressing transcription dependent upon regulatory context. We validate our findings in stable transgenicArabidopsis, maize, and rice plants. The short elements identified here should be useful building blocks for plant biotechnology efforts.
Tetrapod species-area relationships across the Cretaceous-Paleogene mass extinction
Authors: Close Roger Adam , Reijenga Bouwe Rutger
Published: September 2024

Mass extinctions are rare but catastrophic events that profoundly disrupt biodiversity. Widelyaccepted consequences of mass extinctions, such as biodiversity loss and the appearance of temporary ‘disaster taxa,’ imply that nested species-area relationships (SARs, or how biodiversity scales with area) should change dramatically across these events: specifically, both the slope (reflecting the rate of accumulation of new species with increasing area) and intercept (reflecting the density of species at local scales) of the power-law relationship should decrease. However, these hypotheses have not been tested, and the contribution of variation in the SAR to diversity dynamics in deep time has been neglected. We use fossil data to quantify nested SARs in North American terrestrial tetrapods through the Cretaceous-Paleogene (K/Pg) mass extinction (Campanian–Ypresian). SARs vary substantially through time and among groups. In the pre-extinction interval (Maastrichtian), unusually shallow SAR slopes (indicating low beta diversity or provinciality) drive low total regional diversity in dinosaurs, mammals and other tetrapods. In the immediate post-extinction interval (Danian), the explosive diversification of mammals drove high regional diversity via a large increase in SAR slope (indicating higher beta diversity or provinciality), and only a limited increase in SAR intercept (suggesting limited diversity change at small scales). This contradicts the expectation that post-extinction biotas should be regionally homogenized by the spread of disaster taxa and impoverished by diversity loss. This early post-extinction increase in SAR slope was followed in the Thanetian–Selandian (∼4.4. myr later) by increases in the intercept, indicating that diversity dynamics at local and regional scales did not change in synchrony. These results demonstrate the importance of SARs for understanding deep-time diversity dynamics, particularly the spatial dynamics of recovery from mass extinctions.
Prediction of protein biophysical traits from limited data: a case study on nanobody thermostability through NanoMelt
Authors: Ramon Aubin , Ni Mingyang , Predeina Olga , Gaffey Rebecca , Kunz Patrick , Onuoha Shimobi , Sormanni Pietro
Published: September 2024

1

In-silico prediction of protein biophysical traits is often hindered by the limited availability of experimental data and their heterogeneity. Training on limited data can lead to overfitting and poor generalisability to sequences distant from those in the training set. Additionally, inadequate use of scarce and disparate data can introduce biases during evaluation, leading to unreliable model performances being reported. Here, we present a comprehensive study exploring various approaches for protein fitness prediction from limited data, leveraging pre-trained embeddings, repeated stratified nested cross-validation, and ensemble learning to ensure an unbiased assessment of the performances. We applied our framework to introduce NanoMelt, a predictor of nanobody thermostability trained with a dataset of 640 measurements of apparent melting temperature, obtained by integrating data from the literature with 129 new measurements from this study. We find that an ensemble model stacking multiple regression using diverse sequence embeddings achieves state-of-the-art accuracy in predicting nanobody thermostability. We further demonstrate NanoMelt’s potential to streamline nanobody development by guiding the selection of highly stable nanobodies. We make the curated dataset of nanobody thermostability freely available and NanoMelt accessible as a downloadable software and webserver.

2

Significance Statement

Rapidly predicting protein biophysical traits with accuracy is a key goal in protein engineering, yet efforts to develop reliable predictors are often hindered by limited and disparate experimental measurements. We introduce a framework to predict biophysical traits using few training data, leveraging diverse machine learning approaches via a semi-supervised framework combined with ensemble learning. We applied this framework to develop NanoMelt, a tool to predict nanobody thermostability trained on a new dataset of apparent melting temperatures. Nanobodies are increasingly important in research and therapeutics due to their ease of production and small size, which allows deeper tissue penetration and seamless combination into multi-specific compounds. NanoMelt outperforms available methods for protein thermostability prediction and can streamline nanobody development by guiding the design and selection of highly stable nanobodies during discovery and optimization campaigns.
Enhancing Biomarker-Based Oncology Trial Matching Using Large Language Models
Authors: Khoury Nour Al , Shaik Maqsood , Wurmus Ricardo , Akalin Altuna
Published: September 2024

Clinical trials are an essential component of drug development for new cancer treatments, yet the information required to determine a patient’s eligibility for enrollment is scattered in large amounts of unstructured text. Genomic biomarkers are especially important in precision medicine and targeted therapies, making them essential for matching patients to appropriate trials. Large language models (LLMs) offer a promising solution for extracting this information from clinical trial data, aiding both physicians and patients in identifying suitable matches. In this study, we explore various LLM strategies for extracting genetic biomarkers from oncology trials to improve patient enrollment rates. Our results show that open-source language models, when applied out-of-the-box, effectively capture complex logical expressions and structure genomic biomarkers in disjunctive normal form, outperforming closed-source models such as GPT-4 and GPT-3.5-Turbo. Furthermore, fine-tuning these open-source models with additional data significantly enhances their performance.
M2 Macrophage Exosomes Reverse Cardiac Functional Decline in Mice with Diet-Induced Myocardial Infarction by Suppressing Type 1 Interferon Signaling in Myeloid Cells
Authors: Ng Martin , Gao Alex S. , Phu Tuan Anh , Vu Ngan K. , Raffai Robert L.
Published: September 2024

