bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring Integrin α5β1 as a Potential Therapeutic Target for Pulmonary Arterial Hypertension: Insights from Comprehensive Multicenter Preclinical Studies
Authors: Lemay Sarah-Eve , Montesinos Monica S. , Grobs Yann , Yokokawa Tetsuro , Shimauchi Tsukasa , Romanet Charlotte , Sauvaget Mélanie , Breuils-Bonnet Sandra , Bourgeois Alice , Théberge Charlie , Pelletier Andréanne , El Kabbout Reem , Martineau Sandra , Yamamoto Keiko , Ray Adrian S. , Lippa Blaise , Goodwin Bryan , Lin Fu-Yang , Wang Hua , Dowling James E , Lu Min , Qiao Qi , McTeague T. Andrew , Moy Terence I. , Potus François , Provencher Steeve , Boucherat Olivier , Bonnet Sébastien
Published: June 2024

Pulmonary arterial hypertension (PAH) is characterized by obliterative vascular remodeling of the small pulmonary arteries (PA) and progressive increase in pulmonary vascular resistance (PVR) leading to right ventricular (RV) failure. Although several drugs are approved for the treatment of PAH, mortality remains high. Accumulating evidence supports a pathological function of integrins in vessel remodeling, which are gaining renewed interest as drug targets. However, their role in PAH remains largely unexplored. We found that the arginine-glycine-aspartate (RGD)-binding integrin α5β1 is upregulated in PA endothelial cells (PAEC) and PA smooth muscle cells (PASMC) from PAH patients and remodeled PAs from animal models. Blockade of the integrin α5β1 or depletion of the α5 subunit resulted in mitotic defects and inhibition of the pro-proliferative and apoptosis-resistant phenotype of PAH cells. Using a novel small molecule integrin inhibitor and neutralizing antibodies, we demonstrated that α5β1 integrin blockade attenuates pulmonary vascular remodeling and improves hemodynamics and RV function in multiple preclinical models. Our results provide converging evidence to consider α5β1 integrin inhibition as a promising therapy for pulmonary hypertension.

One sentence summary

The α5β1 integrin plays a crucial role in pulmonary vascular remodeling.
Exploring the therapeutic potential of a novel series of imidazothiadiazoles targeting focal adhesion kinase (FAK) for pancreatic cancer treatment: Synthesis, mechanistic Insights and promising antitumor and safety profile
Authors: Carbone Daniela , Pecoraro Camilla , Scianò Fabio , Terrana Francesca , Xu Geng , Bergonzini Cecilia , Roeten Margot S F , Cascioferro Stella , Cirrincione Girolamo , Diana Patrizia , Giovannetti Elisa , Parrino Barbara
Published: June 2024

Focal Adhesion Kinase (FAK) is a non-receptor protein tyrosine kinase that plays a crucial role in various oncogenic processes related to cell adhesion, migration, proliferation, and survival. The strategic targeting of FAK represents a burgeoning approach to address resistant tumors, such as pancreatic ductal adenocarcinoma (PDAC).

Herein, we report a new series of twenty imidazo[2,1-b][1,3,4]thiadiazole derivatives assayed for their antiproliferative activity against the National Cancer Institute (NCI-60) panel and a wide panel of PDAC models. Lead compound10lexhibited effective antiproliferative activity against immortalized (SUIT-2, CAPAN-1, PANC-1, PATU-T, BxPC-3), primary (PDAC-3) and gemcitabine-resistant clone (PANC-1-GR) PDAC cells, eliciting IC50values in the low micromolar range (1.04-3.44 µM), associated with a significant reduction in cell-migration and spheroid shrinkagein vitro. High-throughput kinase arrays revealed a significant inhibition of the FAK signalling network, associated to induction of cell cycle arrest in G2/M phase, suppression of tumor cell invasion and apoptosis induction. The low selectivity index/toxicity prompted studies using PDAC mouse xenografts, demonstrating significant inhibition of tumor growth and safety. In conclusion, compound10ldisplayed antitumor activity and safety in bothin vitroandin vivomodels, emerging as a highly promising lead for the development of FAK inhibitors in PDAC.
Horizon: CNV interpretation through rapid automated ACMG-aligned pathogenicity analysis
Authors: Eldesouky Mariam , Shiyas Suhana , Islam Amirul , Hasan Md. Nahid , Hossain Md. Tanvir , Akter Hosneara , Berdiev Bakhrom K. , Kuebler Wolfgang M. , Rahman Proton , Woodbury-Smith Marc , Hasan Syed M. , Nassir Nasna , Uddin Mohammed
Published: June 2024

Purpose

Our study assesses the Horizon model, a novel CNV classification tool developed in line with American College of Medical Genetics (ACMG) guidelines, to enhance the classification of pathogenicity in CNVs.

Methods

Horizon utilizes a ranking-based algorithm, incorporating multiple proprietary databases and variant inheritance models as per ACMG standards. The model’s effectiveness was verified through Area Under the Curve (AUC) analyses on three datasets comprising 696 pathogenic inherited orde novovariants, as classified by clinical geneticists and several established tools.

Results

Horizon achieved an AUC of 0.97 in the discovery cohort, demonstrating high accuracy in CNV interpretation and proficiency in predicting pathogenicity. We observed an AUC of 0.87 in thede novovariant cohort and an overall AUC of 0.94 across all cohorts, surpassing tools like ClassifyCNV and AnnotSV. It showed particular effectiveness in interpreting duplication CNVs and the highest performance for CNVs sized 3-5 Mb.

