Biology subjects

Berger, B.

Publications and source records attributed to Berger, B..

At least 55 records · Page 3Linked to original sources

bioRxiv2021

Deciphering the species-level structure of topologically associating domains

Chromosome conformation capture technologies such as Hi-C have revealed a rich hierarchical structure of chromatin, with topologically associating domains (TADs) as a key organizational unit, but experimentally reported TAD architectures, currently determined separately for each cell type, are lacking for many cell/tissue types. A solution to address this issue is to integrate existing epigenetic data across cells and tissue types to develop a species-level consensus map relating genes to TADs. Here, we introduce the TAD Map, a bag-of-genes representation that we use to infer, or "impute," TAD architectures for those cells/tissues with limited Hi-C experimental data. The TAD Map enables a systematic analysis of gene coexpression induced by chromatin structure. By overlaying transcriptional data from hundreds of bulk and single-cell assays onto the TAD Map, we assess gene coexpression in TADs and find that expressed genes cluster into fewer TADs than would be expected by chance, and show that time-course and RNA velocity studies further reveal this clustering to be strongest in the early stages of cell differentiation; it is also strong in tumor cells. We provide a probabilistic model to summarize any scRNA-seq transcriptome in terms of its TAD activation profile, which we term a TAD signature, and demonstrate its value for cell type inference, cell fate prediction, and multimodal synthesis. More broadly, our work indicates that the TAD Maps comprehensive, quantitative integration of chromatin structure and scRNA-seq data should play a key role in epigenetic and transcriptomic analyses. Software availability: https://tadmap.csail.mit.edu Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=187 HEIGHT=200 SRC="FIGDIR/small/466333v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@1d652dborg.highwire.dtl.DTLVardef@1d9c68dorg.highwire.dtl.DTLVardef@7a8e66org.highwire.dtl.DTLVardef@1afaff_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2021

Developmental Dynamics of RNA Translation in the Human Brain

The precise regulation of gene expression is fundamental to neurodevelopment, plasticity, and cognitive function. While several studies have deeply profiled mRNA dynamics in the developing human brain, there is a fundamental gap in our understanding of accompanying translational regulation. We perform ribosome profiling from more than 70 human prenatal and adult cortex samples across ontogeny and into adulthood, mapping translation events at nucleotide resolution. In addition to characterizing the translational regulation of annotated open reading frames (ORFs), we identify thousands of previously unknown translation events, including small open reading frames (sORFs) that give rise to human- and/or brain-specific microproteins, many of which we independently verify using size-selected proteomics. Ribosome profiling in stem cell-derived human neuronal cultures further corroborates these findings and shows that several neuronal activity-induced long non-coding RNAs (lncRNAs), including LINC00473, a primate-specific lncRNA implicated in depression, encode previously undescribed microproteins. Physicochemical analysis of these brain microproteinss identifies a large class harboring arginine-glycine-glycine (RGG) repeats as strong candidates for regulating RNA metabolism. Moreover, we find that, collectively, these previously unknown human brain sORFs are enriched for variants associated with schizophrenia. In addition to significantly expanding the translational landscape of the developing brain, this atlas will serve as a rich resource for the annotation and functional interrogation of thousands of previously unknown brain-specific protein products.

neuroscience↗

bioRxiv2021

Phylogenetic reconciliation reveals extensive ancestral recombination in Sarbecoviruses and the SARS-CoV-2 lineage

An accurate understanding of the evolutionary history of rapidly-evolving viruses like SARS-CoV-2, responsible for the COVID-19 pandemic, is crucial to tracking and preventing the spread of emerging pathogens. However, viruses undergo frequent recombination, which makes it difficult to trace their evolutionary history using traditional phylogenetic methods. Here, we present a phylogenetic workflow, virDTL, for analyzing viral evolution in the presence of recombination. Our approach leverages reconciliation methods developed for inferring horizontal gene transfer in prokaryotes, and, compared to existing tools, is uniquely able to identify ancestral recombinations while accounting for several sources of inference uncertainty, including in the construction of a strain tree, estimation and rooting of gene family trees, and reconciliation itself. We apply this workflow to the Sarbecovirus subgenus and demonstrate how a principled analysis of predicted recombination gives insight into the evolution of SARS-CoV-2. In addition to providing confirming evidence for the horseshoe bat as its zoonotic origin, we identify several ancestral recombination events that merit further study.