Effective treatment strategies to alleviate heart failure that develops as a consequence of myocardial infarction (MI) remain an unmet need in cardiovascular medicine. In this study, we uncovered that exosomes produced by human THP-1 macrophages cultured with the cytokine IL-4 (THP1-IL4-exo), reverse cardiac functional decline in mice that develop MI as a consequence of diet-induced occlusive coronary atherosclerosis. Therapeutic benefits of THP1-IL4-exo stem from their ability to reprogram circulating Ly-6Chimonocytes into an M2-like phenotype and suppress Type 1 Interferon signaling in myeloid cells within the bone marrow, the circulation, and cardiac tissue. Collectively, these benefits suppress myelopoiesis, myeloid cell recruitment to cardiac tissue, and preserve populations of resident cardiac macrophages that together mitigate cardiac inflammation, adverse ventricular remodeling, and heart failure. Our findings introduce THP1-IL4-exo, one form of M2-macrophage exosomes, as novel therapeutics to preserve cardiac function subsequent to MI.
Increased Inflammation as well as Decreased Endoplasmic Reticulum Stress and Translation Differentiate Pancreatic Islets of Pre-symptomatic Stage 1 Type 1 Diabetes and Non-diabetic Cases
Authors: Swensen Adam C. , Piehowski Paul D. , Chen Jing , Chan X’avia Y. , Kelly Shane S. , Petyuk Vladislav A. , Moore Ronald J. , Nasif Lith , Butterworth Elizabeth A. , Atkinson Mark A. , Kulkarni Rohit N. , Campbell-Thompson Martha , Mathews Clayton E. , Qian Wei-Jun
Published: September 2024

Aims/hypothesis

Progression to type 1 diabetes (T1D) is associated with genetic factors, the presence of autoantibodies, and a decline in β cell insulin secretion in response to glucose. Very little is known regarding the molecular changes that occur in human insulin-secreting β-cells prior to the onset of T1D. Herein, we applied an unbiased proteomics approach to identify changes in proteins and potential mechanisms of islet dysfunction in islet autoantibody-positive organ donors with pre-symptomatic stage 1 T1D (HbA1c ≤ 6). We aimed to identify pathways in islets that are indicative of β-cell dysfunction.

Methods

Multiple islet sections were collected through laser microdissection of frozen pancreatic tissues of organ donors positive for islet autoantibodies (AAb+, n=5), compared to age/sex-matched nondiabetic controls (ND, n=5) obtained from the Network for Pancreatic Organ donors with Diabetes (nPOD). Islet sections were subjected to mass spectrometry-based proteomics and analyzed with label-free quantification followed by pathway and functional annotations.

Results

Analyses resulted in ∼4,500 proteins identified with low false discovery rate (FDR) <1%, with 2,165 proteins reliably quantified in every islet sample. We observed large inter-donor variations that presented a challenge for statistical analysis of proteome changes between donor groups. We therefore focused on the three multiple AAb+ cases (mAAb+) with high genetic risk and their three matched controls for a final statistical analysis. Approximately 10% of the proteins (n=202) were significantly different between mAAb+ cases versus ND. The significant alterations clustered around major functions for upregulation in the immune response and glycolysis, and downregulation in endoplasmic reticulum (ER) stress response as well as protein translation and synthesis. The observed proteome changes were further supported by several independent published datasets, including proteomics dataset fromin vitroproinflammatory cytokine-treated human islets and single cell RNA-seq data sets from AAb+ cases.

Conclusion/interpretation

In-situ human islet proteome alterations at the stage 1 of AAb+ T1D centered around several major functional categories, including an expected increase in immune response genes (elevated antigen presentation / HLA), with decreases in protein synthesis and ER stress response, as well as compensatory metabolic response. The dataset serves as a proteomics resource for future studies on β cell changes during T1D progression and pathogenesis.
A GeneralizedAddermechanism for Cell Size Homeostasis: Implications for Stochastic Dynamics of Clonal Proliferation
Authors: Nieto César , Vargas-García César Augusto , Singh Abhyudai
Published: September 2024