Conclusion

The Horizon model offers robust and accurate CNV interpretation, outperforming existing tools and aligning closely with clinical evaluations. Its comprehensive approach, integrating a range of genomic features and following ACMG guidelines, makes it a crucial tool in the genomic interpretation landscape, facilitating the rapid and accurate diagnosis of genetic disorders.
A Mimicry-Based Strategy Between Human and Commensal Antigens for the Development of a New Family of Immune Therapies for Cancer
Authors: Talpin Alice , Maia Ana , Carpier Jean-Marie , Kulakowski Guillaume , Gaal Camille , Strozzi Francesco , Billerey Coline , Aubergeon Lucie , Amable Ludivine , Kervevan Jérôme , Mersceman Tifanny , Garnier Alexandrine , Oliveira Catia Pereira , Calderon Carolina , Bachrouche Diana , Ventujol Chloé , Martinez Jennifer , Bonnet Michaël , Noguerol Julie , Laviolette Karl , Boullerot Laura , Malfroy Marine , Chevalier Grégoire , Adotevi Olivier , Joffre Olivier , Idbaih Ahmed , Vieito Maria , Ghiringhelli François , Stradella Agostina , Tabatabai Ghazaleh , Burger Michael C. , Mildenberger Iris , Herrlinger Ulrich , Reardon David A. , Wick Wolfgang , Gouttefangeas Cécile , Bonny Christophe , Chêne Laurent , Gamelas Magalhaes Joao
Published: June 2024

Peptide vaccines have emerged as a promising strategy for cancer immunotherapy, yet often lack of strong, specific and sustained immune responses against tumor antigens. To achieve a robust immune response, the effective selection of tumour antigens is crucial. While neoantigens trigger potent immune responses, their use suffers from patient specificity and their rarity in low-mutational tumors. Alternatively, the immunogenic potential of tumor-associated antigens (TAAs) is limited by central immune tolerance. Molecular mimicry and T cell cross-reactivity is a proposed mechanism to trigger a robust T cell-mediated antitumor response. Although molecular mimicry between pathogens and tumor antigens has been described, the potential benefits of exploiting this molecular mimicry with commensal bacterial antigens in antitumor immunity have not been thoroughly investigated despite strong evidence that the composition of the human microbiota significantly influences immune competency. Our new approach called OncoMimics™, which uses molecular mimicry between commensal bacterial and tumoral antigens to induce cross-reactive cytotoxic T cells against tumor cells. In preclinical studies, vaccination with OncoMimic™ peptides (OMPs) led to the expansion of CD8+T cells reacting against homologous tumor-associated antigen peptides and elicits cytotoxic activity against tumor cells. OMPs are efficiently recognized by a prevalent T cell population within the peripheral blood mononuclear cells of healthy individuals. An ongoing clinical trial (NCT04116658) using OncoMimics™ in patients with glioblastoma demonstrates early, durable, and cross-reactive tumor antigen CD8+T cell responses with pronounced memory persistence. By overcoming the current vaccine limitations, OncoMimics™ constitutes a promising strategy for enhancing cancer immunity and improving patient outcomes.

Statement of Significance

This study introduces OncoMimics™, a peptide-based immunotherapy leveraging molecular mimicry to induce robust, cross-reactive T cell responses against tumor antigens, showing promising early results in an ongoing glioblastoma clinical trial (NCT04116658)
The Impact of Scene Context on Visual Object Recognition: Comparing Humans, Monkeys, and Computational Models
Authors: Djambazovska Sara , Zafer Anaa , Ramezanpour Hamidreza , Kreiman Gabriel , Kar Kohitij
Published: June 2024

During natural vision, we rarely see objects in isolation but rather embedded in rich and complex contexts. Understanding how the brain recognizes objects in natural scenes by integrating contextual information remains a key challenge. To elucidate neural mechanisms compatible with human visual processing, we need an animal model that behaves similarly to humans, so that inferred neural mechanisms can provide hypotheses relevant to the human brain. Here we assessed whether rhesus macaques could model human context-driven object recognition by quantifying visual object identification abilities across variations in the amount, quality, and congruency of contextual cues. Behavioral metrics revealed strikingly similar context-dependent patterns between humans and monkeys. However, neural responses in the inferior temporal (IT) cortex of monkeys that were never explicitly trained to discriminate objects in context, as well as current artificial neural network models, could only partially explain this cross-species correspondence. The shared behavioral variance unexplained by context-naive neural data or computational models highlights fundamental knowledge gaps. Our findings demonstrate an intriguing alignment of human and monkey visual object processing that defies full explanation by either brain activity in a key visual region or state-of-the-art models.
Multi-omics analysis of longitudinal patient samples reveals the molecular mechanism of AML progression
Authors: Ahmed Nisar , Cavattoni Irene , Villiers William , Cugno Chiara , Deola Sara , Mifsud Borbala
Published: June 2024