evolutionary biology↗

bioRxiv2021

Learning the mutational landscape of the cancer genome

An ongoing challenge to better understand and treat cancer is to distinguish neutral mutations, which do not affect tumor fitness, from those that provide a proliferative advantage. However, the variability of mutation rates has limited our ability to model patterns of neutral mutations and therefore identify cancer driver mutations. Here, we predict cancer-specific mutation rates genome-wide by leveraging deep neural networks to learn mutation rates within kilobase-scale regions and then refining these estimates to test for evidence of selection on combinations of mutations by comparing observed to expected mutation counts. We mapped mutation rates for 37 cancer types and used these maps to identify new putative drivers in understudied regions of the genome including cryptic alternative-splice sites, 5 untranslated regions and infrequently mutated genes. These results, available for exploration via web interface, indicate the potential for high-resolution neutral mutation models to empower further driver discovery as cancer sequencing cohorts grow.

cancer biology↗

bioRxiv2021

Minimizer-space de Bruijn graphs

DNA sequencing data continues to progress towards longer reads with increasingly lower sequencing error rates. We focus on the problem of assembling such reads into genomes, which poses challenges in terms of accuracy and computational resources when using cutting-edge assembly approaches, e.g. those based on overlapping reads using minimizer sketches. Here, we introduce the concept of minimizer-space sequencing data analysis, where the minimizers rather than DNA nucleotides are the atomic tokens of the alphabet. By projecting DNA sequences into ordered lists of minimizers, our key idea is to enumerate what we call k-min-mers, that are k-mers over a larger alphabet consisting of minimizer tokens. Our approach, mdBG or minimizer-dBG, achieves orders-of-magnitude improvement in both speed and memory usage over existing methods without much loss of accuracy. We demonstrate three uses cases of mdBG: human genome assembly, metagenome assembly, and the representation of large pangenomes. For assembly, we implemented mdBG in software we call rust-mdbg, resulting in ultra-fast, low memory and highly-contiguous assembly of PacBio HiFi reads. A human genome is assembled in under 10 minutes using 8 cores and 10 GB RAM, and 60 Gbp of metagenome reads are assembled in 4 minutes using 1 GB RAM. For pangenome graphs, we newly allow a graphical representation of a collection of 661,405 bacterial genomes as an mdBG and successfully search it (in minimizer-space) for anti-microbial resistance (AMR) genes. We expect our advances to be essential to sequence analysis, given the rise of long-read sequencing in genomics, metagenomics and pangenomics.

bioinformatics↗

bioRxiv2021

Sequence-based prediction of protein-protein interactions: a structure-aware interpretable deep learning model

Protein-protein interaction (PPI) networks have proven to be a valuable tool in systems biology to facilitate the discovery and understanding of protein function. Unfortunately, experimental PPI data remains sparse in most model organisms and even more so in other species. Existing methods for computational prediction of PPIs seek to address this limitation, and while they perform well when sufficient within-species training data is available, they generalize poorly to new species or often require specific types and sizes of training data that may not be available in the species of interest. We therefore present D-SCRIPT, a deep learning method for predicting a physical interaction between two proteins given just their sequences. Compared to existing methods, D-SCRIPT generalizes better to new species and is robust to limitations in training data size. Our approach encodes the intuition that for two proteins to physically interact, a subset of amino acids from each protein should be in contact with the other. The intermediate stages of D-SCRIPT directly implement this intuition; the penultimate stage in D-SCRIPT is a rough estimate of the inter-protein contact map of the protein dimer. This structurally-motivated design enables interpretability of our model and, since structure is more conserved evolutionarily than sequence, improves generalizability across species. We show that a D-SCRIPT model trained on 38,345 human PPIs enables significantly improved functional characterization of fly proteins compared to the state-of-the-art approach. Evaluating the same D-SCRIPT model on protein complexes with known 3-D structure, we find that the inter-protein contact map output by D-SCRIPT has significant overlap with the ground truth. Our work suggests that recent advances in deep learning language modeling of protein structure can be leveraged for protein interaction prediction from sequence. D-SCRIPT is available at http://dscript.csail.mit.edu.