Measurements of cell size dynamics have revealed phenomeno-logical principles by which individual cells control their size across diverse organisms. One of the emerging paradigms of cell size homeostasis is theadder, where the cell cycle duration is established such that the cell size increase from birth to division is independent of the newborn cell size. We provide a mechanistic formulation of theadderconsidering that cell size follows anyarbitrary non-exponential growth law. Our results show that the main requirement to obtain anadderregardless of the growth law (the time derivative of cell size) is that cell cycle regulators are produced at a rate proportional to the growth law and cell division is triggered when these molecules reach a prescribed threshold level. Among the implications of this generalized adder, we investigate fluctuations in the proliferation of single-cell derived colonies. Considering exponential cell size growth, random fluctuations in clonal size show a transient increase and then eventually decay to zero over time (i.e., clonal populations become asymptotically more similar). In contrast, several forms of non-exponential cell size dynamics (withadder-basedcell size control) yield qualitatively different results: clonal size fluctuations monotonically increase over time reaching a non-zero value. These results characterize the interplay between cell size homeostasis at the single-cell level and clonal proliferation at the population level, explaining the broad fluctuations in clonal sizes seen in barcoded human cell lines.
Molecular computation at equilibrium via programmable entropy
Authors: Wang Boya , Chalk Cameron , Doty David , Soloveichik David
Published: September 2024

Synthetic molecular information processing is typically designed through programming kinetic pathways, so that molecules bind, unbind, or incur conformational changes in some desired order. In DNA nanotechnology, this paradigm is exemplified by strand displacement cascades that control signal transmission through toehold sequestering, and algorithmic tile assembly where correct computation emerges from enforcing ordered growth. In contrast, thermodynamic programming focuses solely on the desired end-state rather than the path, often allowing simpler reasoning and requiring fewer parameters, as well as avoiding energetically-favored, yet undesired, error states that often frustrate kinetic approaches. Here we demonstrate a thermodynamics-first paradigm based on the Thermodynamic Binding Networks (TBN) model, where the system’s equilibrium is determined by two simple factors: maximizing the number of complementary domain-level bonds while favoring configurations with more separate molecular complexes. We first validate the model by quantitatively measuring the free energy benefit of configurations with more separate complexes but identical binding, confirming the entropic driving force central to the TBN abstraction. We then construct signal propagation circuits including fan-in and fan-out, seeded-assembly systems that perform Boolean logic computation, and systems for synthesis of concatemers of size quadratic in that of the substrates (by computing their least common multiple). Realizing these TBN constructions required addressing experimental challenges including ensuring geometric feasibility of desired complexes through systematic domain ordering and compensating for multi-arm junction penalties. Our work may enable new ways to engineer complex molecular behaviors and help inform the understanding of the computational power of kinetics versus thermodynamics for molecular systems.
ECSfinder: Optimized prediction of evolutionarily conserved RNA secondary structures from genome sequences
Authors: Gaonac’h-Lovejoy Vanda , Mattick John S. , Sauvageau Martin , Smith Martin A.
Published: September 2024

Accurate prediction of RNA secondary structures is essential for understanding the evolutionary conservation and functional roles of long noncoding RNAs (lncRNAs) across diverse species. In this study, we benchmarked two leading tools for predicting evolutionarily conserved RNA secondary structures (ECSs)—SISSIzandR-scape— using two distinct experimental frameworks: one focusing on well-characterized mitochondrial RNA structures and the other on experimentally validated Rfam structures embedded within simulated genome alignments. While both tools performed comparably overall, each displayed subtle preferences in detecting ECSs. To address these limitations, we evaluated two interpretable machine learning approaches that integrate the strengths of both methods. By balancing thermodynamic stability features fromRNALalifoldandSISSIzwith robust covariation metrics fromR-scape, a random forest classifier significantly outperformed both conventional tools. This classifier was implemented inECSfinder, a new tool that provides a robust, interpretable solution for genome-wide identification of conserved RNA structures, offering valuable insights into lncRNA function and evolutionary conservation.ECSfinderis designed for large-scale comparative genomics applications and promises to facilitate the discovery of novel functional RNA elements.
LiverMap pipeline for 3D imaging of human liver reveals volumetric spatial dysregulation of cirrhotic vasculobiliary architecture
Authors: Fabyan Wesley B. , Fortin Chelsea L. , Kenerson Heidi L. , Simmonds Susana P. , Liu Jonathan T. C. , Yeh Matthew M. , Carr Rotonya M. , Yeung Raymond S. W. , Stevens Kelly R.
Published: September 2024

The liver contains an intricate microstructure that is critical for liver function. Architectural disruption of this spatial structure is pathologic. Unfortunately, 2D histopathology – the gold standard for pathological understanding of many liver diseases – can misrepresent or leave gaps in our understanding of complex 3D structural features. Here, we utilized immunostaining, tissue clearing, microscopy, and computational software to create 3D multilobular reconstructions of both non-fibrotic and cirrhotic human liver tissue. We found that spatial architecture in human cirrhotic liver samples with varying etiologies had sinusoid zonation dysregulation, reduction in glutamine synthetase-expressing pericentral hepatocytes, regression of central vein networks, disruption of hepatic arterial networks, and fragmentation of biliary networks, which together suggest a pro-portalization/decentralization phenotype in cirrhotic tissue. Further implementation of 3D pathological analyses may provide a deeper understanding of cirrhotic pathobiology and inspire novel treatments for liver disease.
Plant responses to wheat curl mites and wheat streak mosaic virus (WSMV): first evidence of virus tolerance in wheat
Authors: Pingault Lise , Albrecht Tessa , Broders Kirk , Rushton Jennifer , Louis Joe , Nachappa Punya , Nalam Vamsi
Published: September 2024

