Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 57,521 bioRxiv papers from 264,855 authors.
Most downloaded bioRxiv papers, since beginning of last month
56,141 results found. For more information, click each entry to expand.
297 downloads biophysics
Recent advances in light-sheet microscopy have enabled sensitive imaging with high spatiotemporal resolution. However, the creation of thin light-sheets for high axial resolution is challenging, as the thickness of the sheet, field of view and confinement of the excitation need to be carefully balanced. Some of the thinnest light-sheets created so far have found little practical use as they excite too much out-of-focus fluorescence. In contrast, the most commonly used light-sheet for subcellular imaging, the square lattice, has excellent excitation confinement at the cost of lower axial resolving power. Here we leverage the recently discovered Field Synthesis theorem to create light-sheets where thickness and illumination confinement can be continuously tuned to give microscopists more choices. We experimentally characterize these light-sheets and show their application on biological samples.
297 downloads microbiology
Background: Gonorrhoea incidence is increasing rapidly: diagnoses in men who have sex with men (MSM) in England increased eight-fold 2008-2017. Concurrently, antibiotic resistance is making treatment more difficult, leading to renewed interest in a gonococcal vaccine. The MeNZB meningococcal B vaccine is partially protective, and several other candidates are in development. We modelled realistic vaccination strategies under various scenarios of antibiotic resistance and vaccine protection level and duration, to assess the impact of vaccination and examine the feasibility of the WHO's target of reducing gonorrhoea incidence by 90% between 2016 and 2030. Methods: We fitted a stochastic transmission-dynamic model, incorporating asymptomatic and symptomatic infection and heterogeneous sexual behaviour, to gonorrhoea incidence in MSM in England, 2008-17, using particle Markov Chain Monte Carlo methods. Bayesian forecasting was used to examine future scenarios, including emergence of extensively antibiotic-resistant (ABR) gonorrhoea. Findings: Even in the worst-case scenario of untreatable infection emerging, the WHO target could be met by vaccinating all MSM attending sexual health clinics with a 53%-protective vaccine lasting for >6 years, or a 70%-protective vaccine lasting >3 years. A vaccine like MeNZB, conferring 30% protection for 2-4 years, could reduce incidence in 2030 by 45% in the worst-case scenario, and by 75% if >70% of ABR gonorrhoea is treatable. Interpretation: Our statistically-rigorous assessment shows that even a partially-protective vaccine, delivered through a practical targeting strategy, could have substantial benefit in reducing gonorrhoea incidence in the context of an epidemic with rising antibiotic resistance.
296 downloads genomics
Rhesus macaque (Macaca mulatta) is a widely-studied nonhuman primate. Here we present a high-quality de novo genome assembly of the Chinese rhesus macaque (rheMacS) using long-read sequencing and multiplatform scaffolding approaches. Compared to the current Indian rhesus macaque reference genome (rheMac8), the rheMacS genome assembly improves sequence contiguity by 75-fold, closing 21,940 of the remaining assembly gaps (60.8 Mbp). To improve gene annotation, we generated more than two million full-length transcripts from ten different tissues by long-read RNA sequencing. We sequence resolve 53,916 structural variants (96% novel) and identify 17,000 ape-specific structural variants (ASSVs) based on comparison to the long-read assembly of ape genomes. We show that many ASSVs map within ChIP-seq predicted enhancer regions where apes and macaque show diverged enhancer activity and expression. We further characterize a set of candidate ASSVs that may contribute to ape- or great-ape-specific phenotypic traits, including taillessness, brain volume expansion, improved manual dexterity, and large body size. This improved rheMacS genome assembly serves as an ideal reference for future biomedical and evolutionary studies.
295 downloads evolutionary biology
Non-transitivity - commonly illustrated by the rock-paper-scissors game - is purported to be common in evolution despite a lack of examples of non-transitive interactions arising along a single line of descent. We identify a non-transitive evolutionary sequence in the context of yeast experimental evolution in which a 1,000-generation evolved clone loses in direct competition with its ancestor. We show that non-transitivity arises due to the combined effects of adaptation mediated by the evolving nuclear genome combined with the stepwise deterioration of an intracellular virus. We show that multilevel selection is widespread: nearly half of all populations fix adaptive mutations in both the nuclear and viral genomes, and clonal interference and genetic hitchhiking occur at both levels. Surprisingly, viral mutations do not increase the fitness of their host. Instead, the evolutionary success of evolved viral variants results from their selective advantage over viral competitors within the context of individual cells. Overall, our results show that widespread multilevel selection is capable of producing complex evolutionary dynamics - including non-transitivity - under simple laboratory conditions.