bioinformatics↗

bioRxiv2021

Scalpel: Information-based Dimensionality Reduction for Rare Cell Type Discovery

Dimensionality reduction is crucial to summarizing the complex transcriptomic landscape of single cell datasets for downstream analyses. However, current dimensionality reduction approaches favor large cellular populations defined by many genes, at the expense of smaller and more subtly-defined populations. Here, we present surprisal component analysis (SCA), a technique that leverages the information-theoretic notion of surprisal for dimensionality reduction, and demonstrate its ability to improve the representation of clinically important populations that are indistinguishable using existing pipelines. For example, in cytotoxic T-cell data, SCA cleanly separates the gamma-delta and MAIT cell subpopulations, which are not detectable via PCA, ICA, scVI, or a wide array of specialized rare cell recovery tools. We also show that, when used instead of PCA, SCA improves downstream imputation to more accurately restore mRNA dropouts and recover important gene-gene relationships. SCAs information-theoretic paradigm opens the door to more meaningful signal extraction, with broad applications to the study of complex biological tissues in health and disease.

bioinformatics↗

bioRxiv2020

A Python-based optimization framework for high-performance genomics

Exponentially-growing next-generation sequencing data requires high-performance tools and algorithms. Nevertheless, the implementation of high-performance computational genomics software is inaccessible to many scientists because it requires extensive knowledge of low-level software optimization techniques, forcing scientists to resort to high-level software alternatives that are less efficient. Here, we introduce Seq--a Python-based optimization framework that combines the power and usability of high-level languages like Python with the performance of low-level languages like C or C++. Seq allows for shorter, simpler code, is readily usable by a novice programmer, and obtains significant performance improvements over existing languages and frameworks. We showcase and evaluate Seq by implementing seven standard, widely-used applications from all stages of the genomics analysis pipeline, including genome index construction, finding maximal exact matches, long-read alignment and haplotype phasing, and demonstrate its implementations are up to an order of magnitude faster than existing hand-optimized implementations, with just a fraction of the code. By enabling researchers of all backgrounds to easily implement high-performance analysis tools, Seq further opens the door to the democratization and scalability of computational genomics.

bioinformatics↗

bioRxiv2020

Single-cell profiling of tuberculosis lung granulomas reveals functional lymphocyte signatures of bacterial control

Mycobacterium tuberculosis lung infection results in a complex multicellular structure, the granuloma. In some granulomas, immune activity promotes bacterial clearance; in others, bacteria persist and grow. We identified correlates of bacterial control in cynomolgus macaque lung granulomas by co-registering longitudinal PET-CT imaging, single-cell RNA-sequencing, and measures of bacterial clearance. We find that bacterial persistence occurs in granulomas enriched for mast, endothelial, fibroblast and plasma cells, signaling amongst themselves via Type II immunity and wound healing pathways. In contrast, these interactions are largely absent in granulomas that drive bacterial control, which are often those that form later in the course of infection; these restrictive lesions are characterized by cellular ecosystems enriched for Type1-Type17, stem-like, and cytotoxic T cells engaged in pro-inflammatory signaling networks that involve diverse myeloid and non-immune cell populations. There is also a temporal aspect to bacterial control, in that granulomas that arise later in infection (in the context of an established immune response) share the functional characteristics of restrictive granulomas and are more capable of killing Mtb. Taken together, our results define the complex multicellular ecosystems underlying (lack of) granuloma resolution and highlight host immune targets that can be leveraged to develop new vaccine and therapeutic strategies for TB. One-Sentence SummaryBacterial control in TB lung granulomas correlates with distinct cellular immune microenvironments and time of formation after infection.

immunology↗

bioRxiv2020

GM-CSF differentiation of human monocytes stabilizes macrophage state via oxidative signaling

