Adolescents flexibly adapt action selection based on controllability inferences

From early in life, we encounter both controllable environments, in which our actions can causally influence the reward outcomes we experience, and uncontrollable environments, in which they cannot. Environmental controllability is theoretically proposed to organize our behavior. In controllable contexts, we can learn to proactively select instrumental actions that bring about desired outcomes. In uncontrollable environments, Pavlovian learning enables hard-wired, reflexive reactions to anticipated, motivationally salient events, providing "default" behavioral responses. Previous studies characterizing the balance between Pavlovian and instrumental learning systems across development have yielded divergent findings, with some studies observing heightened expression of Pavlovian learning during adolescence and others observing a reduced influence of Pavlovian learning during this developmental stage. In this study, we aimed to investigate whether a theoretical model of controllability-dependent arbitration between learning systems might explain these seemingly divergent findings in the developmental literature, with the specific hypothesis that adolescents' action selection might be particularly sensitive to environmental controllability. To test this hypothesis, 90 participants, aged 8-27, performed a probabilistic-learning task that enables estimation of Pavlovian influence on instrumental learning, across both controllable and uncontrollable conditions. We fit participants' data with a reinforcement-learning model in which controllability inferences adaptively modulate the dominance of Pavlovian versus instrumental control. Relative to children and adults, adolescents exhibited greater flexibility in calibrating the expression of Pavlovian bias to the degree of environmental controllability. These findings suggest that sensitivity to environmental reward statistics that organize motivated behavior may be heightened during adolescence.

Analysis of programmed frameshifting during translation of prfB in Flavobacterium johnsoniae

Ribosomes of Bacteroidia fail to recognize Shine-Dalgarno (SD) sequences due to sequestration of the 3' tail of the 16S rRNA on the 30S platform. Yet in these organisms, the prfB gene typically contains the programmed +1 frameshift site with its characteristic SD sequence. Here, we investigate prfB autoregulation in Flavobacterium johnsoniae, a member of the Bacteroidia. We find that the efficiency of prfB frameshifting in F. johnsoniae is low (∼7%) relative to that in Escherichia coli (∼50%). Mutation or truncation of bS21 in F. johnsoniae increases frameshifting substantially, suggesting that anti-SD (ASD) sequestration is responsible for the reduced efficiency. The frameshift site of certain Flavobacteriales, such as Winogradskyella psychrotolerans, has no SD. In F. johnsoniae, this W. psychrotolerans sequence supports frameshifting as well as the native sequence, and mutation of bS21 causes no enhancement. These data suggest that prfB frameshifting normally occurs without SD-ASD pairing, at least under optimal laboratory growth conditions. Chromosomal mutations that remove the frameshift or ablate the SD confer subtle growth defects in the presence of paraquat or streptomycin, respectively, indicating that both the autoregulatory mechanism and the SD element contribute to F. johnsoniae cell fitness. Analysis of prfB frameshift sites across 2686 representative bacteria shows loss of the SD sequence in many clades, with no obvious relationship to genome-wide SD usage. These data reveal unexpected variation in the mechanism of frameshifting and identify another group of organisms, the Verrucomicrobiales, that globally lack SD sequences.

Effector-triggered and self-regulated plant resistance to insects

Despite many years of research, the molecular mechanisms underlying the activation and regulation of host plant resistance (HPR) to insects remain elusive. Recently, Guo et al. reported that a nucleotide-binding leucine-rich repeat NLR protein activates HPR through direct recognition of an insect effector and that autophagy-mediated degradation of this effector negatively regulates HPR.

A nonneural miRNA cluster mediates hearing via repression of two neural targets

We show here that mir-279/996 are absolutely essential for development and function of Johnston's organ (JO), the primary proprioceptive and auditory organ in Drosophila Their deletion results in highly aberrant cell fate determination, including loss of scolopale cells and ectopic neurons, and mutants are electrophysiologically deaf. In vivo activity sensors and mosaic analyses indicate that these seed-related miRNAs function autonomously to suppress neural fate in nonneuronal cells. Finally, genetic interactions pinpoint two neural targets (elav and insensible) that underlie miRNA mutant JO phenotypes. This work uncovers how critical post-transcriptional regulation of specific miRNA targets governs cell specification and function of the auditory system.

Posttranscriptional modification to the core of tRNAs modulates translational misreading errors

Protein synthesis on the ribosome involves successive rapid recruitment of cognate aminoacyl-tRNAs and rejection of the much more numerous incorrect near- or non-cognates. The principal feature of translation elongation is that at every step, many incorrect aa-tRNAs unsuccessfully enter the A site for each cognate accepted. Normal levels of translational accuracy require that cognate tRNAs have relatively similar acceptance rates by the ribosome. To achieve that, tRNAs evolved to compensate for differences in amino acid properties and codon-anticodon strength that affect acceptance. Part of that response involved tRNA posttranscriptional modifications, which can affect tRNA decoding efficiency, accuracy, and structural stability. The most intensively modified regions of the tRNA are the anticodon loop and structural core of the tRNA. Anticodon loop modifications directly affect codon-anticodon pairing and therefore modulate accuracy. Core modifications have been thought to ensure consistent decoding rates principally by stabilizing tRNA structure to avoid degradation; however, degradation due to instability appears to only be a significant issue above normal growth temperatures. We suspected that the greater role of modification at normal temperatures might be to tune tRNAs to maintain consistent intrinsic rates of acceptance and peptide transfer and that hypomodification by altering these rates might degrade the process of discrimination, leading to increased translational errors. Here, we present evidence that most tRNA core modifications do modulate the frequency of misreading errors, suggesting that the need to maintain accuracy explains their deep evolutionary conservation.

