NMD is required for timely cell fate transitions by fine-tuning gene expression and regulating translation.

Cell fate transitions depend on balanced rewiring of transcription and translation programs to mediate ordered developmental progression. Components of the nonsense-mediated mRNA decay (NMD) pathway have been implicated in regulating embryonic stem cell (ESC) differentiation, but the exact mechanism is unclear. Here we show that NMD controls expression levels of the translation initiation factor Eif4a2 and its premature termination codon-encoding isoform (Eif4a2PTC ). NMD deficiency leads to translation of the truncated eIF4A2PTC protein. eIF4A2PTC elicits increased mTORC1 activity and translation rates and causes differentiation delays. This establishes a previously unknown feedback loop between NMD and translation initiation. Furthermore, our results show a clear hierarchy in the severity of target deregulation and differentiation phenotypes between NMD effector KOs (Smg5 KO > Smg6 KO > Smg7 KO), which highlights heterodimer-independent functions for SMG5 and SMG7. Together, our findings expose an intricate link between mRNA homeostasis and mTORC1 activity that must be maintained for normal dynamics of cell state transitions.

Gruffi: an algorithm for computational removal of stressed cells from brain organoid transcriptomic datasets.

Organoids enable in vitro modeling of complex developmental processes and disease pathologies. Like most 3D cultures, organoids lack sufficient oxygen supply and therefore experience cellular stress. These negative effects are particularly prominent in complex models, such as brain organoids, and can affect lineage commitment. Here, we analyze brain organoid and fetal single-cell RNA sequencing (scRNAseq) data from published and new datasets, totaling about 190,000 cells. We identify a unique stress signature in the data from all organoid samples, but not in fetal samples. We demonstrate that cell stress is limited to a defined subpopulation of cells that is unique to organoids and does not affect neuronal specification or maturation. We have developed a computational algorithm, Gruffi, which uses granular functional filtering to identify and remove stressed cells from any organoid scRNAseq dataset in an unbiased manner. We validated our method using six additional datasets from different organoid protocols and early brains, and show its usefulness to other organoid systems including retinal organoids. Our data show that the adverse effects of cell stress can be corrected by bioinformatic analysis for improved delineation of developmental trajectories and resemblance to in vivo data.

Neutral competition explains the clonal composition of neural organoids.

Neural organoids model the development of the human brain and are an indispensable tool for studying neurodevelopment. Whole-organoid lineage tracing has revealed the number of progenies arising from each initial stem cell to be highly diverse, with lineage sizes ranging from one to more than 20,000 cells. This high variability exceeds what can be explained by existing stochastic models of corticogenesis and indicates the existence of an additional source of stochasticity. To explain this variability, we introduce the SAN model which distinguishes Symmetrically diving, Asymmetrically dividing, and Non-proliferating cells. In the SAN model, the additional source of stochasticity is the survival time of a lineage's pool of symmetrically dividing cells. These survival times result from neutral competition within the sub-population of all symmetrically dividing cells. We demonstrate that our model explains the experimentally observed variability of lineage sizes and derive the quantitative relationship between survival time and lineage size. We also show that our model implies the existence of a regulatory mechanism which keeps the size of the symmetrically dividing cell population constant. Our results provide quantitative insight into the clonal composition of neural organoids and how it arises. This is relevant for many applications of neural organoids, and similar processes may occur in other developing tissues both in vitro and in vivo.

TMT-Opsins differentially modulate medaka brain function in a context-dependent manner.

Vertebrate behavior is strongly influenced by light. Light receptors, encoded by functional opsin proteins, are present inside the vertebrate brain and peripheral tissues. This expression feature is present from fishes to human and appears to be particularly prominent in diurnal vertebrates. Despite their conserved widespread occurrence, the nonvisual functions of opsins are still largely enigmatic. This is even more apparent when considering the high number of opsins. Teleosts possess around 40 opsin genes, present from young developmental stages to adulthood. Many of these opsins have been shown to function as light receptors. This raises the question of whether this large number might mainly reflect functional redundancy or rather maximally enables teleosts to optimally use the complex light information present under water. We focus on tmt-opsin1b and tmt-opsin2, c-opsins with ancestral-type sequence features, conserved across several vertebrate phyla, expressed with partly similar expression in non-rod, non-cone, non-retinal-ganglion-cell brain tissues and with a similar spectral sensitivity. The characterization of the single mutants revealed age- and light-dependent behavioral changes, as well as an impact on the levels of the preprohormone sst1b and the voltage-gated sodium channel subunit scn12aa. The amount of daytime rest is affected independently of the eyes, pineal organ, and circadian clock in tmt-opsin1b mutants. We further focused on daytime behavior and the molecular changes in tmt-opsin1b/2 double mutants, and found that-despite their similar expression and spectral features-these opsins interact in part nonadditively. Specifically, double mutants complement molecular and behavioral phenotypes observed in single mutants in a partly age-dependent fashion. Our work provides a starting point to disentangle the highly complex interactions of vertebrate nonvisual opsins, suggesting that tmt-opsin-expressing cells together with other visual and nonvisual opsins provide detailed light information to the organism for behavioral fine-tuning. This work also provides a stepping stone to unravel how vertebrate species with conserved opsins, but living in different ecological niches, respond to similar light cues and how human-generated artificial light might impact on behavioral processes in natural environments.