Macrophages are central mediators of immunity that integrate diverse signals derived from differentiation cues, tissue location, and disease. Controlling macrophage state and function is an appealing therapeutic objective across many diseases including cancer, atherosclerosis, and tuberculosis. Despite the growing appreciation for the in vivo complexity of macrophage state, existing in vitro models of human monocyte-derived macrophages have used a limited number of individual perturbations to explore the complex phenotypic space that macrophages can occupy. Here, we leverage a tiered differentiation, activation, and stimulation strategy to generate libraries of in vitro monocyte-derived macrophages and examine the in vitro state space of macrophage function using high-dimensional technologies. Our tiered experimental approach further revealed a striking relationship between GM-CSF differentiation and IL-10 production. Cells that were differentiated with GM-CSF produced very low or undetectable levels of IL-10 independent of activation or stimulation condition. To nominate candidate regulators of this IL-10 response, we leverage unbiased single-cell mRNA sequencing to identify transcriptional networks associated with GM-CSF-derived cells. Using these data, we identify oxidative signaling pathways as upregulated in GM-CSF derived cells and demonstrate that scavenging of oxidative radicals can enhance IL-10 production in these cells. Collectively, these data underscore the complexity of monocyte-derived macrophage state over time and highlight a dominant role for GM-CSF in tuning macrophage inflammatory phenotype, metabolic state, and plasticity.

immunology↗

bioRxiv2020

Learning with uncertainty for biological discovery and design

Machine learning that generates biological hypotheses has transformative potential, but most learning algorithms are susceptible to pathological failure when exploring regimes beyond the training data distribution. A solution is to quantify prediction uncertainty so that algorithms can gracefully handle novel phenomena that confound standard methods. Here, we demonstrate the broad utility of robust uncertainty prediction in biological discovery. By leveraging Gaussian process-based uncertainty prediction on modern pretrained features, we train a model on just 72 compounds to make predictions over a 10,833-compound library, identifying and experimentally validating compounds with nanomolar affinity for diverse kinases and whole-cell growth inhibition of Mycobacterium tuberculosis. We show how uncertainty facilitates a tight iterative loop between computation and experimentation, improves the generative design of novel biochemical structures, and generalizes across disparate biological domains. More broadly, our work demonstrates that uncertainty should play a key role in the increasing adoption of machine learning algorithms into the experimental lifecycle.

bioinformatics↗

bioRxiv2020

GWAS reveals the genetic complexity of fructan accumulation patterns in barley grain

We profiled the grain oligosaccharide content of 154 two-row spring barley genotypes and quantified 27 compounds, mainly fructans, that exhibited differential abundance. Clustering revealed two major profile groups where the ‘high’ set contained greater amounts of sugar monomers, sucrose and overall fructans, but lower fructosylraffinose. GWAS identified a significant association for the variability of two fructan types; neoseries-DP7 and inulin-DP9 which showed increased strength when a compound-ratio GWAS was applied. Gene models within this region included five fructan biosynthesis genes, of which three (fructan:fructan 1-fructosyltransferase, sucrose:sucrose 1-fructosyltransferase, and sucrose:fructan 6-fructosyltransferase) have already been described. The remaining two, 6(G)-fructosyltransferase and vacuolar invertase1 have not previously been linked to fructan biosynthesis in barley and showed expression patterns distinct from those of the other three genes, including exclusive expression of 6(G)-fructosyltransferase in outer grain tissues at the storage phase. From exome capture data several SNPs related to inulin- and neoseries-type fructan variability were identified in fructan:fructan 1-fructosyltransferase and 6(G)-fructosyltransferase genes Co-expression analyses uncovered potential regulators of fructan biosynthesis including transcription factors. Our results provide evidence for the distinct biosynthesis of neoseries-type fructans during barley grain maturation plus new gene candidates likely involved in the differential biosynthesis of the various fructan types.Highlight Grain fructan profiles in barley are more complex than previously expected and variations in a diversity panel relate to a genomic region where fructan biosynthesis genes cluster.Abbreviations1-FFTfructan:fructan 1-fructosyltransferase1-SSTsucrose:sucrose 1-fructosyltransferase6-SFTsucrose:fructan 6-fructosyltransferase6G-FFT6(G)-fructosyltransferaseDAPdays after pollinationDPdegree of polymerisationDMdry matterELSDevaporative light scattering detectionFDRfalse discovery rateFOSfructooligosaccharidesFPKMfragments per kilobase, per million mapped readsGWAgenome wide associationGWASgenome wide association studyHAIhours after imbibitionHPAEC–PADhigh pH anion exchange chromatography with pulsed amperometric detectionHPLChigh performance liquid chromatographyKPkestopentaoseKTkestotetraoseLCliquid chromatographyLDlinkage disequilibriumLODlogarithm of oddsMAFminimum allele frequencyMSmass spectrometryNGNeural GasNSneoseries-type fructanPprobability valuePEGpolyethylene glycolQTLquantitative trait lociRFOraffinose family oligosaccharidesRTretention timeSNPsingle nucleotide polymorphismsSPEsolid phase extractionTFAtrifluoroacetic acidTPMtranscripts per millionVI-1vacuolar invertase1View Full Text