Using long-read CAGE sequencing to profile cryptic-promoter-derived transcripts and their contribution to the immunopeptidome

Recent studies have shown that the noncoding genome can produce unannotated proteins as antigens that induce immune response. One major source of this activity is the aberrant epigenetic reactivation of transposable elements (TEs). In tumors, TEs often provide cryptic or alternate promoters, which can generate transcripts that encode tumor-specific unannotated proteins. Thus, TE-derived transcripts (TE transcripts) have the potential to produce tumor-specific, but recurrent, antigens shared among many tumors. Identification of TE-derived tumor antigens holds the promise to improve cancer immunotherapy approaches; however, current genomics and computational tools are not optimized for their detection. Here we combined CAGE technology with full-length long-read transcriptome sequencing (long-read CAGE, or LRCAGE) and developed a suite of computational tools to significantly improve immunopeptidome detection by incorporating TE and other tumor transcripts into the proteome database. By applying our methods to human lung cancer cell line H1299 data, we show that long-read technology significantly improves mapping of promoters with low mappability scores and that LRCAGE guarantees accurate construction of uncharacterized 5' transcript structure. Augmenting a reference proteome database with newly characterized transcripts enabled us to detect noncanonical antigens from HLA-pulldown LC-MS/MS data. Lastly, we show that epigenetic treatment increased the number of noncanonical antigens, particularly those encoded by TE transcripts, which might expand the pool of targetable antigens for cancers with low mutational burden.

miRNA-like secondary structures in maize (Zea mays) genes and transposable elements correlate with small RNAs, methylation, and expression

RNA molecules carry information in their primary sequence and also their secondary structure. Secondary structure can confer important functional information, but it is also a signal for an RNAi-like host epigenetic response mediated by small RNAs (smRNAs). In this study, we used two bioinformatic methods to predict local secondary structures across features of the maize genome, focusing on small regions that had similar folding properties to pre-miRNA loci. We found miRNA-like secondary structures to be common in genes and most, but not all, superfamilies of RNA and DNA transposable elements (TEs). The miRNA-like regions map to a higher diversity of smRNAs than regions without miRNA-like structure, explaining up to 27% of variation in smRNA mapping for some TE superfamilies. This mapping bias is more pronounced among putatively autonomous TEs relative to nonautonomous TEs. Genome-wide, miRNA-like regions are also associated with elevated methylation levels, particularly in the CHH context. Among genes, those with miRNA-like secondary structure are 1.5-fold more highly expressed, on average, than other genes. However, these genes are also more variably expressed across the 26 nested association mapping founder lines, and this variability positively correlates with the number of mapping smRNAs. We conclude that local miRNA-like structures are a nearly ubiquitous feature of expressed regions of the maize genome, that they correlate with higher smRNA mapping and methylation, and that they may represent a trade-off between functional requirements and the potentially negative consequences of smRNA production.

Small-molecule Ro-08-2750 interacts with many RNA-binding proteins and elicits MUSASHI2-independent phenotypes

RNA-binding proteins (RBPs) are key regulators of gene expression. Small molecules targeting these RBP-RNA interactions are a rapidly emerging class of therapeutics for treating a variety of diseases. Ro-08-2750 (Ro) is a small molecule identified as a competitive inhibitor of Musashi (MSI)-RNA interactions. Here, we show that multiple Ro-dependent cellular phenotypes, specifically adrenocortical steroid production and cell viability, are Musashi-2 (MSI2)-independent. Using an unbiased proteome-wide approach, we discovered Ro broadly interacts with RBPs, many containing RRM domains. To confirm this finding, we leveraged the large-scale ENCODE data to identify a subset of RBPs whose depletion phenocopies Ro inhibition, indicating Ro is a promiscuous inhibitor of multiple RBPs. Consistent with broad disruption of ribonucleoprotein complexes, Ro treatment leads to stress granule formation. This strategy represents a generalizable framework for validating the specificity and identifying targets of RBP inhibitors in a cellular context.

Infernape uncovers cell type-specific and spatially resolved alternative polyadenylation in the brain

Differential polyadenylation sites (PAs) critically regulate gene expression, but their cell type-specific usage and spatial distribution in the brain have not been systematically characterized. Here, we present Infernape, which infers and quantifies PA usage from single-cell and spatial transcriptomic data and show its application in the mouse brain. Infernape uncovers alternative intronic PAs and 3'-UTR lengthening during cortical neurogenesis. Progenitor-neuron comparisons in the excitatory and inhibitory neuron lineages show overlapping PA changes in embryonic brains, suggesting that the neural proliferation-differentiation axis plays a prominent role. In the adult mouse brain, we uncover cell type-specific PAs and visualize such events using spatial transcriptomic data. Over two dozen neurodevelopmental disorder-associated genes such as Csnk2a1 and Mecp2 show differential PAs during brain development. This study presents Infernape to identify PAs from scRNA-seq and spatial data, and highlights the role of alternative PAs in neuronal gene regulation.

Analyses of 600+ insect genomes reveal repetitive element dynamics and highlight biodiversity-scale repeat annotation challenges

Repetitive elements (REs) are integral to the composition, structure, and function of eukaryotic genomes, yet remain understudied in most taxonomic groups. We investigated REs across 601 insect species and report wide variation in RE dynamics across groups. Analysis of associations between REs and protein-coding genes revealed dynamic evolution at the interface between REs and coding regions across insects, including notably elevated RE-gene associations in lineages with abundant long interspersed nuclear elements (LINEs). We leveraged this large, empirical data set to quantify impacts of long-read technology on RE detection and investigate fundamental challenges to RE annotation in diverse groups. In long-read assemblies, we detected ∼36% more REs than short-read assemblies, with long terminal repeats (LTRs) showing 162% increased detection, whereas DNA transposons and LINEs showed less respective technology-related bias. In most insect lineages, 25%-85% of repetitive sequences were "unclassified" following automated annotation, compared with only ∼13% in Drosophila species. Although the diversity of available insect genomes has rapidly expanded, we show the rate of community contributions to RE databases has not kept pace, preventing efficient annotation and high-resolution study of REs in most groups. We highlight the tremendous opportunity and need for the biodiversity genomics field to embrace REs and suggest collective steps for making progress toward this goal.