Relapse remains a determinant of treatment failure and contributes significantly to mortality in acute myeloid leukemia (AML) patients. Despite efforts to understand AML progression and relapse mechanisms, findings on acquired gene mutations in relapse vary, suggesting inherent genetic heterogeneity and emphasizing the role of epigenetic modifications. We conducted a multi-omics analysis using Omni-C, ATAC-seq, and RNA-seq on longitudinal samples from two adult AML patients at diagnosis and relapse. Herein, we characterized genetic and epigenetic changes in AML progression to elucidate the underlying mechanisms of relapse. Differential interaction analysis showed significant 3D chromatin landscape reorganization between relapse and diagnosis samples. Comparing global open chromatin profiles revealed that relapse samples had significantly fewer accessible chromatin regions than diagnosis samples. In addition, we discovered that relapse-related upregulation was achieved either by forming new active enhancer contacts or by losing interactions with poised enhancers/potential silencers. Altogether, our study highlights the impact of genetic and epigenetic changes on AML progression, underlining the importance of multi-omics approaches in understanding disease relapse mechanisms and guiding potential therapeutic interventions.
Multiple blood feeding bouts in mosquitoes allow for prolonged survival and are predicted to increase viral transmission during drought
Authors: Holmes Christopher J. , Chakraborty Souvik , Ajayi Oluwaseun M. , Uhran Melissa R. , Frigard Ronja , Stacey Crystal L. , Susanto Emily E. , Chen Shyh-Chi , Rasgon Jason L. , DeGennaro Matthew , Xiao Yanyu , Benoit Joshua B.
Published: June 2024

Survival through periods of drought is critical for mosquitoes to reside in semi-arid regions with humans. Dry conditions increase blood feeding propensity in mosquitoes, but it is unknown if dehydration-induced bloodmeals increase feeding beyond what is necessary for reproduction. Following a bloodmeal, prolonged exposure to dry conditions increased secondary blood feeding in mosquitoes by nearly two-fold, and chronic blood feeding allowed mosquitoes to survive up to twenty days without access to water sources. This refeeding did not alter the number of eggs generated, suggesting this refeeding is for hydration and nutrient replenishment. Exposure to desiccating conditions following a bloodmeal resulted in increased activity, decreased sleep levels, and prompted a return of CO2sensing before egg deposition. Increased blood feeding during the vitellogenic stage and higher survival during dry periods are predicted to increase pathogen transmission, allowing for a rapid rebound in mosquito populations when more favorable conditions return. This explains the elevated levels of specific arbovirus cases in association with periodic dry conditions and warrants further consideration as climate change progresses. Overall, these results solidify our understanding of the role of dry periods on mosquito blood feeding and how mosquito dehydration contributes to vectorial capacity and disease transmission dynamics

Significance statement

Bouts of dehydration yield substantial changes to insects’ physiology and behavior. Mosquitoes are exceptionally prone to dehydration due to high water loss rates, but few integrative studies have examined the comprehensive impact of drought conditions on mosquitoes. Here, we demonstrate that dry conditions lead to multiple blood feeding events, allowing mosquitoes to survive dry periods. This repeated blood feeding is associated with higher activity and an early return of attraction to vertebrate hosts. Increased dry season survival and more frequent blood feeding are predicted to yield higher transmission of mosquito-borne viruses. This suggests that a higher prevalence of drought associated with climate change will have varying impacts on mosquito-borne diseases.
Substantial Braking Force of Anterior Tibial Translation Induces the Spontaneous Healing of Murine Anterior Cruciate Ligament
Authors: Saito Riku , Nakayama Kazuki , Usami Yuna , Enomoto Saaya , Nogi Koyo , Kokubun Takanori
Published: June 2024

The anterior cruciate ligament (ACL) has been recognized to have no spontaneous healing capacity after complete rupture due to an intra-articular environment. Meanwhile, our group reported that a completely ruptured ACL could heal spontaneously by restabilizing the femur-tibia joint. Several clinical studies have recently reported that ruptured ACL could heal spontaneously by controlling abnormal joint movement. Appropriate mechanical stimulation on the ruptured ACL promotes spontaneous healing, whereas excessive mechanical stress may inhibit the healing response. Here, we investigated how much braking force to control anterior tibial translation (ATT) affects the healing response of the ACL using two different braking forces for controlled ATT (CATT) models. Male and female C57BL/6 mice were ruptured ACL and randomly classified into Tight-CATT, Loose-CATT, and ACL-Rupture (ACL-R) groups. The Tight-CATT group showed a higher success rate of ACL healing than the Loose-CATT group at 4 and 8 weeks. However, collagen maturation and mechanical properties on healed ACL did not differ between the Tight-CATT and Loose-CATT groups. Our results suggested that tight ATT braking immediately after injury is essential for successfully healing the completely ruptured ACL. On the other hand, tightly ATT in the early healing phase was insufficient to improve mechanical strength and collagen maturation. Therefore, we should control mechanical stress tightly in the early phase of ACL spontaneous healing, and it would be necessary to apply higher mechanical stress in the later phases to achieve greater healing of the ACL.