SVhound: detection of regions that harbor yet undetected structural variation.

BACKGROUND: Recent population studies are ever growing in number of samples to investigate the diversity of a population or species. These studies reveal new polymorphism that lead to important insights into the mechanisms of evolution, but are also important for the interpretation of these variations. Nevertheless, while the full catalog of variations across entire species remains unknown, we can predict which regions harbor additional not yet detected variations and investigate their properties, thereby enhancing the analysis for potentially missed variants. RESULTS: To achieve this we developed SVhound ( https://github.com/lfpaulin/SVhound ), which based on a population level SVs dataset can predict regions that harbor unseen SV alleles. We tested SVhound using subsets of the 1000 genomes project data and showed that its correlation (average correlation of 2800 tests r = 0.7136) is high to the full data set. Next, we utilized SVhound to investigate potentially missed or understudied regions across 1KGP and CCDG. Lastly we also apply SVhound on a small and novel SV call set for rhesus macaque (Macaca mulatta) and discuss the impact and choice of parameters for SVhound. CONCLUSIONS: SVhound is a unique method to identify potential regions that harbor hidden diversity in model and non model organisms and can also be potentially used to ensure high quality of SV call sets.

ModelRevelator: Fast phylogenetic model estimation via deep learning.

Selecting the best model of sequence evolution for a multiple-sequence-alignment (MSA) constitutes the first step of phylogenetic tree reconstruction. Common approaches for inferring nucleotide models typically apply maximum likelihood (ML) methods, with discrimination between models determined by one of several information criteria. This requires tree reconstruction and optimisation which can be computationally expensive. We demonstrate that neural networks can be used to perform model selection, without the need to reconstruct trees, optimise parameters, or calculate likelihoods. We introduce ModelRevelator, a model selection tool underpinned by two deep neural networks. The first neural network, NNmodelfind, recommends one of six commonly used models of sequence evolution, ranging in complexity from Jukes and Cantor to General Time Reversible. The second, NNalphafind, recommends whether or not a Γ-distributed rate heterogeneous model should be incorporated, and if so, provides an estimate of the shape parameter, ɑ. Users can simply input an MSA into ModelRevelator, and swiftly receive output recommending the evolutionary model, inclusive of the presence or absence of rate heterogeneity, and an estimate of ɑ. We show that ModelRevelator performs comparably with likelihood-based methods and the recently published machine learning method ModelTeller over a wide range of parameter settings, with significant potential savings in computational effort. Further, we show that this performance is not restricted to the alignments on which the networks were trained, but is maintained even on unseen empirical data. We expect that ModelRevelator will provide a valuable alternative for phylogeneticists, especially where traditional methods of model selection are computationally prohibitive.

Critical Growth of Cerebral Tissue in Organoids: Theory and Experiments.

We develop a Fokker-Planck theory of tissue growth with three types of cells (symmetrically dividing, asymmetrically dividing, and nondividing) as main agents to study the growth dynamics of human cerebral organoids. Fitting the theory to lineage tracing data obtained in next generation sequencing experiments, we show that the growth of cerebral organoids is a critical process. We derive analytical expressions describing the time evolution of clonal lineage sizes and show how power-law distributions arise in the limit of long times due to the vanishing of a characteristic growth scale. We discuss that the independence of critical growth on initial conditions could be biologically advantageous.

Complex Evolution of Light-Dependent Protochlorophyllide Oxidoreductases in Aerobic Anoxygenic Phototrophs: Origin, Phylogeny, and Function.