Developmental changes in retention and generalization of nonadjacent dependencies over a period containing sleep in 18-mo-old infants

Sleep promotes the stabilization of memories in adulthood, with a growing literature on the benefits of sleep for memory in infants and children. In two studies, we examined the role of sleep in the retention and generalization of nonadjacent dependencies (NADs; e.g., a-X-b/c-X-d phrases) in an artificial language. Previously, a study demonstrated that over a delay of 4 h, 15 mo olds who nap after training retain a general memory of the NAD rule instead of memory for specific NADs heard during training. In experiment 1, we designed a replication of the nap condition used in the earlier study but tested 18-mo-old infants. Infants of this age retained veridical memory for specific NADs over a delay containing sleep, providing preliminary evidence of the development of memory processes (experiment 1). In experiment 2, we tested 18 mo olds' ability to generalize the NAD to new vocabulary, finding only infants who napped after training generalized their knowledge of the pattern to completely novel phrases. Overall, by 18 mo of age, children retain specific memories over a period containing sleep, and sleep promotes abstract memories to a greater extent than wakefulness.

The influence of encoding strategy on associative memory consolidation across wake and sleep

Sleep benefits memory consolidation. However, factors present at initial encoding may moderate this effect. Here, we examined the role that encoding strategy plays in subsequent memory consolidation during sleep. Eighty-nine participants encoded pairs of words using two different strategies. Each participant encoded half of the word pairs using an integrative visualization technique, where the two items were imagined in an integrated scene. The other half were encoded nonintegratively, with each word pair item visualized separately. Memory was tested before and after a period of nocturnal sleep (N = 47) or daytime wake (N = 42) via cued recall tests. Immediate memory performance was significantly better for word pairs encoded using the integrative strategy compared with the nonintegrative strategy (P < 0.001). When looking at the change in recall across the delay, there was significantly less forgetting of integrated word pairs across a night of sleep compared with a day spent awake (P < 0.001), with no significant difference in the nonintegrated pairs (P = 0.19). This finding was driven by more forgetting of integrated compared with not-integrated pairs across the wake delay (P < 0.001), whereas forgetting was equivalent across the sleep delay (P = 0.26). Together, these results show that the strategy engaged in during encoding impacts both the immediate retention of memories and their subsequent consolidation across sleep and wake intervals.

Copy number variation alters local and global mutational tolerance

Copy number variants (CNVs), duplications and deletions of genomic sequences, contribute to evolutionary adaptation but can also confer deleterious effects and cause disease. Whereas the effects of amplifying individual genes or whole chromosomes (i.e., aneuploidy) have been studied extensively, much less is known about the genetic and functional effects of CNVs of differing sizes and structures. Here, we investigated Saccharomyces cerevisiae (yeast) strains that acquired adaptive CNVs of variable structures and copy numbers following experimental evolution in glutamine-limited chemostats. Although beneficial in the selective environment, CNVs result in decreased fitness compared with the euploid ancestor in rich media. We used transposon mutagenesis to investigate mutational tolerance and genome-wide genetic interactions in CNV strains. We find that CNVs increase mutational target size, confer increased mutational tolerance in amplified essential genes, and result in novel genetic interactions with unlinked genes. We validated a novel genetic interaction between different CNVs and BMH1 that was common to multiple strains. We also analyzed global gene expression and found that transcriptional dosage compensation does not affect most genes amplified by CNVs, although gene-specific transcriptional dosage compensation does occur for ∼12% of amplified genes. Furthermore, we find that CNV strains do not show previously described transcriptional signatures of aneuploidy. Our study reveals the extent to which local and global mutational tolerance is modified by CNVs with implications for genome evolution and CNV-associated diseases, such as cancer.

Efficient mapping of accurate long reads in minimizer space with mapquik

DNA sequencing data continue to progress toward longer reads with increasingly lower sequencing error rates. We focus on the critical problem of mapping, or aligning, low-divergence sequences from long reads (e.g., Pacific Biosciences [PacBio] HiFi) to a reference genome, which poses challenges in terms of accuracy and computational resources when using cutting-edge read mapping approaches that are designed for all types of alignments. A natural idea would be to optimize efficiency with longer seeds to reduce the probability of extraneous matches; however, contiguous exact seeds quickly reach a sensitivity limit. We introduce mapquik, a novel strategy that creates accurate longer seeds by anchoring alignments through matches of k consecutively sampled minimizers (k-min-mers) and only indexing k-min-mers that occur once in the reference genome, thereby unlocking ultrafast mapping while retaining high sensitivity. We show that mapquik significantly accelerates the seeding and chaining steps-fundamental bottlenecks to read mapping-for both the human and maize genomes with [Formula: see text] sensitivity and near-perfect specificity. On the human genome, for both real and simulated reads, mapquik achieves a [Formula: see text] speedup over the state-of-the-art tool minimap2, and on the maize genome, mapquik achieves a [Formula: see text] speedup over minimap2, making mapquik the fastest mapper to date. These accelerations are enabled from not only minimizer-space seeding but also a novel heuristic [Formula: see text] pseudochaining algorithm, which improves upon the long-standing [Formula: see text] bound. Minimizer-space computation builds the foundation for achieving real-time analysis of long-read sequencing data.

Exploratory Multisite MR Spectroscopic Imaging Shows White Matter Neuroaxonal Loss Associated with Complications of Type 1 Diabetes in Children

BACKGROUND AND PURPOSE

Type 1 diabetes affects over 200,000 children in the United States and is associated with an increased risk of cognitive dysfunction. Prior single-site, single-voxel MRS case reports and studies have identified associations between reduced NAA/Cr, a marker of neuroaxonal loss, and type 1 diabetes. However, NAA/Cr differences among children with various disease complications or across different brain tissues remain unclear. To better understand this phenomenon and the role of MRS in characterizing it, we conducted a multisite pilot study.