Study Design

A controlled laboratory study.
Exploring the energetic and conformational properties of the sequence space connecting naturally occurring RNA tetraloop receptor motifs
Authors: Shin John H. , Cuevas Lena M. , Roy Rohit , Bonilla Steve L. , Al-Hashimi Hashim , Greenleaf William J , Herschlag Daniel
Published: June 2024

Folded RNAs contain tertiary contact motifs whose structures and energetics are conserved across different RNAs. The transferable properties of RNA motifs simplify the RNA folding problem, but measuring energetic and conformational properties of many motifs remains a challenge. Here, we use a high-throughput thermodynamic approach to investigate how sequence changes alter the binding properties of naturally-occurring motifs, the GAAA tetraloop • tetraloop receptor (TLR) interactions. We measured the binding energies and conformational preferences of TLR sequences that span mutational pathways from the canonical 11ntR to two other natural TLRs, the IC3R and Vc2R. While the IC3R and Vc2R share highly similar energetic and conformational properties, the landscapes that map the sequence changes for their conversion from the 11ntR to changes in these properties differ dramatically. Differences in the energetic landscapes stem from the mutations needed to convert the 11ntR to the IC3R and Vc2R rather than a difference in the intrinsic energetic architectures of these TLRs. The conformational landscapes feature several non-native TLR variants with conformational preferences that differ from both the initial and final TLRs; these species represent potential branching points along the multidimensional sequence space to sequences with greater fitness in other RNA contexts with alternative conformational preferences. Our high-throughput, quantitative approach reveals the complex nature of sequence-fitness landscapes and leads to models for their molecular origins. Systematic and quantitative molecular approaches provide critical insights into understanding the evolution of natural RNAs as they traverse complex landscapes in response to selective pressures.
A quantitative characterization of the heterogeneous response of glioblastoma U-87 MG cell line to temozolomide
Authors: Dixit Pragyesh , Djafer-Cherif Ilyas , Shah Saumil , Traulsen Arne , Waclaw Bartlomiej
Published: June 2024

Most cancers are genetically and phenotypically heterogeneous. This includes subpopulations of cells with different levels of sensitivity to chemotherapy, which may lead to treatment failure as the more resistant cells can survive drug treatment and continue to proliferate. While the genetic basis of resistance to many drugs is relatively well characterised, non-genetic factors are much less understood. Here we investigate the role of non-genetic, phenotypic heterogeneity in the response of glioblastoma cancer cells to the drug temozolomide (TMZ) often used to treat this type of cancer. Using a combination of live imaging, machine-learning image analysis and agent-based modelling, we show that even if all cells share the same genetic background, individual cells respond differently to TMZ. We quantitatively characterise this response by measuring the doubling time, lifespan, and motility of cells, and determine how these quantities correlate with each other as well as between the mother and daughter cell. We also show that these responses do not correlate with the cellular level of the enzyme MGMT which has been implicated in the response to TMZ.
Genome scale metabolic modelling of human gut microbes to inform rational community design
Authors: Ortiz Juan Pablo Molina , McClure Dale David , Holmes Andrew , Rice Scott Alan , Read Mark Norman , Shanahan Erin Rose
Published: June 2024

Background

The human gut microbiome plays a pivotal role in health and disease, influenced significantly by diet, particularly through the intake of digestion-resistant carbohydrates (DRCs). Emerging evidence underscores the potential of DRC supplementation in modulating the gut microbiome towards health-promoting metabolic outputs, notably through the fermentation of DRCs into short-chain fatty acids (SCFAs) including butyrate. However, the effectiveness of such interventions is hindered by the inherent complexity of microbial communities and the variable functional capacity of microbiomes across individuals. Improved understanding of gut ecology is necessary to move past interventions with transient benefits.

Results

This study leverages genome-scale metabolic models (GEMs) to characterise the metabolic capabilities of 598 stable human gut colonising strains from the AGORA database. We infer the strains’ abilities to utilize dietary carbohydrates of varying complexities and produce metabolites that mediate interactions with other microbes and the host.

Our analysis reveals a stratified functional landscape where prominent bacterial families show predispositions towards primary or secondary degrader roles based on their carbohydrate utilisation capabilities. Further, we identify metabolite production profiles that exceed phylogenetic variation in our sample. These results offer a comprehensive functional mapping of carbohydrate metabolism across a wide array of gut microbes, shedding light on the complex trophic networks underpinning the gut ecosystem.

Supporting DRC-based interventions with rationally designed microbial communities can better guarantee the delivery of the intended health-promoting metabolic outputs. Applying our functional assessment, and principles of reverse ecology and network analysis, we propose a novel framework for the rational design gut microbial communities, where trophic networks are optimised to produce target metabolites from selected DRCs. Our results further suggest that this framework can predict resilient minimal communities, an important trait in the constantly changing human gut nutritional environment.

Conclusion

Our work provides novel insights into gut microbial ecology as well as intervention and consortia design. The identified metabolic capabilities of individual strains inform the rational design of a purpose-based microbial community to optimise butyrate production from inulin degradation. The framework we propose herein sets a foundation for future efforts aimed at the rational design of interventions that target the human gut microbiome to improve health.
Transferability of stream benthic macroinvertebrate distribution models to drought-related conditions
Authors: Medina Madariaga Graciela , Nguyen Hong Hanh , Kiesel Jens , Peters Kristin , Feld Christian K , Jaehnig Sonja C. , Cambas Yusdiel Torres
Published: June 2024