Light-dependent protochlorophyllide oxidoreductase (LPOR) and dark-operative protochlorophyllide oxidoreductase are evolutionary and structurally distinct enzymes that are essential for the synthesis of (bacterio)chlorophyll, the primary pigment needed for both anoxygenic and oxygenic photosynthesis. In contrast to the long-held hypothesis that LPORs are only present in oxygenic phototrophs, we recently identified a functional LPOR in the aerobic anoxygenic phototrophic bacterium (AAPB) Dinoroseobacter shibae and attributed its presence to a single horizontal gene transfer event from cyanobacteria. Here, we provide evidence for the more widespread presence of genuine LPOR enzymes in AAPBs. An exhaustive bioinformatics search identified 36 putative LPORs outside of oxygenic phototrophic bacteria (cyanobacteria) with the majority being AAPBs. Using in vitro and in vivo assays, we show that the large majority of the tested AAPB enzymes are genuine LPORs. Solution structural analyses, performed for two of the AAPB LPORs, revealed a globally conserved structure when compared with a well-characterized cyanobacterial LPOR. Phylogenetic analyses suggest that LPORs were transferred not only from cyanobacteria but also subsequently between proteobacteria and from proteobacteria to Gemmatimonadetes. Our study thus provides another interesting example for the complex evolutionary processes that govern the evolution of bacteria, involving multiple horizontal gene transfer events that likely occurred at different time points and involved different donors.

The genomic basis of circadian and circalunar timing adaptations in a midge.

Organisms use endogenous clocks to anticipate regular environmental cycles, such as days and tides. Natural variants resulting in differently timed behaviour or physiology, known as chronotypes in humans, have not been well characterized at the molecular level. We sequenced the genome of Clunio marinus, a marine midge whose reproduction is timed by circadian and circalunar clocks. Midges from different locations show strain-specific genetic timing adaptations. We examined genetic variation in five C. marinus strains from different locations and mapped quantitative trait loci for circalunar and circadian chronotypes. The region most strongly associated with circadian chronotypes generates strain-specific differences in the abundance of calcium/calmodulin-dependent kinase II.1 (CaMKII.1) splice variants. As equivalent variants were shown to alter CaMKII activity in Drosophila melanogaster, and C. marinus (Cma)-CaMKII.1 increases the transcriptional activity of the dimer of the circadian proteins Cma-CLOCK and Cma-CYCLE, we suggest that modulation of alternative splicing is a mechanism for natural adaptation in circadian timing.

Distinguishing Felsenstein Zone from Farris Zone Using Neural Networks.

Maximum likelihood and maximum parsimony are two key methods for phylogenetic tree reconstruction. Under certain conditions, each of these two methods can perform more or less efficiently, resulting in unresolved or disputed phylogenies. We show that a neural network can distinguish between four-taxon alignments that were evolved under conditions susceptible to either long-branch attraction or long-branch repulsion. When likelihood and parsimony methods are discordant, the neural network can provide insight as to which tree reconstruction method is best suited to the alignment. When applied to the contentious case of Strepsiptera evolution, our method shows robust support for the current scientific view, that is, it places Strepsiptera with beetles, distant from flies.

IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era.

IQ-TREE (http://www.iqtree.org, last accessed February 6, 2020) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have continuously expanded IQ-TREE to integrate a plethora of new models of sequence evolution and efficient computational approaches of phylogenetic inference to deal with genomic data. Here, we describe notable features of IQ-TREE version 2 and highlight the key advantages over other software.

Accurate detection of complex structural variations using single-molecule sequencing.

Structural variations are the greatest source of genetic variation, but they remain poorly understood because of technological limitations. Single-molecule long-read sequencing has the potential to dramatically advance the field, although high error rates are a challenge with existing methods. Addressing this need, we introduce open-source methods for long-read alignment (NGMLR; https://github.com/philres/ngmlr ) and structural variant identification (Sniffles; https://github.com/fritzsedlazeck/Sniffles ) that provide unprecedented sensitivity and precision for variant detection, even in repeat-rich regions and for complex nested events that can have substantial effects on human health. In several long-read datasets, including healthy and cancerous human genomes, we discovered thousands of novel variants and categorized systematic errors in short-read approaches. NGMLR and Sniffles can automatically filter false events and operate on low-coverage data, thereby reducing the high costs that have hindered the application of long reads in clinical and research settings.

Timing strains of the marine insect Clunio marinus diverged and persist with gene flow.