MATERIALS AND METHODS

In 25 children, 6-14 years of age, with type 1 diabetes across 3 sites, we acquired T1WI and axial 2D MRSI along with phantom studies to calibrate scanner effects. We quantified tissue-weighted NAA/Cr in WM and deep GM and modeled them against study covariates.

RESULTS

We found that MRSI differentiated WM and deep GM by NAA/Cr on the individual level. On the population level, we found significant negative associations of WM NAA/Cr with chronic hyperglycemia quantified by hemoglobin A1c (P < .005) and a history of diabetic ketoacidosis at disease onset (P < .05). We found a statistical interaction (P < .05) between A1c and ketoacidosis, suggesting that neuroaxonal loss from ketoacidosis may outweigh that from poor glucose control. These associations were not present in deep GM.

CONCLUSIONS

Our pilot study suggests that MRSI differentiates GM and WM by NAA/Cr in this population, disease complications may lead to neuroaxonal loss in WM in children, and deeper investigation is warranted to further untangle how diabetic ketoacidosis and chronic hyperglycemia affect brain health and cognition in type 1 diabetes.

Modeling and predicting cancer clonal evolution with reinforcement learning

Cancer results from an evolutionary process that typically yields multiple clones with varying sets of mutations within the same tumor. Accurately modeling this process is key to understanding and predicting cancer evolution. Here, we introduce clone to mutation (CloMu), a flexible and low-parameter tree generative model of cancer evolution. CloMu uses a two-layer neural network trained via reinforcement learning to determine the probability of new mutations based on the existing mutations on a clone. CloMu supports several prediction tasks, including the determination of evolutionary trajectories, tree selection, causality and interchangeability between mutations, and mutation fitness. Importantly, previous methods support only some of these tasks, and many suffer from overfitting on data sets with a large number of mutations. Using simulations, we show that CloMu either matches or outperforms current methods on a wide variety of prediction tasks. In particular, for simulated data with interchangeable mutations, current methods are unable to uncover causal relationships as effectively as CloMu. On breast cancer and leukemia cohorts, we show that CloMu determines similarities and causal relationships between mutations as well as the fitness of mutations. We validate CloMu's inferred mutation fitness values for the leukemia cohort by comparing them to clonal proportion data not used during training, showing high concordance. In summary, CloMu's low-parameter model facilitates a wide range of prediction tasks regarding cancer evolution on increasingly available cohort-level data sets.

CST-Polα/Primase: the second telomere maintenance machine

It has been known for decades that telomerase extends the 3' end of linear eukaryotic chromosomes and dictates the telomeric repeat sequence based on the template in its RNA. However, telomerase does not mitigate sequence loss at the 5' ends of chromosomes, which results from lagging strand DNA synthesis and nucleolytic processing. Therefore, a second enzyme is needed to keep telomeres intact: DNA polymerase α/Primase bound to Ctc1-Stn1-Ten1 (CST). CST-Polα/Primase maintains telomeres through a fill-in reaction that replenishes the lost sequences at the 5' ends. CST not only serves to maintain telomeres but also determines their length by keeping telomerase from overelongating telomeres. Here we discuss recent data on the evolution, structure, function, and recruitment of mammalian CST-Polα/Primase, highlighting the role of this complex and telomere length control in human disease.

Deriving confidence intervals for mutation rates across a wide range of evolutionary distances using FracMinHash

Sketching methods offer computational biologists scalable techniques to analyze data sets that continue to grow in size. MinHash is one such technique to estimate set similarity that has enjoyed recent broad application. However, traditional MinHash has previously been shown to perform poorly when applied to sets of very dissimilar sizes. FracMinHash was recently introduced as a modification of MinHash to compensate for this lack of performance when set sizes differ. This approach has been successfully applied to metagenomic taxonomic profiling in the widely used tool sourmash gather. Although experimental evidence has been encouraging, FracMinHash has not yet been analyzed from a theoretical perspective. In this paper, we perform such an analysis to derive various statistics of FracMinHash, and prove that although FracMinHash is not unbiased (in the sense that its expected value is not equal to the quantity it attempts to estimate), this bias is easily corrected for both the containment and Jaccard index versions. Next, we show how FracMinHash can be used to compute point estimates as well as confidence intervals for evolutionary mutation distance between a pair of sequences by assuming a simple mutation model. We also investigate edge cases in which these analyses may fail to effectively warn the users of FracMinHash indicating the likelihood of such cases. Our analyses show that FracMinHash estimates the containment of a genome in a large metagenome more accurately and more precisely compared with traditional MinHash, and the point estimates and confidence intervals perform significantly better in estimating mutation distances.

Genomic sketching with multiplicities and locality-sensitive hashing using Dashing 2

A genomic sketch is a small, probabilistic representation of the set of k-mers in a sequencing data set. Sketches are building blocks for large-scale analyses that consider similarities between many pairs of sequences or sequence collections. Although existing tools can easily compare tens of thousands of genomes, data sets can reach millions of sequences and beyond. Popular tools also fail to consider k-mer multiplicities, making them less applicable in quantitative settings. Here, we describe a method called Dashing 2 that builds on the SetSketch data structure. SetSketch is related to HyperLogLog (HLL) but discards use of leading zero count in favor of a truncated logarithm of adjustable base. Unlike HLL, SetSketch can perform multiplicity-aware sketching when combined with the ProbMinHash method. Dashing 2 integrates locality-sensitive hashing to scale all-pairs comparisons to millions of sequences. It achieves superior similarity estimates for the Jaccard coefficient and average nucleotide identity compared with the original Dashing, but in much less time while using the same-sized sketch. Dashing 2 is a free, open source software.