295 downloads neuroscience
The internal representations of early deep artificial neural networks (ANNs) were found to be remarkably similar to the internal neural representations measured experimentally in the primate brain. Here we ask, as deep ANNs have continued to evolve, are they becoming more or less brain-like? ANNs that are most functionally similar to the brain will contain mechanisms that are most like those used by the brain. We therefore developed Brain-Score - a composite of multiple neural and behavioral benchmarks that score any ANN on how similar it is to the brain's mechanisms for core object recognition - and we deployed it to evaluate a wide range of state-of-the-art deep ANNs. Using this scoring system, we here report that: (1) DenseNet-169, CORnet-S and ResNet-101 are the most brain-like ANNs. (2) There remains considerable variability in neural and behavioral responses that is not predicted by any ANN, suggesting that no ANN model has yet captured all the relevant mechanisms. (3) Extending prior work, we found that gains in ANN ImageNet performance led to gains on Brain-Score. However, correlation weakened at >= 70% top-1 ImageNet performance, suggesting that additional guidance from neuroscience is needed to make further advances in capturing brain mechanisms. (4) We uncovered smaller (i.e. less complex) ANNs that are more brain-like than many of the best-performing ImageNet models, which suggests the opportunity to simplify ANNs to better understand the ventral stream. The scoring system used here is far from complete. However, we propose that evaluating and tracking model-benchmark correspondences through a Brain-Score that is regularly updated with new brain data is an exciting opportunity: experimental benchmarks can be used to guide machine network evolution, and machine networks are mechanistic hypotheses of the brain's network and thus drive next experiments. To facilitate both of these, we release Brain-Score.org: a platform that hosts the neural and behavioral benchmarks, where ANNs for visual processing can be submitted to receive a Brain-Score and their rank relative to other models, and where new experimental data can be naturally incorporated.
295 downloads bioinformatics
Motivation: Long-read sequencing and novel long-range assays have revolutionized de novo genome assembly by automating the reconstruction of reference-quality genomes. In particular, Hi-C sequencing is becoming an economical method for generating chromosome-scale scaffolds. Despite its increasing popularity, there are limited open-source tools available. Errors, particularly inversions and fusions across chromosomes, remain higher than alternate scaffolding technologies. Results: We present a novel open-source Hi-C scaffolder that does not require an a priori estimate of chromosome number and minimizes errors by scaffolding with the assistance of an assembly graph. We demonstrate higher accuracy than the state-of-the-art methods across a variety of Hi-C library preparations and input assembly sizes. Availability and Implementation: The Python and C++ code for our method is openly available at https://github.com/machinegun/SALSA.
294 downloads neuroscience
Sensory pathways are typically studied starting at receptor neurons and following postsynaptic neurons into the brain. This leads to a bias in analysis of sensory activity towards early layers of sensory processing or to regions containing the majority of postsynaptic neurons. Here, we present new methods for unbiased volumetric neural imaging with precise across-brain registration, to characterize auditory activity throughout the entire central brain of Drosophila, and make comparisons across trials, individuals, and sexes. We discover that auditory activity is widespread across nearly all central brain regions. These auditory representations are temporally diverse, but the majority of activity is focused on aspects of the conspecific courtship song. We find that auditory responses are stereotyped across trials and animals in early mechanosensory regions, becoming more variable at higher layers of the putative pathway. This study highlights the power of using a brain-wide approach for mapping the functional organization of sensory activity.