Genetic divergence of populations in the presence of gene flow is a central theme in speciation research. Theory predicts that divergence can happen with full range overlap - in sympatry - driven by ecological factors, but there are few empirical examples of how ecologically divergent selection can overcome gene flow and lead to reproductive isolation. In the marine midge Clunio marinus (Diptera: Chironomidae) reproduction is ecologically restricted to the time of the lowest tides, which is ensured through accurate control of development and adult emergence by circalunar and circadian clocks. As tidal regimes differ along the coastline, locally adapted timing strains of C. marinus are found in different sites across Europe. At the same time, ecologically suitable low tides occur at both full and new moon and twice a day, providing C. marinus with four nonoverlapping temporal niches at every geographic location. Along the coast of Brittany, which is characterized by a steep gradient in timing of the tides, we found an unusually large number of differentially adapted timing strains, and the first known instances of sympatric C. marinus strains occupying divergent temporal niches. Analysis of mitochondrial genotypes suggests that these timing strains originated from a single recent colonization event. Nuclear genotypes show strong gene flow, sympatric timing strains being the least differentiated. Even when sympatric strains exist in nonoverlapping temporal niches, timing adaptations do not result in genome-wide genetic divergence, suggesting timing adaptations are maintained by permanent ecological selection. This constitutes a model case for incipient ecological divergence with gene flow.

In vivo insertion pool sequencing identifies virulence factors in a complex fungal-host interaction.

Large-scale insertional mutagenesis screens can be powerful genome-wide tools if they are streamlined with efficient downstream analysis, which is a serious bottleneck in complex biological systems. A major impediment to the success of next-generation sequencing (NGS)-based screens for virulence factors is that the genetic material of pathogens is often underrepresented within the eukaryotic host, making detection extremely challenging. We therefore established insertion Pool-Sequencing (iPool-Seq) on maize infected with the biotrophic fungus U. maydis. iPool-Seq features tagmentation, unique molecular barcodes, and affinity purification of pathogen insertion mutant DNA from in vivo-infected tissues. In a proof of concept using iPool-Seq, we identified 28 virulence factors, including 23 that were previously uncharacterized, from an initial pool of 195 candidate effector mutants. Because of its sensitivity and quantitative nature, iPool-Seq can be applied to any insertional mutagenesis library and is especially suitable for genetically complex setups like pooled infections of eukaryotic hosts.

Polymorphism-Aware Species Trees with Advanced Mutation Models, Bootstrap, and Rate Heterogeneity.

Molecular phylogenetics has neglected polymorphisms within present and ancestral populations for a long time. Recently, multispecies coalescent based methods have increased in popularity, however, their application is limited to a small number of species and individuals. We introduced a polymorphism-aware phylogenetic model (PoMo), which overcomes this limitation and scales well with the increasing amount of sequence data whereas accounting for present and ancestral polymorphisms. PoMo circumvents handling of gene trees and directly infers species trees from allele frequency data. Here, we extend the PoMo implementation in IQ-TREE and integrate search for the statistically best-fit mutation model, the ability to infer mutation rate variation across sites, and assessment of branch support values. We exemplify an analysis of a hundred species with ten haploid individuals each, showing that PoMo can perform inference on large data sets. While PoMo is more accurate than standard substitution models applied to concatenated alignments, it is almost as fast. We also provide bmm-simulate, a software package that allows simulation of sequences evolving under PoMo. The new options consolidate the value of PoMo for phylogenetic analyses with population data.

Species Identification and Antibiotic Resistance Prediction by Analysis of Whole-Genome Sequence Data by Use of ARESdb: an Analysis of Isolates from the Unyvero Lower Respiratory Tract Infection Trial.

Whole-genome sequencing (WGS) is now routinely performed in clinical microbiology laboratories to assess isolate relatedness. With appropriately developed analytics, the same data can be used for prediction of antimicrobial susceptibility. We assessed WGS data for identification using open-source tools and antibiotic susceptibility testing (AST) prediction using ARESdb compared to matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) identification and broth microdilution phenotypic susceptibility testing on clinical isolates from a multicenter clinical trial of the FDA-cleared Unyvero lower respiratory tract infection (LRTI) application (Curetis). For the trial, more than 2,000 patient samples were collected from intensive care units across nine hospitals and tested for LRTI. The isolate subset used in this study included 620 clinical isolates originating from 455 LRTI culture-positive patient samples. Isolates were sequenced using the Illumina Nextera XT protocol and FASTQ files with raw reads uploaded to the ARESdb cloud platform (ares-genetics.cloud; released for research use in 2020). The platform combines Ares Genetics' proprietary database ARESdb with state-of-the-art bioinformatics tools and curated public data. For identification, WGS showed 99 and 93% concordance with MALDI-TOF MS at the genus and species levels, respectively. WGS-predicted susceptibility showed 89% categorical agreement with phenotypic susceptibility across a total of 129 species-compound pairs analyzed, with categorical agreement exceeding 90% in 78 species-compound pairs and reaching 100% in 32. Results of this study add to the growing body of literature showing that, with improvement of analytics, WGS data could be used to predict antimicrobial susceptibility.