Assessing transcriptomic reidentification risks using discriminative sequence models

Gene expression data provide molecular insights into the functional impact of genetic variation, for example, through expression quantitative trait loci (eQTLs). With an improving understanding of the association between genotypes and gene expression comes a greater concern that gene expression profiles could be matched to genotype profiles of the same individuals in another data set, known as a linking attack. Prior works show such a risk could analyze only a fraction of eQTLs that is independent owing to restrictive model assumptions, leaving the full extent of this risk incompletely understood. To address this challenge, we introduce the discriminative sequence model (DSM), a novel probabilistic framework for predicting a sequence of genotypes based on gene expression data. By modeling the joint distribution over all known eQTLs in a genomic region, DSM improves the power of linking attacks with necessary calibration for linkage disequilibrium and redundant predictive signals. We show greater linking accuracy of DSM compared with existing approaches across a range of attack scenarios and data sets including up to 22,288 individuals, suggesting that DSM helps uncover a substantial additional risk overlooked by previous studies. Our work provides a unified framework for assessing the privacy risks of sharing diverse omics data sets beyond transcriptomics.

Leveraging family data to design Mendelian randomization that is provably robust to population stratification

Mendelian randomization (MR) has emerged as a powerful approach to leverage genetic instruments to infer causality between pairs of traits in observational studies. However, the results of such studies are susceptible to biases owing to weak instruments, as well as the confounding effects of population stratification and horizontal pleiotropy. Here, we show that family data can be leveraged to design MR tests that are provably robust to confounding from population stratification, assortative mating, and dynastic effects. We show in simulations that our approach, MR-Twin, is robust to confounding from population stratification and is not affected by weak instrument bias, whereas standard MR methods yield inflated false positive rates. We then conduct an exploratory analysis of MR-Twin and other MR methods applied to 121 trait pairs in the UK Biobank data set. Our results suggest that confounding from population stratification can lead to false positives for existing MR methods, whereas MR-Twin is immune to this type of confounding, and that MR-Twin can help assess whether traditional approaches may be inflated owing to confounding from population stratification.

Major β cell-specific functions of NKX2.2 are mediated via the NK2-specific domain

The consolidation of unambiguous cell fate commitment relies on the ability of transcription factors (TFs) to exert tissue-specific regulation of complex genetic networks. However, the mechanisms by which TFs establish such precise control over gene expression have remained elusive-especially in instances in which a single TF operates in two or more discrete cellular systems. In this study, we demonstrate that β cell-specific functions of NKX2.2 are driven by the highly conserved NK2-specific domain (SD). Mutation of the endogenous NKX2.2 SD prevents the developmental progression of β cell precursors into mature, insulin-expressing β cells, resulting in overt neonatal diabetes. Within the adult β cell, the SD stimulates β cell performance through the activation and repression of a subset of NKX2.2-regulated transcripts critical for β cell function. These irregularities in β cell gene expression may be mediated via SD-contingent interactions with components of chromatin remodelers and the nuclear pore complex. However, in stark contrast to these pancreatic phenotypes, the SD is entirely dispensable for the development of NKX2.2-dependent cell types within the CNS. Together, these results reveal a previously undetermined mechanism through which NKX2.2 directs disparate transcriptional programs in the pancreas versus neuroepithelium.

Duf89 abets lncRNA control of fission yeast phosphate homeostasis via its antagonism of precocious lncRNA transcription termination

Fission yeast phosphate homeostasis gene pho1 is actively repressed during growth in phosphate-rich medium by transcription in cis of a long noncoding (lnc) RNA from the 5' flanking prt(nc-pho1) gene. Pho1 expression is: (i) derepressed by genetic maneuvers that favor precocious lncRNA 3'-processing and termination, in response to DSR and PAS signals in prt; and (ii) hyperrepressed in genetic backgrounds that dampen 3'-processing/termination efficiency. Governors of 3'-processing/termination include the RNA polymerase CTD code, the CPF (cleavage and polyadenylation factor) complex, termination factors Seb1 and Rhn1, and the inositol pyrophosphate signaling molecule 1,5-IP8 Here, we present genetic and biochemical evidence that fission yeast Duf89, a metal-dependent phosphatase/pyrophosphatase, is an antagonist of precocious 3'-processing/termination. We show that derepression of pho1 in duf89Δ cells correlates with squelching the production of full-length prt lncRNA and is erased or attenuated by: (i) DSR/PAS mutations in prt; (ii) loss-of-function mutations in components of the 3'-processing and termination machinery; (iii) elimination of the CTD Thr4-PO4 mark; (iv) interdicting CTD prolyl isomerization by Pin1; (v) inactivating the Asp1 kinase that synthesizes IP8; and (vi) loss of the putative IP8 sensor Spx1. The findings that duf89Δ is synthetically lethal with pho1-derepressive mutations CTD-S7A and aps1Δ-and that this lethality is rescued by CTD-T4A, CPF/Rhn1/Pin1 mutations, and spx1Δ-implicate Duf89 more broadly as a collaborator in cotranscriptional regulation of essential fission yeast genes. The duf89-D252A mutation, which abolishes Duf89 phosphohydrolase activity, phenocopied duf89 +, signifying that duf89Δ phenotypes are a consequence of Duf89 protein absence, not absence of Duf89 catalysis.

High resolution genomes of multiple Xiphophorus species provide new insights into microevolution, hybrid incompatibility, and epistasis

Because of diverged adaptative phenotypes, fish species of the genus Xiphophorus have contributed to a wide range of research for a century. Existing Xiphophorus genome assemblies are not at the chromosomal level and are prone to sequence gaps, thus hindering advancement of the intra- and inter-species differences for evolutionary, comparative, and translational biomedical studies. Herein, we assembled high-quality chromosome-level genome assemblies for three distantly related Xiphophorus species, namely, X. maculatus, X. couchianus, and X. hellerii Our overall goal is to precisely assess microevolutionary processes in the clade to ascertain molecular events that led to the divergence of the Xiphophorus species and to progress understanding of genetic incompatibility to disease. In particular, we measured intra- and inter-species divergence and assessed gene expression dysregulation in reciprocal interspecies hybrids among the three species. We found expanded gene families and positively selected genes associated with live bearing, a special mode of reproduction. We also found positively selected gene families are significantly enriched in nonpolymorphic transposable elements, suggesting the dispersal of these nonpolymorphic transposable elements has accompanied the evolution of the genes, possibly by incorporating new regulatory elements in support of the Britten-Davidson hypothesis. We characterized inter-specific polymorphisms, structural variants, and polymorphic transposable element insertions and assessed their association to interspecies hybridization-induced gene expression dysregulation related to specific disease states in humans.