Wheat curl mites (WCM) are arthropod pests that pose significant threats to wheat crops by causing direct damage by feeding, and transmitting viruses such as Wheat Streak Mosaic Virus (WSMV), Triticum Mosaic Virus (TriMV), and High Plains Wheat Mosaic Virus (HPWMoV), leading to substantial losses in wheat, barley, oats, and rye. Over three years of field screening, we found that the cultivar Hatcher consistently produced higher yields under high WSMV disease pressure, outperforming Mace and TAM112, which carry theWsm1gene and a QTL for curl mite resistance, respectively, indicating tolerance. To investigate the mechanisms underlying the tolerance phenotype in Hatcher, we compared its response to WCM and WSMV infection with a susceptible genotype, CO15D173R. Transcriptomic analysis revealed a nuanced interplay between plant defense and growth in Hatcher, with upregulation of genes related to jasmonic acid (JA), salicylic acid (SA), and abscisic acid (ABA) pathways, indicating a coordinated defense response. The activation of lignin biosynthesis genes points to a potential role of cell wall strengthening in deterring WCM and WSMV. Additionally, the regulation of genes involved in growth-related hormonal pathways such as gibberellic acid (GA), and brassinosteroids (BR) highlights Hatcher’s ability to maintain growth disease pressure. Our findings provide insight into the intricate network of phytohormones, growth-defense trade-offs, and cell wall modifications contributing to Hatcher’s tolerance to WCM and WSMV. This knowledge can inform the development of tolerant wheat varieties and enhance integrated pest management strategies, ultimately safeguarding wheat production.
Hypoxia-mediated fine-tuning of the TLR7/9-triggered human PDC-derived IFN-α response is mediated by combined cellular and soluble IFN-regulators
Authors: Lenkewitz Alexander , Ben Brahim Ibtissem , Herrmann Felix , Islam Noor-A-Kasida , Ticha Olga , Bekeredjian-Ding Isabelle
Published: September 2024

Hypoxia occurs in settings where stringent control of inflammation is mandatory to avoid tissue damage. Here, we analyzed how hypoxia-induced mediators alter recognition of pathogen-derived molecular patterns (PAMPs). The most relevant finding was that hypoxia selectively targets IFN-α induced upon stimulation with nucleic acid-sensing Toll-like receptors (TLR7/8 and -9) in PBMC. Notably, IFN-α secretion was reduced, but IFN-α producing capacity preserved. Corroborating these findings IFN-dependent cytokines IP-10 and IL-12 p70 were reduced under hypoxia, while other cytokines such as TNF remained unaffected. A role for hypoxia-inducible factors (HIF) was confirmed with prolyl hydroxylase (PHD) inhibitors CoCl2and DMOG. Plasmacytoid dendritic cells (PDC) were identified as the major source of IFN-α and experiments in B cell/PDC and monocyte/PDC cocultures indicated that T cells are not required and both lymphoid and myeloid cells equally support inhibition of IFN-α. Moreover, supernatants from unstimulated PBMC generated under hypoxic conditions were not suppressive but further experiments indicated that hypoxia-specific release of IFN-α regulators lactate and TGF-β can contribute to the IFN-α blockage and might synergize with PGE2. However, each one of these factors alone is insufficient to block IFN-α secretion. These findings suggest that control of the PDC response occurs via combined soluble factors and cell contact-dependent mechanisms, thereby enabling a fine-tuning of the anti-microbial immune response in hypoxia.
Generative AI for Qualitative Analysis in a Maternal Health Study: Coding In-depth Interviews using Large Language Models (LLMs)
Authors: Qiao Shan , Fang Xingyu , Garrett Camryn , Zhang Ran , Li Xiaoming , Kang Yuhao
Published: September 2024

Study Objectives

In-depth interviews are one of the most widely used approaches for qualitative studies in public health. The coding of transcripts is a critical step for information extraction and preliminary analysis. However, manual coding is often labor-intensive and time-consuming. The emergence of generative artificial intelligence (GenAI), supported by Large Language Models (LLMs), presents new opportunities to understand human languages, which may significantly facilitate the coding process. This study aims to build a computational coding framework that uses GenAI to automatically detect and extract themes from in-depth interview transcripts.

Methods

We conducted an experiment using transcripts of in-depth interviews with maternity care providers in South Carolina. We leveraged ChatGPT to perform two tasks automatically: (1) deductive coding, which involves applying a predefined set of codes to dialogues; and (2) inductive coding, which can generate codes from dialogues without any preconceptions or assumptions. We fine-tuned ChatGPT to understand the content of the interview transcripts, enabling it to detect and summarize codes. We then evaluated the performance of the proposed approach by comparing the codes generated by ChatGPT with those generated manually by human coders, involving human-in-the-loop evaluation.