Freshwater ecosystems increasingly face pressures from climate change induced extreme events, like droughts, posing significant threats to biodiversity. While Species Distribution Models (SDMs) serve as vital tools for predicting species responses to environmental shifts, their transferability to novel environmental conditions, especially during and after drought remains poorly understood. In this study, we delve into the transferability of SDMs for freshwater macroinvertebrates from drought-free to drought-influenced conditions. We examine how sensitive the transferability is to traits such as tolerance scores according to their distribution along longitudinal gradients, as well as the used modelling method. We constructed and validated SDMs for freshwater macroinvertebrates in a central German catchment under drought-free conditions using four different algorithms (Generalised linear models; GLM, Spatial Stream Networks; SSN, Random Forests; RF, and Maximum Entropy; MaxEnt). We then projected these models to environmental conditions influenced by drought, and obtained their transferability by computing the difference in accuracy when predicting under drought-free and drought-influenced conditions (AUCgap). Our findings reveal a marked reduction in SDM accuracy under drought conditions, illustrating the challenges of accurately predicting species distributions into novel environmental conditions. Our results show a slightly better transferability when using SSN and RF. In addition, we observed that SDM transferability can be influenced by species tolerance, with sensitive and tolerant species presenting higher AUCgap (i.e. lower transferability). Furthermore, we found that when sensitive species were modelled using SSNs, the AUCgap was reduced. Our study underscores the limitations of SDMs in capturing species responses to drought and advocates for integrating ecologically relevant predictors and modelling methods that account for the stream connectivity to allow for robust predictions. These considerations could enhance the ability of SDMs to effectively estimate the impacts of extreme events on freshwater biodiversity.
Lysophosphatidic acid receptor 1 influences disease severity in a mouse model of multiple sclerosis
Authors: Uemura Nagi , Matsuo Nagisa , Taniyama Kazunao , Nakajima Hiroki , Nagayasu Kazuki , Kaneko Shuji , Chun Jerold , Ueda Hiroshi , Shirakawa Hisashi
Published: June 2024

Multiple sclerosis (MS), a chronic inflammatory disease affecting the central nervous system (CNS), is characterized by demyelination and axonal degeneration. Current treatments, which focus mainly on reducing lymphocyte infiltration into the CNS, are insufficient due to serious side effects and limited effectiveness; thus, identifying drugs with new mechanisms of action is crucial. Lysophosphatidic acid (LPA), a bioactive lipid produced by the enzyme autotaxin, may play a role in MS pathogenesis. Specifically, the LPA1subtype of LPA receptors is linked to release of inflammatory cytokines in the CNS, and to demyelination in the peripheral nervous system. Our study investigated the role of LPA1in a mouse model of MS. Knocking out the LPA1gene in mice with experimental autoimmune encephalomyelitis improved clinical outcomes and reduced demyelination. Additionally, the absence of LPA1reduced activation of Iba1-positive cells. Treatment with AM095, an LPA1antagonist, tended to improve clinical outcomes and reduce levels of inflammatory mediators. These findings indicate that activation of LPA1contributes to MS pathogenesis by promoting microglial activation and infiltration of peripheral immune cells.
The effect of host population genetic diversity on variation in metrics of parasite success
Authors: Paplauskas Sam , Duthie Brad , Tinsley Matthew
Published: June 2024

Conventional wisdom suggests that populations with lower levels of genetic diversity are at a greater risk of the more harmful effects of disease. However, previous attempts to qualify this proposition have focused on measuring the mean, rather than the variability, in metrics of parasite success. Since the ability of host population genetic diversity to limit the spread of disease requires some specificity between hosts and parasites, and the benefits of host population genetic diversity in resistance to infection may depend on the respective parasite population genetic diversity, we propose a diversity-uncertainty model which predicts that the mean and variability in parasite success depend on a combination of host range and parasite population genetic diversity. By re-analyzing a dataset combining 48 studies collected by previous meta-analyses, we show that the effect of host population genetic diversity reduces the mean success of single-host, but not host generalist, parasites. We find evidence for our original hypothesis that the variability of parasite success depends on a combination of host population genetic diversity, parasite population genetic diversity and host range. Together, these results challenge conventional wisdom and have important implications for how genetic diversity can be better managed in host populations.
Nuclear phylogenomics of grasses (Poaceae) supports current classification and reveals repeated reticulation
Authors:   Grass Phylogeny Working Group III, Arthan Watchara , Baker William J. , Barrett Matthew D. , Barrett Russell L. , Bennetzen Jeffrey , Besnard Guillaume , Bianconi Matheus E. , Birch Joanne L. , Catalán Pilar , Chen Wenli , Christenhusz Maarten , Christin Pascal-Antoine , Clark Lynn G. , Columbus J. Travis , Couch Charlotte , Crayn Darren M. , Davidse Gerrit , Dransfield Soejatmi , Dunning Luke T. , Duvall Melvin R. , Ficinski Sarah Z. , Fisher Amanda E. , Fjellheim Siri , Forest Felix , Gillespie Lynn J. , Hackel Jan , Haevermans Thomas , Hodkinson Trevor R. , Huang Chien-Hsun , Huang Weichen , Humphreys Aelys M. , Jobson Richard W. , Kayombo Canisius J. , Kellogg Elizabeth A. , Kimeu John M. , Larridon Isabel , Letsara Rokiman , Li De-Zhu , Liu Jing-Xia , Londoño Ximena , Luke Quentin W.R. , Ma Hong , Macfarlane Terry D. , Maurin Olivier , McKain Michael R. , McLay Todd G.B. , Moreno-Aguilar Maria Fernanda , Murphy Daniel J. , Nanjarisoa Olinirina P. , Onjalalaina Guy E. , Peterson Paul M. , Rakotonasolo Rivontsoa A. , Razanatsoa Jacqueline , Saarela Jeffery M. , Simpson Lalita , Snow Neil W. , Soreng Robert J. , Sosef Marc , Thompson John J.E. , Traiperm Paweena , Verboom G. Anthony , Vorontsova Maria S. , Walsh Neville G. , Washburn Jacob D. , Watcharamongkol Teera , Waycott Michelle , Welker Cassiano A.D. , Xanthos Martin D. , Xia Nianhe , Zhang Lin , Zizka Alexander , Zuloaga Fernando O. , Zuntini Alexandre R.
Published: June 2024