Large haplotypes highlight a complex age structure within the maize pan-genome

The genomes of maize and other eukaryotes contain stable haplotypes in regions of low recombination. These regions, including centromeres, long heterochromatic blocks, and rDNA arrays, have been difficult to analyze with respect to their diversity and origin. Greatly improved genome assemblies are now available that enable comparative genomics over these and other nongenic spaces. Using 26 complete maize genomes, we developed methods to align intergenic sequences while excluding genes and regulatory regions. The centromere haplotypes (cenhaps) extend for megabases on either side of the functional centromere regions and appear as evolutionary strata, with haplotype divergence/coalescence times dating as far back as 450 thousand years ago (kya). Application of the same methods to other low recombination regions (heterochromatic knobs and rDNA) and all intergenic spaces revealed that deep coalescence times are ubiquitous across the maize pan-genome. Divergence estimates vary over a broad timescale with peaks at ∼16 and 300 kya, reflecting a complex history of gene flow among diverging populations and changes in population size associated with domestication. Cenhaps and other long haplotypes provide vivid displays of this ancient diversity.

The Planemo toolkit for developing, deploying, and executing scientific data analyses in Galaxy and beyond

There are thousands of well-maintained high-quality open-source software utilities for all aspects of scientific data analysis. For more than a decade, the Galaxy Project has been providing computational infrastructure and a unified user interface for these tools to make them accessible to a wide range of researchers. To streamline the process of integrating tools and constructing workflows as much as possible, we have developed Planemo, a software development kit for tool and workflow developers and Galaxy power users. Here we outline Planemo's implementation and describe its broad range of functionality for designing, testing, and executing Galaxy tools, workflows, and training material. In addition, we discuss the philosophy underlying Galaxy tool and workflow development, and how Planemo encourages the use of development best practices, such as test-driven development, by its users, including those who are not professional software developers.

Sequencing Illumina libraries at high accuracy on the ONT MinION using R2C2

High-throughput short-read sequencing has taken on a central role in research and diagnostics. Hundreds of different assays take advantage of Illumina short-read sequencers, the predominant short-read sequencing technology available today. Although other short-read sequencing technologies exist, the ubiquity of Illumina sequencers in sequencing core facilities and the high capital costs of these technologies have limited their adoption. Among a new generation of sequencing technologies, Oxford Nanopore Technologies (ONT) holds a unique position because the ONT MinION, an error-prone long-read sequencer, is associated with little to no capital cost. Here we show that we can make short-read Illumina libraries compatible with the ONT MinION by using the rolling circle to concatemeric consensus (R2C2) method to circularize and amplify the short library molecules. This results in longer DNA molecules containing tandem repeats of the original short library molecules. This longer DNA is ideally suited for the ONT MinION, and after sequencing, the tandem repeats in the resulting raw reads can be converted into high-accuracy consensus reads with similar error rates to that of the Illumina MiSeq. We highlight this capability by producing and benchmarking RNA-seq, ChIP-seq, and regular and target-enriched Tn5 libraries. We also explore the use of this approach for rapid evaluation of sequencing library metrics by implementing a real-time analysis workflow.

The kinetic energy of PAH dication and trication dissociation determined by recoil-frame covariance map imaging

We investigated the dissociation of dications and trications of three polycyclic aromatic hydrocarbons (PAHs), fluorene, phenanthrene, and pyrene. PAHs are a family of molecules ubiquitous in space and involved in much of the chemistry of the interstellar medium. In our experiments, ions are formed by interaction with 30.3 nm extreme ultraviolet (XUV) photons, and their velocity map images are recorded using a PImMS2 multi-mass imaging sensor. Application of recoil-frame covariance analysis allows the total kinetic energy release (TKER) associated with multiple fragmentation channels to be determined to high precision, ranging 1.94-2.60 eV and 2.95-5.29 eV for the dications and trications, respectively. Experimental measurements are supported by Born-Oppenheimer molecular dynamics (BOMD) simulations.

Brain serotonergic fibers suggest anomalous diffusion-based dropout in artificial neural networks

Random dropout has become a standard regularization technique in artificial neural networks (ANNs), but it is currently unknown whether an analogous mechanism exists in biological neural networks (BioNNs). If it does, its structure is likely to be optimized by hundreds of millions of years of evolution, which may suggest novel dropout strategies in large-scale ANNs. We propose that the brain serotonergic fibers (axons) meet some of the expected criteria because of their ubiquitous presence, stochastic structure, and ability to grow throughout the individual's lifespan. Since the trajectories of serotonergic fibers can be modeled as paths of anomalous diffusion processes, in this proof-of-concept study we investigated a dropout algorithm based on the superdiffusive fractional Brownian motion (FBM). The results demonstrate that serotonergic fibers can potentially implement a dropout-like mechanism in brain tissue, supporting neuroplasticity. They also suggest that mathematical theories of the structure and dynamics of serotonergic fibers can contribute to the design of dropout algorithms in ANNs.

A model-based constrained deep learning clustering approach for spatially resolved single-cell data

Spatially resolved scRNA-seq (sp-scRNA-seq) technologies provide the potential to comprehensively profile gene expression patterns in tissue context. However, the development of computational methods lags behind the advances in these technologies, which limits the fulfillment of their potential. In this study, we develop a deep learning approach for clustering sp-scRNA-seq data, named Deep Spatially constrained Single-cell Clustering (DSSC). In this model, we integrate the spatial information of cells into the clustering process in two steps: (1) the spatial information is encoded by using a graphical neural network model, and (2) cell-to-cell constraints are built based on the spatial expression pattern of the marker genes and added in the model to guide the clustering process. Then, a deep embedding clustering is performed on the bottleneck layer of autoencoder by Kullback-Leibler (KL) divergence along with the learning of feature representation. DSSC is the first model that can use information from both spatial coordinates and marker genes to guide cell/spot clustering. Extensive experiments on both simulated and real data sets show that DSSC boosts clustering performance significantly compared with the state-of-the-art methods. It has robust performance across different data sets with various cell type/tissue organization and/or cell type/tissue spatial dependency. We conclude that DSSC is a promising tool for clustering sp-scRNA-seq data.