ModelFinder: fast model selection for accurate phylogenetic estimates.

Model-based molecular phylogenetics plays an important role in comparisons of genomic data, and model selection is a key step in all such analyses. We present ModelFinder, a fast model-selection method that greatly improves the accuracy of phylogenetic estimates by incorporating a model of rate heterogeneity across sites not previously considered in this context and by allowing concurrent searches of model space and tree space.

Thiol-linked alkylation of RNA to assess expression dynamics.

Gene expression profiling by high-throughput sequencing reveals qualitative and quantitative changes in RNA species at steady state but obscures the intracellular dynamics of RNA transcription, processing and decay. We developed thiol(SH)-linked alkylation for the metabolic sequencing of RNA (SLAM seq), an orthogonal-chemistry-based RNA sequencing technology that detects 4-thiouridine (s4U) incorporation in RNA species at single-nucleotide resolution. In combination with well-established metabolic RNA labeling protocols and coupled to standard, low-input, high-throughput RNA sequencing methods, SLAM seq enabled rapid access to RNA-polymerase-II-dependent gene expression dynamics in the context of total RNA. We validated the method in mouse embryonic stem cells by showing that the RNA-polymerase-II-dependent transcriptional output scaled with Oct4/Sox2/Nanog-defined enhancer activity, and we provide quantitative and mechanistic evidence for transcript-specific RNA turnover mediated by post-transcriptional gene regulatory pathways initiated by microRNAs and N6-methyladenosine. SLAM seq facilitates the dissection of fundamental mechanisms that control gene expression in an accessible, cost-effective and scalable manner.

Large-scale assessment of antimicrobial resistance marker databases for genetic phenotype prediction: a systematic review.

BACKGROUND: Antimicrobial resistance (AMR) is a rising health threat with 10 million annual casualties estimated by 2050. Appropriate treatment of infectious diseases with the right antibiotics reduces the spread of antibiotic resistance. Today, clinical practice relies on molecular and PCR techniques for pathogen identification and culture-based antibiotic susceptibility testing (AST). Recently, WGS has started to transform clinical microbiology, enabling prediction of resistance phenotypes from genotypes and allowing for more informed treatment decisions. WGS-based AST (WGS-AST) depends on the detection of AMR markers in sequenced isolates and therefore requires AMR reference databases. The completeness and quality of these databases are material to increase WGS-AST performance. METHODS: We present a systematic evaluation of the performance of publicly available AMR marker databases for resistance prediction on clinical isolates. We used the public databases CARD and ResFinder with a final dataset of 2587 isolates across five clinically relevant pathogens from PATRIC and NDARO, public repositories of antibiotic-resistant bacterial isolates. RESULTS: CARD and ResFinder WGS-AST performance had an overall balanced accuracy of 0.52 (±0.12) and 0.66 (±0.18), respectively. Major error rates were higher in CARD (42.68%) than ResFinder (25.06%). However, CARD showed almost no very major errors (1.17%) compared with ResFinder (4.42%). CONCLUSIONS: We show that AMR databases need further expansion, improved marker annotations per antibiotic rather than per antibiotic class and validated multivariate marker panels to achieve clinical utility, e.g. in order to meet performance requirements such as provided by the FDA for clinical microbiology diagnostic testing.

Structure of the space of taboo-free sequences.

Models of sequence evolution typically assume that all sequences are possible. However, restriction enzymes that cut DNA at specific recognition sites provide an example where carrying a recognition site can be lethal. Motivated by this observation, we studied the set of strings over a finite alphabet with taboos, that is, with prohibited substrings. The taboo-set is referred to as [Formula: see text] and any allowed string as a taboo-free string. We consider the so-called Hamming graph [Formula: see text], whose vertices are taboo-free strings of length n and whose edges connect two taboo-free strings if their Hamming distance equals one. Any (random) walk on this graph describes the evolution of a DNA sequence that avoids taboos. We describe the construction of the vertex set of [Formula: see text]. Then we state conditions under which [Formula: see text] and its suffix subgraphs are connected. Moreover, we provide an algorithm that determines if all these graphs are connected for an arbitrary [Formula: see text]. As an application of the algorithm, we show that about [Formula: see text] of bacteria listed in REBASE have a taboo-set that induces connected taboo-free Hamming graphs, because they have less than four type II restriction enzymes. On the other hand, four properly chosen taboos are enough to disconnect one suffix subgraph, and consequently connectivity of taboo-free Hamming graphs could change depending on the composition of restriction sites.