Results

The results demonstrated the potential of GenAI in detecting and summarizing codes from in-depth interview transcripts. ChatGPT could be utilized for both deductive and inductive coding processes. The overall accuracy of GenAI is higher than 80% and the codes it generated showed high positive associations with those generated manually. More impressively, GenAI reduced the time required for coding by 81%, demonstrating its efficiency compared to traditional methods.

Discussion

GenAI models like ChatGPT show high generalizability, scalability and efficiency in handling large datasets, and are proficient in multi-level semantic structure identification. They demonstrate promising results in qualitative coding, making it a valuable tool for supporting people in public health research. However, challenges such as inaccuracy, systematic biases, and privacy concerns must be addressed when using them in practice. GenAI-based coding results should be handled with caution and reviewed by human coders to ensure accuracy and reliability.
A Multi-City COVID-19 Categorical Forecasting Model Utilizing Wastewater-Based Epidemiology
Authors: Rankin Naomi , Saiyed Samee , Du Hongru , Gardner Lauren
Published: September 2024

The COVID-19 pandemic highlighted shortcomings in forecasting models, such as unreliable inputs/outputs and poor performance at critical points. As COVID-19 remains a threat, it is imperative to improve current forecasting approaches by incorporating reliable data and alternative forecasting targets to better inform decision-makers.

Wastewater-based epidemiology (WBE) has emerged as a viable method to track COVID-19 transmission, offering a more reliable metric than reported cases for forecasting critical outcomes like hospitalizations. Recognizing the natural alignment of wastewater systems with city structures, ideal for leveraging WBE data, this study introduces a multi-city, wastewater-based forecasting model to categorically predict COVID-19 hospitalizations.

Using hospitalization and COVID-19 wastewater data for six US cities, accompanied by other epidemiological variables, we develop a Generalized Additive Model (GAM) to generate two categorization types. The Hospitalization Capacity Risk Categorization (HCR) predicts the burden on the healthcare system based on the number of available hospital beds in a city. The Hospitalization Rate Trend (HRT) Categorization predicts the trajectory of this burden based on the growth rate of COVID-19 hospitalizations. Using these categorical thresholds, we create probabilistic forecasts to retrospectively predict the risk and trend category of six cities over a 20-month period for 1, 2, and 3 week forecasting windows.

We also propose a new methodology to measure forecasting model performance at change points, or time periods where sudden changes in outbreak dynamics occurred. We also explore the influence of wastewater as a predictor for hospitalizations, showing its inclusion positively impacts the model’s performance. With this categorical forecasting study, we are able to predict hospital capacity risk and disease trends in a novel and useful way, giving city decision-makers a new tool to predict COVID-19 hospitalizations.
Metabolic growth coupling strategies for in vivo enzyme selection systems
Authors: Alter Tobias Benedikt , Pieters Pascal A. , Lloyd Colton J. , Feist Adam M. , Ozdemir Emre , Palsson Bernhard O. , Zielinski Daniel Craig
Published: September 2024

Whole-cell biocatalysis facilitates the production of a wide range of industrially and pharmaceutically relevant molecules from sustainable feedstocks such as plastic wastes, carbon dioxide, lignocellulose, or plant-based sugar sources. The identification and use of efficient enzymes in the applied biocatalyst is key to establishing economically feasible production processes. The generation and selection of favorable enzyme variants in adaptive laboratory evolution experiments using growth as a selection criterion is facilitated by tightly coupling enzyme catalytic activity to microbial metabolic activity. Here, we present a computational workflow to design strains that have a severe, growth-limiting metabolic chokepoint through a shared class of enzymes. The resulting chassis cell, termed enzyme selection system (ESS), is a platform for growth-coupling any enzyme from the respective enzyme class, thus offering cross-pathway application for enzyme engineering purposes. By applying the constraint-based modeling workflow, a publicly accessible database of 25,505 potential and experimentally tractable ESS designs was built for Escherichia coli and a broad range of production pathways with biotechnological relevance. Model-based analysis of the generated design database reveals a general design principle that the target enzyme activity is linked to overall microbial metabolic activity, not just the synthesis of one biomass precursor. Furthermore, the use of currently available genome-reduced strains or one preeminent carbon source does not significantly enable or improve growth-coupling of target enzymes. Most importantly, observed trade-offs between the predicted viability of ESSs, the design-inflicted metabolic perturbations, and the coupling strength suggest that a suboptimal coupling has benefits regarding the experimental implementation of ESSs and growth-coupling in general. Overall, the computed design database, which is accessible through https://biosustain.github.io/ESS-Designs/, and its analysis lay the foundation for generating valuable in vivo ESSs for a range of biotechnological applications.
Shade Inhibits Cambial Activity inPopulusStems by the SPL16/SPL23-Mediated Cytokinin Pathway
Authors: Wei Hongbin , Xiao Xingyue , Deng Jiao , Li Yi , Luo Mengting , Zhang Chengshan , Xu Jinyi , Luo Keming
Published: September 2024