294 downloads microbiology
Themoula Charalampous, Hollian Richardson, Gemma Louise Kay, Rossella Baldan, Christopher Jeanes, Duncan Rae, Sara Grundy, Daniel J Turner, John Wain, Richard Mark Leggett, David M Livermore, Justin O'Grady
Lower respiratory infections (LRIs) accounted for three million deaths worldwide in 2016, the leading infectious cause of mortality. The 'gold standard' for investigation of bacterial LRIs is culture, which has poor sensitivity and is too slow to guide early antibiotic therapy. Metagenomic sequencing potentially could replace culture, providing rapid, sensitive and comprehensive results. We developed a metagenomics pipeline for the investigation of bacterial LRIs using saponin-based host DNA depletion combined with rapid nanopore sequencing. The first iteration of the pipeline was tested on respiratory samples from 40 patients. It was then refined to reduce turnaround and increase sensitivity, before testing a further 41 samples. The refined method was 96.6% concordant with culture for detection of pathogens and could accurately detect resistance genes with a turnaround time of six hours. This study demonstrates that nanopore metagenomics can rapidly and accurately characterise bacterial LRIs when combined with efficient human DNA depletion.
294 downloads neuroscience
Many experimental studies suggest that animals can rapidly learn to identify odors and predict the rewards associated with them. However, the underlying plasticity mechanism remains elusive. In particular, it is not clear how olfactory circuits achieve rapid, data efficient learning with local synaptic plasticity. Here, we formulate olfactory learning as a Bayesian optimization process, then map the learning rules into a computational model of the mammalian olfactory circuit. The model is capable of odor identification from a small number of observations, while reproducing cellular plasticity commonly observed during development. We extend the framework to reward-based learning, and show that the circuit is able to rapidly learn odor-reward association with a plausible neural architecture. These results deepen our theoretical understanding of unsupervised learning in the mammalian brain.
294 downloads systems biology
Katja Luck, Dae-Kyum Kim, Luke Lambourne, Kerstin Spirohn, Bridget E Begg, Wenting Bian, Ruth Brignall, Tiziana Cafarelli, Francisco J Campos-Laborie, Benoit Charloteaux, Dongsic Choi, Atina G. Cote, Meaghan Daley, Steven Deimling, Alice Desbuleux, Amelie Dricot, Marinella Gebbia, Madeleine F Hardy, Nishka Kishore, Jennifer J Knapp, Istvan A Kovacs, Irma Lemmens, Miles W Mee, Joseph C. Mellor, Carl Pollis, Carles Pons, Aaron D Richardson, Sadie Schlabach, Bridget Teeking, Anupama Yadav, Mariana Babor, Dawit Balcha, Omar Basha, Christian Bowman-Colin, Suet-Feung Chin, Soon Gang Choi, Claudia Colabella, Georges Coppin, Cassandra D'Amata, David De Ridder, Steffi De Rouck, Miquel Duran-Frigola, Hanane Ennajdaoui, Florian Goebels, Liana Goehring, Anjali Gopal, Ghazal Haddad, Elodie Hatchi, Mohamed Helmy, Yves Jacob, Yoseph Kassa, Serena Landini, Roujia Li, Natascha van Lieshout, Andrew MacWilliams, Dylan Markey, Joseph N Paulson, Sudharshan Rangarajan, John Rasla, Ashyad Rayhan, Thomas Rolland, Adriana San-Miguel, Yun Shen, Dayag Sheykhkarimli, Gloria M. Sheynkman, Eyal Simonovsky, Murat Taşan, Alexander Tejeda, Jean-Claude Twizere, Yang Wang, Robert J. Weatheritt, Jochen Weile, Yu Xia, Xinping Yang, Esti Yeger-Lotem, Quan Zhong, Patrick Aloy, Gary D. Bader, Javier De Las Rivas, Suzanne Gaudet, Tong Hao, Janusz Rak, Jan Tavernier, Vincent Tropepe, David E. Hill, Marc Vidal, Frederick P Roth, Michael A. Calderwood
Global insights into cellular organization and function require comprehensive understanding of interactome networks. Similar to how a reference genome sequence revolutionized human genetics, a reference map of the human interactome network is critical to fully understand genotype-phenotype relationships. Here we present the first human "all-by-all" binary reference interactome map, or "HuRI". With ~53,000 high-quality protein-protein interactions (PPIs), HuRI is approximately four times larger than the information curated from small-scale studies available in the literature. Integrating HuRI with genome, transcriptome and proteome data enables the study of cellular function within essentially any physiological or pathological cellular context. We demonstrate the use of HuRI in identifying specific subcellular roles of PPIs and protein function modulation via splicing during brain development. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms underlying tissue-specific phenotypes of Mendelian diseases. HuRI thus represents an unprecedented, systematic reference linking genomic variation to phenotypic outcomes.