Learning probabilistic protein-DNA recognition codes from DNA-binding specificities using structural mappings

Knowledge of how proteins interact with DNA is essential for understanding gene regulation. Although DNA-binding specificities for thousands of transcription factors (TFs) have been determined, the specific amino acid-base interactions comprising their structural interfaces are largely unknown. This lack of resolution hampers attempts to leverage these data in order to predict specificities for uncharacterized TFs or TFs mutated in disease. Here we introduce recognition code learning via automated mapping of protein-DNA structural interfaces (rCLAMPS), a probabilistic approach that uses DNA-binding specificities for TFs from the same structural family to simultaneously infer both which nucleotide positions are contacted by particular amino acids within the TF as well as a recognition code that relates each base-contacting amino acid to nucleotide preferences at the DNA positions it contacts. We apply rCLAMPS to homeodomains, the second largest family of TFs in metazoans and show that it learns a highly effective recognition code that can predict de novo DNA-binding specificities for TFs. Furthermore, we show that the inferred amino acid-nucleotide contacts reveal whether and how nucleotide preferences at individual binding site positions are altered by mutations within TFs. Our approach is an important step toward automatically uncovering the determinants of protein-DNA specificity from large compendia of DNA-binding specificities and inferring the altered functionalities of TFs mutated in disease.

Changes in sex differences in neuroanatomical structure and cognitive behavior across the life span

Sex differences occur in the structure and function of the rat cerebral cortex and hippocampus, which can change from the juvenile period through old age. Although the evidence is incomplete, it appears that in at least some portions of the cortex these differences develop due to the rise of ovarian hormones at puberty and are potentially not dependent on the perinatal rise in testosterone, which is essential for sexual differentiation of the hypothalamus and sexual behavior. During aging of female rats, the presence of continued ovarian hormone secretion after cessation of the estrous cycle also influences sex differences in neuroanatomical structure and cognitive behavior, resulting in nullification or reversal of sex differences seen in younger adults. Sex differences can be altered by experience in a stimulating environment during the juvenile/adolescent period, and sex differences in performance even can be affected by the parameters of a task. Thus, broad generalizations about differences such as "spatial ability" are to be avoided. It is clear that to understand how the brain produces behavior, sex and hormones have to be taken into account.

Assembler artifacts include misassembly because of unsafe unitigs and underassembly because of bidirected graphs

Recent assemblies by the T2T and VGP consortia have achieved significant accuracy but required a tremendous amount of effort and resources. More typical assembly efforts, on the other hand, still suffer both from misassemblies (joining sequences that should not be adjacent) and from underassemblies (not joining sequences that should be adjacent). To better understand the common algorithm-driven causes of these limitations, we investigated the unitig algorithm, which is a core algorithm at the heart of most assemblers. We prove that, contrary to popular belief, even when there are no sequencing errors, unitigs are not always safe (i.e., they are not guaranteed to be substrings of the sequenced genome). We also prove that the unitigs of a bidirected de Bruijn graph are different from those of a doubled de Bruijn graph and, contrary to our expectations, result in underassembly. Using experimental simulations, we then confirm that these two artifacts exist not only in theory but also in the output of widely used assemblers. In particular, when coverage is low, then even error-free data result in unsafe unitigs; also, unitigs may unnecessarily split palindromes in half if special care is not taken. To the best of our knowledge, this paper is the first to theoretically predict the existence of these assembler artifacts and confirm and measure the extent of their occurrence in practice.

A general calculus of fitness landscapes finds genes under selection in cancers

Genetic variants drive the evolution of traits and diseases. We previously modeled these variants as small displacements in fitness landscapes and estimated their functional impact by differentiating the evolutionary relationship between genotype and phenotype. Conversely, here we integrate these derivatives to identify genes steering specific traits. Over cancer cohorts, integration identified 460 likely tumor-driving genes. Many have literature and experimental support but had eluded prior genomic searches for positive selection in tumors. Beyond providing cancer insights, these results introduce a general calculus of evolution to quantify the genotype-phenotype relationship and discover genes associated with complex traits and diseases.

The PIWI/piRNA response is relaxed in a rodent that lacks mobilizing transposable elements

Transposable elements (TEs) are genomic parasites that can propagate throughout host genomes. Mammalian genomes are typically dominated by LINE retrotransposons and their associated SINEs, and germline mobilization is a challenge to genome integrity. There are defenses against TE proliferation and the PIWI/piRNA defense is among the most well understood. However, the PIWI/piRNA system has been investigated largely in animals with actively mobilizing TEs and it is unclear how the PIWI/piRNA system functions in the absence of mobilizing TEs. The 13-lined ground squirrel provides the opportunity to examine PIWI/piRNA and TE dynamics within the context of minimal, and possibly nonexistent, TE accumulation. To do so, we compared the PIWI/piRNA dynamics in squirrels to observations from the rabbit and mouse. Despite a lack of young insertions in squirrels, TEs were still actively transcribed at higher levels compared to mouse and rabbit. All three Piwi genes were not expressed, prior to P8 in squirrel testis, and there was little TE expression change with the onset of Piwi expression. We also demonstrated there was not a major expression change in the young squirrel LINE families in the transition from juvenile to adult testis in contrast to young mouse and rabbit LINE families. These observations lead us to conclude that PIWI suppression, was weaker for squirrel LINEs and SINEs and did not strongly reduce their transcription. We speculate that, although the PIWI/piRNA system is adaptable to novel TE threats, transcripts from TEs that are no longer threatening receive less attention from PIWI proteins.