Trees in natural forests or plantations often encounter neighbor proximity signal that negatively impacts wood production. However, the molecular basis underlying shade-regulation of vascular cambial activity during stem radial growth remains unknown in woody species. Here, we revealed that high stand density and simulated shade (low R/FR ratio) suppress the division and differentiation of cambial cells in poplar stems. A genome-wide screen forPopulus SQUAMOSA PROMOTER BINDING PROTEIN-LIKE(SPL) genes identified thatSPL16andSPL23are preferentially expressed in the phloem and cambium, being downregulated by simulated shade. Knocking outSPL16/23impaired cambial activity, whereas phloem-specific overexpression ofSPL16stimulated cambial proliferation and mitigated the shade-inhibition of cambial activity. Additionally, shade decreased bioactive cytokinin (CK) levels by suppressing the expression of CK biosynthesis genesIPT5a,IPT5bandLOG1bin poplar stems. Molecular and genetic studies reveled that SPL16/23 directly activateIPT5s/LOG1bexpression to promote CK biosynthesis and cambial activity. Moreover, elevated miR156 expression in shade-treated stems regulatedSPL16/23at the post-transcriptional level, mediating shade’s effects on cambial activity. Collectively, our findings unravel that the miR156-SPL16/23-IPT5/LOG1-cytokinin pathway operates in the shade-mediated inhibition of cambial activity, providing potential targets for the genetic improvement of shade-tolerant trees.
A Qualitative Study of People with Alzheimer’s Disease in a Memory Clinic Considering Lecanemab Treatment
Authors: Parks Anna L , Thacker Ayush , Dohan Daniel , Ramirez Gomez Liliana A , Ritchie Christine S , Paladino Joanna , Shah Sachin J
Published: September 2024

Background

People with Alzheimer’s disease (AD) now have access to disease-modifying treatment with anti-amyloid monoclonal antibodies (mAbs). Their perception of risks and benefits and approach to treatment decisions remain unknown.

Objective

We aimed to understand how people with AD weigh the benefits and costs of anti-amyloid mAbs and incorporate these into decisions about treatment.

Methods

We conducted semi-structured interviews with people with biomarker- or imaging-confirmed AD and mild or moderate cognitive impairment who were seen at memory care clinics and discussed lecanemab with a clinician. Interviews were recorded, transcribed, and deidentified. Thematic analysis identified themes and subthemes.

Results

Among 22 participants (mean age 70, 8 [36%] women, 22 [100%] White), analysis revealed 3 major themes and associated subthemes: 1) People with AD sought and obtained information from different sources—advocacy organizations, the Internet, and clinicians; 2) Hopes, expected benefits, and the existential threat of dementia drove willingness and readiness to start lecanemab; 3) Individual traits, family factors, and degree of trust in expertise influenced how people balanced risks and benefits. Some would accept treatment at any cost; others carefully weighed risks and burdens, but were motivated by supportive families, insurance coverage, and trust in expertise; for a few, costs decidedly outweighed their personal benefits. People with AD desired more individualized information and to hear more from patients who took the medication.

Conclusion

Results from this first qualitative study of people with AD considering treatment with anti-amyloid mAbs can inform clinician, health system and policy efforts to individualize decisions.
Identifying individuals at risk for surgical supravalvar aortic stenosis by polygenic risk score with graded phenotyping
Authors: Liu D. , Mervis C.B. , Levin M.D. , Biamino E. , Bedeschi M.F. , Digilio M.C. , Squeo G.M. , Villa R. , Osgood S. , Freeman J.A. , Raja N. , Merla G. , Roberts A.E. , Morris C.A. , Osborne L.R. , Kozel B.A.
Published: September 2024

In a previous pathway-based, extreme phenotype study, we identified 1064 variants associated with supravalvar aortic stenosis (SVAS) severity in people with Williams syndrome (WS) and either no SVAS or surgical SVAS. Here, we use those variants to develop and test polygenic risk scores (PRS). We used the clumping and thresholding (CT) approach on the full 1064 variants and a 427-variant subset that was part of 13 biologically relevant pathways identified in the previous study. We also used a lasso approach on the full set. We were able to achieve an area under the curve (AUC) of >0.99 for the two CT PRS methods, using only 622 and 320 variants respectively when 2/3 of the initial 217 participants data were used for training and 1/3 for testing. The lasso performed less well. We then evaluated the performance of those PRS variant sets on an additional group of 138 patients with WS with intermediate severity SVAS and found a misclassification rate of <10% between the surgical and intermediate groups, suggesting potential for clinical utility of the score.
Exome wide association study for blood lipids in 1,158,017 individuals from diverse populations
Authors: Koyama Satoshi , Yu Zhi , Choi Seung Hoan , Jurgens Sean J. , Selvaraj Margaret Sunitha , Klarin Derek , Huffman Jennifer E. , Clarke Shoa L. , Trinh Michael N. , Ravi Akshaya , Dron Jacqueline S. , Spinks Catherine , Surakka Ida , Bhatnagar Aarushi , Lannery Kim , Hornsby Whitney , Damrauer Scott M. , Chang Kyong-Mi , Lynch Julie A , Assimes Themistocles L. , Tsao Philip S. , Rader Daniel J. , Cho Kelly , Peloso Gina M. , Ellinor Patrick T. , Sun Yan V. , Wilson Peter WF. , Program Million Veteran , Natarajan Pradeep
Published: September 2024