plant biology↗

bioRxiv2020

Density-Preserving Data Visualization Unveils Dynamic Patterns of Single-Cell Transcriptomic Variability

Nonlinear data-visualization methods, such as t-SNE and UMAP, have become staple tools for summarizing the complex transcriptomic landscape of single cells in 2D or 3D. However, existing approaches neglect the local density of data points in the original space, often resulting in misleading visualizations where densely populated subpopulations of cells are given more visual space even if they account for only a small fraction of transcriptional diversity within the dataset. We present den-SNE and densMAP, our density-preserving visualization tools based on t-SNE and UMAP, respectively, and demonstrate their ability to facilitate more accurate visual interpretation of single-cell RNA-seq data. On recently published datasets, our methods newly reveal significant changes in transcriptomic variability within a range of biological processes, including cancer, immune cell specialization in human, and the developmental trajectory of C. elegans. Our methods are readily applicable to visualizing high-dimensional data in other scientific domains.

bioinformatics↗

bioRxiv2020

Greenhouse spatial effects detected in the barley (Hordeum vulgare L.) epigenome underlie stochasticity of DNA methylation

Environmental cues are known to alter the methylation profile of genomic DNA, and thereby change the expression of some genes. A proportion of such modifications may become adaptive by adjusting expression of stress response genes but others been shown to be highly stochastic, even under controlled conditions. The influence of environmental flux on plants adds an additional layer of complexity that has potential to confound attempts to interpret interactions between environment, methylome and plant form. We therefore adopt a positional and longitudinal approach to study progressive changes to barley DNA methylation patterns in response to salt exposure during development under greenhouse conditions. Methylation-Sensitive Amplified Polymorphism (MSAP) and phenotypic analyses of nine diverse barley varieties were grown in a randomized plot design, under two salt treatments (0 mM and 75 mM NaCl). Combining environmental, phenotypic and epigenetic data analyses, we show that at least part of the epigenetic variability, previously described as stochastic, is linked to environmental micro-variations during plant growth. Additionally, we show that differences in methylation increase with time of exposure to micro-variations in environment. We propose that subsequent epigenetic studies take into account microclimate-induced epigenetic variability.

plant biology↗

bioRxiv2020

CryoDRGN: Reconstruction of heterogeneous structures from cryo-electron micrographs using neural networks

Cryo-EM single-particle analysis has proven powerful in determining the structures of rigid macromolecules. However, many protein complexes are flexible and can change conformation and composition as a result of functionally-associated dynamics. Such dynamics are poorly captured by current analysis methods. Here, we present cryoDRGN, an algorithm that for the first time leverages the representation power of deep neural networks to efficiently reconstruct highly heterogeneous complexes and continuous trajectories of protein motion. We apply this tool to two synthetic and three publicly available cryo-EM datasets, and we show that cryoDRGN provides an interpretable representation of structural heterogeneity that can be used to identify discrete states as well as continuous conformational changes. This ability enables cryoDRGN to discover previously overlooked structural states and to visualize molecules in motion.