293 downloads pathology
A quantitative model to genetically interpret the histology in whole microscopy slide images is desirable to guide downstream immunohistochemistry, genomics, and precision medicine. We constructed a statistical model that predicts whether or not SPOP is mutated in prostate cancer, given only the digital whole slide after standard hematoxylin and eosin [H&E] staining. Using a TCGA cohort of 177 prostate cancer patients where 20 had mutant SPOP, we trained multiple ensembles of residual networks, accurately distinguishing SPOP mutant from SPOP non-mutant patients (test AUROC=0.74, p=0.0007 Fisher's Exact Test). We further validated our full metaensemble classifier on an independent test cohort from MSK-IMPACT of 152 patients where 19 had mutant SPOP. Mutants and non-mutants were accurately distinguished despite TCGA slides being frozen sections and MSK-IMPACT slides being formalin-fixed paraffin-embedded sections (AUROC=0.86, p=0.0038). Moreover, we scanned an additional 36 MSK-IMPACT patient having mutant SPOP, trained on this expanded MSK-IMPACT cohort (test AUROC=0.75, p=0.0002), tested on the TCGA cohort (AUROC=0.64, p=0.0306), and again accurately distinguished mutants from non-mutants using the same pipeline. Importantly, our method demonstrates tractable deep learning in this "small data" setting of 20-55 positive examples and quantifies each prediction's uncertainty with confidence intervals. To our knowledge, this is the first statistical model to predict a genetic mutation in cancer directly from the patient's digitized H&E-stained whole microscopy slide. Moreover, this is the first time quantitative features learned from patient genetics and histology have been used for content-based image retrieval, finding similar patients for a given patient where the histology appears to share the same genetic driver of disease i.e. SPOP mutation (p=0.0241 Kost's Method), and finding similar patients for a given patient that does not have have that driver mutation (p=0.0170 Kost's Method).
293 downloads molecular biology
Chromatin remodelling plays important roles in gene regulation during development, differentiation and in disease. The chromatin remodelling enzyme CHD4 is a component of the NuRD and ChAHP complexes that are involved in gene repression. Here we report the cryo-electron microscopy (cryo-EM) structure of Homo sapiens CHD4 engaged with a nucleosome core particle in the presence of the non-hydrolysable ATP analogue AMP-PNP at an overall resolution of 3.1 Å. The ATPase motor of CHD4 binds and distorts nucleosomal DNA at superhelical location (SHL) +2, supporting the 'twist defect' model of chromatin remodelling. CHD4 does not induce unwrapping of terminal DNA, in contrast to its homologue Chd1, which functions in gene activation. Our results also rationalize the effect of CHD4 mutations that are associated with cancer or the intellectual disability disorder Sifrim-Hitz-Weiss syndrome.
293 downloads neuroscience
Neuronal ensembles that hold specific memory (memory engrams) have been identified in the hippocampus, amygdala, and cortex. It has been hypothesized that engrams for a specific memory are distributed among multiple brain regions that are functionally connected. Here, we report the hitherto most extensive engram map for contextual fear memory by characterizing activity-tagged neurons in 409 regions using SHIELD-based tissue phenotyping. The mapping was aided by a novel engram index, which identified cFos+ brain regions holding engrams with a high probability. Optogenetic manipulations confirmed previously known engrams and revealed new engrams. Many of these engram holding-regions were functionally connected to the CA1 or amygdala engrams. Simultaneous chemogenetic reactivation of multiple engrams, which mimics natural memory recall, conferred a greater level of memory recall than reactivation of a single engram ensemble. Overall, our study supports the hypothesis that a memory is stored in functionally connected engrams distributed across multiple brain regions.
293 downloads scientific communication and education
Although the Journal Impact Factor (JIF) is widely acknowledged to be a poor indicator of the quality of individual papers, it is used routinely to evaluate research and researchers. Here, we present a simple method for generating the citation distributions that underlie JIFs. Application of this straightforward protocol reveals the full extent of the skew of these distributions and the variation in citations received by published papers that is characteristic of all scientific journals. Although there are differences among journals across the spectrum of JIFs, the citation distributions overlap extensively, demonstrating that the citation performance of individual papers cannot be inferred from the JIF. We propose that this methodology be adopted by all journals as a move to greater transparency, one that should help to refocus attention on individual pieces of work and counter the inappropriate usage of JIFs during the process of research assessment.