Grasses (Poaceae) comprise around 11,800 species and are central for human livelihoods and terrestrial ecosystems. Knowing their relationships and evolutionary history is key to comparative research and crop breeding. Advances in genome-scale sequencing allow for increased breadth and depth of phylogenomic analyses, making it possible to infer a new reference species tree of the family.

We inferred a comprehensive species tree of grasses by combining new and published sequences for 331 nuclear genes from genome, transcriptome, target enrichment and shotgun data. Our 1,153-tip tree covers 79% of grass genera (including 21 genera sequenced for the first time) and all but two small tribes. We compared it to a 910-tip plastome tree.

The nuclear phylogeny matches that of the plastome at most deep branches, with only a few instances of incongruence. Gene tree–species tree reconciliation suggests that reticulation events occurred repeatedly in the history of grasses.

We provide a robust framework for the grass tree of life to support research on grass evolution, including modes of reticulation, and genetic diversity for sustainable agriculture.
Likelihood-based Finetuning of Protein Language Models for Few-shot Fitness Prediction and Design
Authors: Hawkins-Hooker Alex , Surana Shikha , Kmec Jakub , Bent Oliver , Duckworth Paul
Published: June 2024

Protein language models (PLMs) implicitly learn distributional constraints on protein sequences upheld over the course of evolution. As a consequence, the sequence and mutation-level likeli-hoods of such models form effective zero-shot predictors of mutations. Although various schemes have been proposed for exploiting the distributional knowledge captured by PLMs to enhance supervised fitness prediction and sequence design tasks, a lack of head-to-head comparison across different prediction strategies and different classes of PLM has made it challenging to identify the best-performing methods. Our contribution is to extend previously proposed ranking-based loss functions to develop likelihood scoring functions forfamily-basedandmaskedPLMs. We demonstrate that in the low-data setting the best configurations outperform the current SOTA approach, which is based on frozen embeddings. Furthermore, we propose ensembling strategies that exploit the strong dependence of the mutational distributions learned by PLMs on sequence context, showing that they can be used to guide efficient optimisation strategies over fitness landscapes.
Energetics of the outer retina II: Calculation of a spatio-temporal energy budget in retinal pigment epithelium and photoreceptor cells based on quantification of cellular processes
Authors: Kiel Christina , Prins Stella , Foss Alexander JE , Luthert Philip J
Published: June 2024

The outer retina (OR) is highly energy demanding. Impaired energy metabolism combined with high demands are expected to cause energy insufficiencies that make the OR susceptible to complex blinding diseases such as age-related macular degeneration (AMD). Here, anatomical, physiological and quantitative molecular data were used to calculate the ATP expenditure of the main energy-consuming processes in three cell types of the OR for the night and two different periods during the day. The predicted energy demands in a rod dominated (perifovea) area are 1.33 × 1013ATP/s/mm2tissue in the night and 6.53 × 1012ATP/s/mm2tissue during the day with indoor light conditions. For a cone-dominated foveal area the predicted energy demands are 6.44 × 1012ATP/s/mm2tissue in the night and 6.75 × 1012ATP/s/mm2tissue with indoor light conditions during daytime. We propose the likely need of for diurnal/circadian shifts in energy demands to efficiently stagger all energy consuming processes. Our data provide insights into vulnerabilities in the aging OR and suggest that diurnal constraints may be important when considering therapeutic interventions to optimize metabolism.
“Energetics of the outer retina I: Estimates of nutrient exchange and ATP generation.”
Authors: Prins Stella , Kiel Christina , Foss Alexander JE , Zouache Moussa A , Luthert Philip J
Published: June 2024

Photoreceptors (PRs) are metabolically demanding and packed at high density, which presents a challenge for nutrient exchange between the associated vascular beds and the tissue. Motivated by the ambition to understand the constraints under which PRs function, in this study we have drawn together diverse physiological and anatomical data in order to generate estimates of the rates of ATP production per mm2of retinal surface area. With the predictions of metabolic demand in the companion paper, we seek to develop an integrated energy budget for the outer retina.