molecular biology↗

bioRxiv2020

Phylogeny and distribution of Y-chromosomal haplotypes in domestic, ancient and wild goats

The male-specific part of the Y-chromosome is in mammalian and many other species the longest haplotype that is inherited without recombination. By its paternal transmission it has a small effective population size in species with dominant males. In several species, Y-chromosomal haplotypes are sensitive markers of population history and introgression. Previous studies have identified in domestic goats four major Y-chromosomal haplotypes Y1A, Y1B, Y2A and Y2B with a marked geographic differentiation and several regional variants. In this study we used published whole-genome sequences of 70 male goats from 16 modern breeds, 11 ancient-DNA samples and 29 samples from seven wild goat species. We identified single-copy male-specific SNPs in four scaffolds, containing SRY, ZFY, DBY with SSX3Y and UTY, and USP9Y with UMN2001, respectively. Phylogenetic analyses indicated haplogroups corresponding to the haplotypes Y1B, Y2A and Y2B, respectively, but Y1A was split into Y1AA and Y1AB. All haplogroups were detected in ancient DNA samples from southeast Europe and, with the exception of Y1AB, in the bezoar goat, which is the wild ancestor of the domestic goats. Combining these data with those of previous studies and with genotypes obtained by Sanger sequencing or the KASP assay yielded haplogroup distributions for 132 domestic breeds or populations. The phylogeographic differentiation indicated paternal population bottlenecks on all three continents. This possibly occurred during the Neolithic introductions of domestic goats to those continents with a particularly strong influence in Europe along the Danubian route. This study illustrates the power of the Y-chromosomal haplotype for the reconstructing the history of mammalian species with a wide geographic range.

genetics↗

bioRxiv2020

A single nucleotide substitution in TaHKT1;5-D controls shoot Na+ accumulation in bread wheat

Improving salinity tolerance in the most widely cultivated cereal, bread wheat (Triticum aestivum L.), is essential to increase grain yields on saline agricultural lands. A Portuguese landrace, Mocho de Espiga Branca accumulates up to 6 folds greater leaf and sheath sodium (Na+) than two Australian cultivars, Gladius and Scout, under salt stress. Despite high leaf and sheath Na+ concentrations, Mocho de Espiga Branca maintained similar salinity tolerance compared to Gladius and Scout. A naturally occurring single nucleotide substitution was identified in the gene encoding a major Na+ transporter TaHKT1;5-D in Mocho de Espiga Branca, which resulted in a L190P amino acid residue variation. This variant prevents Mocho de Espiga Branca from retrieving Na+ from the root xylem leading to a high shoot Na+ concentration. The identification of the tissue tolerant Mocho de Espiga Branca will accelerate the development of more elite salt tolerant bread wheat cultivars.

plant biology↗

bioRxiv2020

Privacy-preserving biomedical database queries with optimal privacy-utility trade-offs

Sharing data across research groups is an essential driver of biomedical research. In particular, biomedical databases with interactive query-answering systems allow users to retrieve information from the database using restricted types of queries (e.g. number of subjects satisfying certain criteria). While these systems aim to facilitate the sharing of aggregate biomedical insights without divulging sensitive individual-level data, they can still leak private information about the individuals in the database through the query answers. Existing strategies to mitigate such risks either provide insufficient levels of privacy or greatly diminish the usefulness of the database. Here, we draw upon recent advances in differential privacy to introduce privacy-preserving query-answering mechanisms for biomedical databases that provably maximize the expected utility of the system while achieving formal privacy guarantees. We demonstrate the accuracy improvement of our methods over existing approaches for a range of use cases, including count, membership, and association queries. Notably, our new theoretical results extend the proof of optimality of the underlying mechanism, previously known only for count queries with symmetric utility functions, to asymmetric utility functions needed for count queries in cohort discovery workflows as well as membership queries--a core functionality of the Beacon Project recently launched by the Global Alliance for Genomics and Health (GA4GH). Our work presents a path towards biomedical query-answering systems that achieve the best privacy-utility trade-offs permitted by the theory of differential privacy.

bioinformatics↗