292 downloads bioinformatics
We introduce a simple new approach to variable selection in linear regression, and to quantifying uncertainty in selected variables. The approach is based on a new model -- the "Sum of Single Effects" (SuSiE) model -- which comes from writing the sparse vector of regression coefficients as a sum of "single-effect" vectors, each with one non-zero element. We also introduce a corresponding new fitting procedure -- Iterative Bayesian Stepwise Selection (IBSS) -- which is a Bayesian analogue of traditional stepwise selection methods. IBSS shares the computational simplicity and speed of traditional stepwise methods, but instead of selecting a single variable at each step, IBSS computes a distribution on variables that captures uncertainty in which variable to select. We show that the IBSS algorithm computes a variational approximation to the posterior distribution under the SuSiE model. Further, this approximate posterior distribution naturally leads to a convenient, novel, way to summarize uncertainty in variable selection, and provides a Credible Set for each selected variable. Our methods are particularly well suited to settings where variables are highly correlated and true effects are very sparse, both of which are characteristics of genetic fine-mapping applications. We demonstrate through numerical experiments that our methods outperform existing methods for this task, and illustrate the methods by fine-mapping genetic variants that influence alternative splicing in human cell-lines. We also discuss both the potential and the challenges for applying these methods to generic variable selection problems.
292 downloads developmental biology
The evolution of embryological development has long been characterized by deep conservation. Both morphological and transcriptomic surveys have proposed a 'hourglass' model of Evo-Devo. A stage in mid-embryonic development, the phylotypic stage, is highly conserved among species within the same phylum. However, the reason for this phylotypic stage is still elusive. Here we hypothesize that the phylotypic stage might be characterized by selection for robustness to noise and environmental perturbations. This could lead to mutational robustness, thus evolutionary conservation of expression and the hourglass pattern. To test this, we quantified expression variability of single embryo transcriptomes throughout fly Drosophila melanogaster embryogenesis. We found that indeed expression variability is lower at extended germband, the phylotypic stage. We explain this pattern by stronger histone modification mediated transcriptional noise control at this stage. In addition, we find evidence that histone modifications can also contribute to mutational robustness in regulatory elements. Thus, the robustness to noise does indeed contributes to robustness of gene expression to genetic variations, and to the conserved phylotypic stage.
292 downloads bioinformatics
One common task in Computational Biology is the prediction of aspects of protein function and structure from their amino acid sequence. For 26 years, most state-of-the-art approaches toward this end have been marrying machine learning and evolutionary information, resulting in the need to retrieve related proteins at increasing cost from ever growing sequence databases. This search is so time-consuming to often render the analysis of entire proteomes infeasible. On top, evolutionary information is less powerful for small families, e.g. for proteins from the Dark Proteome . Here, we introduced a novel way to represent protein sequences as continuous vectors ( embeddings ) by using the deep bidirectional language model ELMo. The model effectively captured the biophysical properties of protein sequences from unlabeled big data (UniRef50). We showed how, after training, this knowledge was transferred to single protein sequences by predicting relevant sequence features. We referred to these new embeddings as SeqVec ( Seq uence-to- Vec tor) and demonstrated their effectiveness by training simple neural networks on existing data sets for two completely different prediction tasks. At the per-residue level, we improved secondary structure (for NetSurfP-2.0 data set: Q3=79%±1, Q8=68%±1) and disorder predictions (MCC=0.59±0.03) that use only single protein sequences by a large margin. At the per-protein level, we predicted subcellular localization in ten classes (for DeepLoc dataset: Q10=68%±1) and distinguished membrane-bound from water-soluble proteins (Q2= 87%±1). All results built upon embeddings gained from the new tool SeqVec. These are derived from the target protein’s sequence alone. Where the lightning-fast HHblits needed on average several minutes to generate the evolutionary information for a target protein, SeqVec created the vector representation on average in 0.027 seconds. Availability SeqVec : <https://github.com/Rostlab/SeqVec> Prediction server : <https://embed.protein.properties> * 1D : one-dimensional – information representable in a string such as secondary structure or solvent accessibility 3D : three-dimensional 3D structure : three-dimensional coordinates of protein structure MCC : Matthews-Correlation-Coefficient RSA : relative solvent accessibility