It is known that rod PR number and the extent of the choriocapillaris (CC) vascular network that supports PRs both decline with age. To set the outer retina energy budget in the context of aging we demonstrate how, at different eccentricities, decline CC density is more than matched by rod loss in a way that tends to preserve nutrient exchange per rod. Together these finds provide an integrated framework for the study of outer retinal metabolism and how it might change with age.
Trajectory-centric Framework TrajAtlas reveals multi-scale differentiation heterogeneity among cells, genes, and gene module in osteogenesis
Authors: Han Litian , Ji Yaoting , Yu Yiqian , Ni Yueqi , Zeng Hao , Zhang Xiaoxin , Liu Huan , Zhang Yufeng
Published: June 2024

Osteoblast differentiation is crucial for bone formation and maintaining skeletal integrity. Although it is now understood that this process exhibits significant heterogeneity across developmental stages and tissue microenvironments, the underlying mechanisms remain largely unexplored. In the present study, we introduceTrajAtlas, a comprehensive framework that addresses this gap in knowledge.TrajAtlascomprises four modules: a reference atlas (Differentiation Atlas), a differentiation model (Differentiation Model), a tool for differential pseudotime analysis (TrajDiff), and a method for pseudotemporal gene module detection (TRAVMap). By leveraging single-cell technologies,TrajAtlasoffers a systematic approach to exploring the multi-scale heterogeneity among cells, genes, and gene modules within population-level trajectories across diverse tissues and age groups. We systematically investigate the impact of age and injury on osteogenesis, providing new insights into osteoporosis and bone regeneration. In conclusion, our comprehensive framework offers novel insights into osteogenesis and provides a valuable resource for understanding the complexities of bone formation.

Author Summary

Osteoblasts, the cells responsible for bone formation, can originate from various cellular sources. However, it’s unclear how different progenitor cells differentiate into osteoblasts, and how this process is influenced by factors such as age and tissue location. This knowledge gap stems from the lack of comprehensive databases and tools to decipher the differentiation process. In this study, we introduce TrajAtlas, a comprehensive framework designed to bridge this gap. To explore the cellular origins of osteoblasts, we constructed an atlas centered on osteogenesis. To answer how progenitor cells differentiate to osteoblasts, we developed a model that reveals the dynamic regulatory landscape during this process. To elucidate the influence of age and tissue location on differentiation, we built a tool for differential analysis. Furthermore, to identify conserved patterns of differentiation, we developed an approach to detect pseudotemporal gene modules. We validated the effectiveness of this framework by applying it to more datasets, unveiling novel cell states associated with injury. Notably, this framework focuses on dynamic processes, with the potential for broader applications in studying cell differentiation and complementing cell-centric analyses.
RNADiffFold: Generative RNA Secondary Structure Prediction using Discrete Diffusion Models
Authors: Wang Zhen , Feng Yizhen , Tian Qingwen , Liu Ziqi , Yan Pengju , Li Xiaolin
Published: June 2024

RNA molecules are essential macromolecules that perform diverse biological functions in living beings. Precise prediction of RNA secondary structures is instrumental in deciphering their complex three-dimensional architecture and functionality. Traditional methodologies for RNA structure prediction, including energy-based and learning-based approaches, often depict RNA secondary structures from a static perspective and rely on stringent a priori constraints. Inspired by the success of diffusion models, in this work, we introduce RNADiffFold, an innovative generative prediction approach of RNA secondary structures based on multinomial diffusion. We reconceptualize the prediction of contact maps as akin to pixel-wise segmentation and accordingly train a denoising model to refine the contact maps starting from a noise-infused state progressively. We also devise a potent conditioning mechanism that harnesses features extracted from RNA sequences to steer the model toward generating an accurate secondary structure. These features encompass one-hot encoded sequences, probabilistic maps generated from a pre-trained scoring network, and embeddings and attention maps derived from RNA-FM. Experimental results on both within- and cross-family datasets demonstrate RNADiffFold’s competitive performance compared with current state-of-the-art methods. Additionally, RNADiffFold has shown a notable proficiency in capturing the dynamic aspects of RNA structures, a claim corroborated by its performance on datasets comprising multiple conformations.
A clinically defined and xeno-free hydrogel system for regenerative medicine
Authors: Ong John , Gibbons George , Siang Lim Yee , Lei Zhou , Zhao Junzhe , Justin Alexander W. , Cammarata Federico , Rajarethinam Ravisankar , Limegrover Colleen , Sinha Sanjay , Lakatos Andras , Rouhani Foad J. , Young Dan Yock , Markaki Athina E.
Published: June 2024

Biofabricated scaffolds facilitate bona fide cellular interactions, cell type specification, and the formation of three-dimensional tissue architecture from human pluripotent stem cells (hPSCs). However, poorly defined and non-clinically approved synthetic biomaterials greatly hinder the translation of lab-grown therapies into clinical practice. Here, we describe a protein screen-based hydrogel system biofabricated from widely available clinical-grade human components. We show that “Alphagel”, a base hydrogel comprising human embryonic matrices, supports the trilineage differentiation of hPSCs into neural, cardiac, and liver tissue. Alphagel is also biocompatible and biodegradablein vivo. Further, by adding select proteins found in the maturing human foetal liver, the resulting hydrogel (termed “Hepatogel”) enhances the differentiation of hPSC-derived hepatocytes (H-iHeps) compared to Matrigel, a xenogenic and commonly used hydrogel that is prohibited in clinical use. Importantly, when injected into mice livers, Hepatogel significantly improves the engraftment rates of H-iHeps compared to standard cell injections. Altogether, our results provide proof of concept that this customisable hydrogel system is a useful tool for developing organ-specific and clinically translatable therapies for regenerative medicine and tissue engineering.
AlphaDIA enables End-to-End Transfer Learning for Feature-Free Proteomics
Authors: Wallmann Georg , Skowronek Patricia , Brennsteiner Vincenth , Lebedev Mikhail , Thielert Marvin , Steigerwald Sophia , Kotb Mohamed , Heymann Tim , Zhou Xie-Xuan , Schwörer Magnus , Strauss Maximilian T. , Ammar Constantin , Willems Sander , Zeng Wen-Feng , Mann Matthias
Published: June 2024