Rare coding alleles play crucial roles in the molecular diagnosis of genetic diseases. However, the systemic identification of these alleles has been challenging due to their scarcity in the general population. Here, we discovered and characterized rare coding alleles contributing to genetic dyslipidemia, a principal risk for coronary artery disease, among over a million individuals combining three large contemporary genetic datasets (the Million Veteran Program, n = 634,535, UK Biobank, n = 431,178, and the All of Us Research Program, n = 92,304) totaling 1,158,017 multi-ancestral individuals. Unlike previous rare variant studies in lipids, this study included 238,243 individuals (20.6%) from non-European-like populations.

Testing 2,997,401 rare coding variants from diverse backgrounds, we identified 800 exome-wide significant associations across 209 genes including 176 predicted loss of function and 624 missense variants. Among these exome-wide associations, 130 associations were driven by non-European-like populations. Associated alleles are highly enriched in functional variant classes, showed significant additive and recessive associations, exhibited similar effects across populations, and resolved pathogenicity for variants enriched in African or South-Asian populations. Furthermore, we identified 5 lipid-related genes associated with coronary artery disease(RORC, CFAP65, GTF2E2, PLCB3, and ZNF117). Among them,RORCis a potentially novel therapeutic target through the down regulation of LDLC by its silencing.

This study provides resources and insights for understanding causal mechanisms, quantifying the expressivity of rare coding alleles, and identifying novel drug targets across diverse populations.
Human papillomavirus vaccination at the national level in Tunisia: a cost-effectiveness analysis using a comparative modeling study
Authors: Laraj Oumaima , Benzina Beya , Gzara Ahlem , Kebir Amira , Abbas Kaja , Miled Slimane Ben
Published: September 2024

Cervical cancer is one of the most prevalent cancer diseases in women caused by persistent infection with one of 13 sexually transmitted high-risk human papillomavirus (HPV) types. Vaccination can significantly reduce the prevalence of this burden in low-middle income countries. However, HPV vaccination is not included in the Tunisian immunization program. Since the economic evaluation of HPV vaccines is crucial to inform public-health decisions, the World Health Organization (WHO) recommends that a cost-effectiveness analysis of HPV vaccination is conducted before nationwide introduction. This study aimed to conduct a cost-effectiveness analysis of incorporating different HPV vaccines into the national immunisation schedule in Tunisia. The potential health and economic impacts of human papillomavirus (HPV) vaccination were evaluated through comparative modelling analysis using two published static models (UNIVAC and Papillomavirus Rapid Interface for Modelling and Economics (PRIME)). Academic literature and anecdotal evidence were included on the demographic variables, cervical cancer incidence and mortality, treatment costs, vaccine delivery costs and other model parameters.

The cost of vaccination, treatment costs saved, net costs, cases and deaths averted, life years saved, disability-adjusted life years (DALYs) prevented, and incremental cost-effectiveness ratios were predicted and reported as primary outcomes. The incremental cost-effectiveness ratios (ICERs) were estimated per disability-adjusted life years (DALYs) averted using the cost-effectiveness threshold (CET) defined by the World Health Organisation (WHO). All HPV vaccines were very cost effective (with every disability-adjusted life-year averted costing less than the cost-effectiveness threshold). The analyses were done from a health system and societal perspective. Despite model differences, the PRIME and UNIVAC models yielded similar vaccine-impact estimates.
Exploratory functional and quality of life outcomes with daily consumption of the ketone ester bis-octanoyl (R)-1,3-butanediol in healthy older adults: a randomized, parallel arm, double-blind, placebo-controlled study
Authors: Stubbs Brianna J , Stephens Elizabeth B , Senadheera Chatura , Diaz Stephanie Roa , Peralta Sawyer , Alexander Laura , Silverman-Martin Wendie , Kurtzig Jamie , Fernando B. Ashen , Yurkovich James T , Garcia Thelma Y , Yukawa Michi , Morris Jennifer , Johnson James B , Newman John C
Published: September 2024

Background

Ketone bodies are metabolites produced during fasting or on a ketogenic diet that have pleiotropic effects on the inflammatory and metabolic aging pathways underpinning frailty inin vivomodels. Ketone esters (KEs) are compounds that induce hyperketonemia without dietary changes and that may impact physical and cognitive function in young adults. The functional effects of KEs have not been studied in older adults.

Objectives

Our long-term goal is to examine if KEs modulate aging biology mechanisms and clinical outcomes relevant to frailty in older adults. Here, we report the exploratory functional and quality-of-life outcome measures collected during a 12-week safety and tolerability study of KE (NCT05585762).

Design

Randomized, placebo-controlled, double-blinded, parallel-group, pilot trial of 12-weeks of daily KE ingestion.

Setting

The Clinical Research Unit at the Buck Institute for Research on Aging, California. Participants: Community-dwelling older adults (≥ 65 years), independent in activities of daily living, with no unstable acute medical conditions (n = 30).