292 downloads genomics
Ziyi Xiong, Gabriela Dankova, Laurence Howe Howe, Myoung Lee, Pirro Hysi, Markus de Jong, Gu Zhu, Kaustubh Adhikari, Dan Li, Yi Li, Bo Pan, Eleanor Feingold, Mary Marazita, John Shaffer, Kerrie McAloney, Shuhua Xu, Li Jin, Sijia Wang, Femke Vrij, Bas Lendemeijer, Stephen Richmond, Alexei Zhurov, Sarah Lewis, Gemma Sharp, Lavinia Paternoster, Holly Thompson, Rolando Gonzales-Jose, Maria Catira Bortolini, Samuel Canizales-Quinteros, Carla Gallo, Giovanni Poletti, Gabriel Bedoya, Francisco Rothhammer, Andre Uitterlinden, M Arfan Ikram, Eppo Wolvius, Steven Kushner, Tamar Nijsten, Robert-Jan Palstra, Stefan Boehringer, Sarah Medland, Kun Tang, Andres Ruiz-Linares, Nicholas Martin, Timothy Spector, Evie Stergiakouli, Seth Weinberg, Fan Liu, Manfred Kayser
The human face represents a combined set of highly heritable phenotypes, but knowledge on its genetic architecture remains limited despite the relevance for various fields of science and application. A series of genome-wide association studies on 78 facial shape phenotypes quantified from 3-dimensional facial images of 10,115 Europeans identified 24 genetic loci reaching genome-wide significant association, among which 17 were previously unreported. A multi-ethnic study in additional 7,917 individuals confirmed 13 loci including 8 unreported ones. A global map of polygenic face scores assembled facial features in major continental groups consistent with anthropological knowledge. Analyses of epigenomic datasets from cranial neural crest cells revealed abundant cis-regulatory activities at the face-associated genetic loci. Luciferase reporter assays in neural crest progenitor cells highlighted enhancer activities of several face-associated DNA variants. These results substantially advance our understanding of the genetic basis underlying human facial variation and provide candidates for future in-vivo functional studies.
291 downloads biochemistry
Live-cell fluorescence nanoscopy is a powerful tool to study cellular biology on a molecular scale, yet its use is held back by the paucity of suitable fluorescent probes. Fluorescent probes based on regular fluorophores usually suffer from low cell permeability and unspecific background signal. We report a general strategy to transform regular fluorophores into fluorogenic probes with excellent cell permeability and low unspecific background signal. The strategy is based on the conversion of a carboxyl group found in rhodamines and related fluorophores into an electron-deficient amide. This conversion does not affect the spectroscopic properties of the fluorophore but permits it to exist in a dynamic equilibrium between two different forms: a fluorescent zwitterion and a non-fluorescent, cell permeable spirolactam. Probes based on such fluorophores generally are fluorogenic as the equilibrium shifts towards the fluorescent form when the probe binds to its cellular targets. The resulting increase in fluorescence can be up to 1000-fold. Using this simple design principle we created fluorogenic probes in various colours for different cellular targets for wash-free, multicolour, live-cell nanoscopy. The work establishes a general strategy to develop fluorogenic probes for live-cell bioimaging.
291 downloads immunology
The targeting of metabolic pathways is emerging as an exciting new approach for modulating immune cell function and polarization states. In this study, carbon tracing and systems biology approaches integrating metabolomic and transcriptomic profiling data were used to identify adaptations in human T cell metabolism important for fueling pro-inflammatory T cell function. Results of this study demonstrate that T cell receptor (TCR) stimulation leads to a significant increase in glucose and amino acid metabolism that trigger downstream biosynthetic processes. Specifically, increased expression of several enzymes such as CTPS1, IL4I1, and ASL results in the reprogramming of amino acid metabolism. Additionally, the strength of TCR signaling resulted in different metabolic enzymes utilized by T cells to facilitate similar biochemical endpoints. Furthermore, this study shows that cyclosporine represses the pathways involved in amino acid and glucose metabolism, providing novel insights on the immunosuppressive mechanisms of this drug. To explore the implications of the findings of this study in clinical settings, conventional immunosuppressants were tested in combination with drugs that target metabolic pathways. Results showed that such combinations increased efficacy of conventional immunosuppressants. Overall, the results of this study provide a comprehensive resource for identifying metabolic targets for novel combinatorial regimens in the treatment of intractable immune diseases.
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