Mass spectrometry (MS)-based proteomics continues to evolve rapidly, opening more and more application areas. The scale of data generated on novel instrumentation and acquisition strategies pose a challenge to bioinformatic analysis. Search engines need to make optimal use of the data for biological discoveries while remaining statistically rigorous, transparent and performant. Here we present alphaDIA, a modular open-source search framework for data independent acquisition (DIA) proteomics. We developed a feature-free identification algorithm particularly suited for detecting patterns in data produced by sensitive time-of-flight instruments. It naturally adapts to novel, more eTicient scan modes that are not yet accessible to previous algorithms. Rigorous benchmarking demonstrates competitive identification and quantification performance. While supporting empirical spectral libraries, we propose a new search strategy named end-to-end transfer learning using fully predicted libraries. This entails continuously optimizing a deep neural network for predicting machine and experiment specific properties, enabling the generic DIA analysis of any post-translational modification (PTM). AlphaDIA provides a high performance and accessible framework running locally or in the cloud, opening DIA analysis to the community.
PhyloFunc: Phylogeny-informed Functional Distance as a New Ecological Metric for Metaproteomic Data Analysis
Authors: Wang Luman , Simopoulos Caitlin M. A. , Serrana Joeselle M. , Ning Zhibin , Li Yutong , Sun Boyan , Yuan Jinhui , Figeys Daniel , Li Leyuan
Published: June 2024

Background

Beta-diversity is a fundamental ecological metric for exploring dissimilarities between microbial communities. On the functional dimension, metaproteomics data can be used to quantify beta-diversity to understand how microbial community functional profiles vary under different environmental conditions. Conventional approaches to metaproteomic functional beta-diversity often treat protein functions as independent features, ignoring the evolutionary relationships among microbial taxa from which different proteins originate. A more informative functional distance metric that incorporates evolutionary relatedness is needed to better understand microbiome functional dissimilarities.

Results

Here, we introduce PhyloFunc, a novel functional beta-diversity metric that incorporates microbiome phylogeny to inform on metaproteomic functional distance. Leveraging the phylogenetic framework of weighted UniFrac distance, PhyloFunc innovatively utilizes branch lengths to weigh between-sample functional distances for each taxon, rather than differences in taxonomic abundance as in weighted UniFrac. Proof-of-concept using a simulated toy dataset and a real dataset from mouse inoculated with a synthetic gut microbiome and fed different diets show that PhyloFunc successfully captured functional compensatory effects between phylogenetically related taxa. We further tested a third dataset of complex human gut microbiomes treated with five different drugs to compare PhyloFunc’s performance with other traditional distance methods. PCoA and machine learning-based classification algorithms revealed higher sensitivity of PhyloFunc in microbiome responses to paracetamol. We providePhyloFuncas an open-source Python package (available athttps://pydigger.com/pypi/PhyloFunc), enabling efficient calculation of functional beta-diversity distances between a pair of samples or the generation of a distance matrix for all samples within a dataset.

Conclusions

Unlike traditional approaches that consider metaproteomics features as independent and unrelated, PhyloFunc acknowledges the role of phylogenetic context in shaping the functional landscape in metaproteomes. In particular, we report that PhyloFunc accounts for the functional compensatory effect of taxonomically related species. Its effectiveness, ecological relevance, and enhanced sensitivity in distinguishing group variations are demonstrated through the specific applications presented in this study.
AFsample2: Predicting multiple conformations and ensembles with AlphaFold2
Authors: Kalakoti Yogesh , Wallner Björn
Published: June 2024

Understanding protein dynamics and conformational states carries profound scientific and practical implications for several areas of research, ranging from a general understanding of biological processes at the molecular level to a detailed understanding of disease mechanisms, which in turn can open up new avenues in drug development. Multiple solutions have been recently developed to widen the conformational landscape of predictions made by Alphafold2 (AF2). Here, we introduce AFsample2, a method employing random MSA column masking to reduce the influence of co-evolutionary signals to enhance the structural diversity of models generated by the AF2 neural network. AFsample2 improves the prediction of alternative states for a broad range of proteins, yielding high-quality end states and diverse conformational ensembles. In the data set of open-closed conformations (OC23), alternate state models improved in 17 out of 23 cases without compromising the generation of the preferred state. Consistent results were observed in 16 membrane protein transporters, with improvements in 12 out of 16 targets. TM-score improvements to experimental end states were substantial, sometimes exceeding 50%, elevating mediocre scores from 0.58 to nearly perfect 0.98. Furthermore, AFsample2 increased the diversity of intermediate conformations by 70% compared to the standard AF2 system, producing highly confident models, that could potentially be on-path between the two states. In addition, we also propose a way of selecting the end-states in generated model ensembles. These solutions could potentially enhance the generation and identification of alternative protein conformations, thereby providing a more comprehensive understanding of protein function and dynamics. Future work will focus on validating the accuracy of these intermediate conformations and exploring their relevance to functional transitions in proteins.