Intervention

Subjects were randomly allocated (1:1) to consume 25 g daily of either KE (bis-octanoyl (R)-1,3-butanediol) or a taste, appearance, and calorie-matched placebo (PLA) containing canola oil.

Measurements

Longitudinal change in physical function, cognitive function and quality of life were assessed as exploratory outcomes in n = 23 completers (n = 11 PLA, n = 12 KE). A composite functional outcome to describe the vigor-frailty continuum was calculated. Heart rate and activity was measured throughout the study using digital wearables.

Results

There were no statistically significant longitudinal differences between groups in exploratory functional, activity-based or quality of life outcomes.

Conclusion

Daily ingestion of 25 g of KE did not affect exploratory functional or quality-of-life end points in this pilot cohort of healthy older adults. Future work will address these endpoints as primary and secondary outcomes in a larger trial of pre-frail older adults.
Relationship between blood-cerebrospinal fluid barrier integrity, cardiometabolic and inflammatory factors in schizophrenia-spectrum disorders
Authors: Yakimov Vladislav , Jäger Iris , Roell Lukas , Boudriot Emanuel , Meisinger Verena , Campana Mattia , Krčmář Lenka , Halstead Sean , Warren Nicola , Siskind Dan , Maurus Isabel , Hasan Alkomiet , Falkai Peter , Schmitt Andrea , Raabe Florian J. , Keeser Daniel ,   CDP-Working Group, Wagner Elias , Moussiopoulou Joanna
Published: September 2024

Abstract

The blood-cerebrospinal fluid barrier (BCB) builds an integral interface between the central nervous system and the periphery and appears to be impaired in a substantial proportion of individuals with schizophrenia-spectrum disorders (SSD). Even though a disruption of the BCB is associated with higher symptom severity, factors linked to BCB disruption in SSDs have been minimally investigated.

To address this gap, 57 inpatients with SSD underwent cerebrospinal fluid (CSF) and blood analyses as well as comprehensive clinical assessments. In a subgroup of 28 participants structural magnetic resonance imaging (MRI) was performed. We developed a BCB dysfunction score, employing principal component analysis of CSF/serum albumin, CSF/serum IgG ratios and total protein levels in CSF, with higher values indicating stronger abnormalities. We calculated multiple regression models to explore the associations between BCB integrity and cardiometabolic, inflammatory, brain morphometric, and clinical measures respectively.

BCB dysfunction score was negatively associated with high-density lipoprotein cholesterol and positively associated with total cholesterol, low-density lipoprotein cholesterol, and triglycerides. Furthermore, we observed a trend towards a positive association between BCB dysfunction score and treatment resistance that did not survive multiple testing correction. We did not find significant associations between the BCB composite score and any other assessed cardiometabolic, inflammatory or cerebroventricular measures.

These findings suggest that BCB integrity is associated with dyslipidemia in SSD, highlighting the interplay between cardiometabolic risk factors and brain health in SSD. Addressing cardiometabolic health in individuals with SSD might thus have implications beyond physical health, potentially influencing the integrity of the BCB and, consequently, clinical trajectories.
Statin use is associated with reduction in risk ofde novoprimary sclerosing cholangitis among patients with inflammatory bowel disease: A National Database Study
Authors: Kulkarni Chiraag , Gubatan John , Pike C. William , Hui Gavin , Gombar Saurabh , Kramer Jennifer , Jackson Michael L. , Goel Aparna , Vierling John , Cholankeril George , Sinha Sidhartha R.
Published: September 2024

Background and Aims

Primary sclerosing cholangitis (PSC) is a liver disease without medical therapy that significantly increases the risk of malignancies, acute cholangitis and cirrhosis. Approximately 2-7% of patients with inflammatory bowel disease (IBD) develop PSC. No drug has been shown to preventde novoPSC in patients with IBD. Statin use, however, has been associated with increased liver transplant-free survival. Yet, the impact of statins on development of new PSC among those with IBD is unknown.

Methods

We conducted a retrospective cohort study of patients diagnosed with IBD with and without statin exposure using a large, representative national database. Patients were followed from the date of diagnosis of IBD. Patient demographics, co-morbidities, medications, and type of IBD were extracted. Unmatched and propensity score-matched Cox regression analyses were performed.

Results

Our analysis included 33,813 patients with IBD of whom 8,813 were exposed to statins. PSC developed in 181 patients over a median follow-up of 3.7 years. Only nine patients (0.1%) who were exposed to statins developed PSC compared to 173 (0.69%) in the non-exposed population. In propensity score-matched analysis, statin therapy was associated with an 86% lower risk of developing PSC (HR 0.14; 95% CI 0.06-0.33, p<0.001). The findings were consistent when accounting for unmeasured confounders (E-value) in sensitivity analyses, including stratification by age group, duration of statin use, and type of statin.

Conclusion

In a propensity score-matched analysis, statin use was associated with an 86% risk reduction in new PSC diagnosis among patients with known IBD, suggesting a potential benefit as a prophylactic agent. These findings warrant prospective validation.