In utero exposure to electronic-cigarette aerosols decreases lung fibrillar collagen content, increases Newtonian resistance and induces sex-specific molecular signatures in neonatal mice

Approximately 7% of pregnant women in the United States use electronic-cigarette (e-cig) devices during pregnancy. There is, however, no scientific evidence to support e-cig use as being 'safe' during pregnancy. Little is known about the effects of fetal exposures to e-cig aerosols on lung alveologenesis. In the present study, we tested the hypothesis that in utero exposure to e-cig aerosol impairs lung alveologenesis and pulmonary function in neonates. Pregnant BALB/c mice were exposed 2 h a day for 20 consecutive days during gestation to either filtered air or cinnamon-flavored e-cig aerosol (36 mg/mL of nicotine). Lung tissue was collected in offspring during lung alveologenesis on postnatal day (PND) 5 and PND11. Lung function was measured at PND11. Exposure to e-cig aerosol in utero led to a significant decrease in body weights at birth which was sustained through PND5. At PND5, in utero e-cig exposures dysregulated genes related to Wnt signaling and epigenetic modifications in both females (~ 120 genes) and males (40 genes). These alterations were accompanied by reduced lung fibrillar collagen content at PND5-a time point when collagen content is close to its peak to support alveoli formation. In utero exposure to e-cig aerosol also increased the Newtonian resistance of offspring at PND11, suggesting a narrowing of the conducting airways. At PND11, in females, transcriptomic dysregulation associated with epigenetic alterations was sustained (17 genes), while WNT signaling dysregulation was largely resolved (10 genes). In males, at PND11, the expression of only 4 genes associated with epigenetics was dysregulated, while 16 Wnt related-genes were altered. These data demonstrate that in utero exposures to cinnamon-flavored e-cig aerosols alter lung structure and function and induce sex-specific molecular signatures during lung alveologenesis in neonatal mice. This may reflect epigenetic programming affecting lung disease development later in life.

Distinct structural bases for sequence-specific DNA binding by mammalian BEN domain proteins

The BEN domain is a recently recognized DNA binding module that is present in diverse metazoans and certain viruses. Several BEN domain factors are known as transcriptional repressors, but, overall, relatively little is known of how BEN factors identify their targets in humans. In particular, X-ray structures of BEN domain:DNA complexes are only known for Drosophila factors bearing a single BEN domain, which lack direct vertebrate orthologs. Here, we characterize several mammalian BEN domain (BD) factors, including from two NACC family BTB-BEN proteins and from BEND3, which has four BDs. In vitro selection data revealed sequence-specific binding activities of isolated BEN domains from all of these factors. We conducted detailed functional, genomic, and structural studies of BEND3. We show that BD4 is a major determinant for in vivo association and repression of endogenous BEND3 targets. We obtained a high-resolution structure of BEND3-BD4 bound to its preferred binding site, which reveals how BEND3 identifies cognate DNA targets and shows differences with one of its non-DNA-binding BEN domains (BD1). Finally, comparison with our previous invertebrate BEN structures, along with additional structural predictions using AlphaFold2 and RoseTTAFold, reveal distinct strategies for target DNA recognition by different types of BEN domain proteins. Together, these studies expand the DNA recognition activities of BEN factors and provide structural insights into sequence-specific DNA binding by mammalian BEN proteins.

Resetting of the 24-nt siRNA landscape in rice zygotes

The zygote, a totipotent stem cell, is crucial to the life cycle of sexually reproducing organisms. It is produced by the fusion of two differentiated cells-the egg and sperm, which in plants have radically different siRNA transcriptomes from each other and from multicellular embryos. Owing to technical challenges, the epigenetic changes that accompany the transition from differentiated gametes to totipotent zygote are poorly understood. Because siRNAs serve as both regulators and outputs of the epigenome, we characterized small RNA transcriptomes of zygotes from rice. Zygote small RNAs exhibit extensive maternal carryover and an apparent lack of paternal contribution, indicated by absence of sperm signature siRNAs. Zygote formation is accompanied by widespread redistribution of 24-nt siRNAs relative to gametes, such that ∼70% of the zygote siRNA loci do not overlap any egg cell siRNA loci. Newly detected siRNA loci in zygote are gene-proximal and not associated with centromeric heterochromatin, similar to canonical siRNAs, in sharp contrast to gametic siRNA loci that are gene-distal and heterochromatic. In addition, zygote but not egg siRNA loci are associated with high DNA methylation in the mature embryo. Thus, the zygote begins transitioning before the first embryonic division to an siRNA profile that is associated with future RdDM in embryogenesis. These findings indicate that, in addition to changes in gene expression, the transition to totipotency in the plant zygote is accompanied by resetting of the epigenetic reprogramming that occurred during gamete formation.

Application of Synthetic Peptide CEP1 Increases Nutrient Uptake Rates Along Plant Roots

The root system of a plant provides vital functions including resource uptake, storage, and anchorage in soil. The uptake of macro-nutrients like nitrogen (N), phosphorus (P), potassium (K), and sulphur (S) from the soil is critical for plant growth and development. Small signaling peptide (SSP) hormones are best known as potent regulators of plant growth and development with a few also known to have specialized roles in macronutrient utilization. Here we describe a high throughput phenotyping platform for testing SSP effects on root uptake of multiple nutrients. The SSP, CEP1 (C-TERMINALLY ENCODED PEPTIDE) enhanced nitrate uptake rate per unit root length in Medicago truncatula plants deprived of N in the high-affinity transport range. Single structural variants of M. truncatula and Arabidopsis thaliana specific CEP1 peptides, MtCEP1D1:hyp4,11 and AtCEP1:hyp4,11, enhanced uptake not only of nitrate, but also phosphate and sulfate in both model plant species. Transcriptome analysis of Medicago roots treated with different MtCEP1 encoded peptide domains revealed that hundreds of genes respond to these peptides, including several nitrate transporters and a sulfate transporter that may mediate the uptake of these macronutrients downstream of CEP1 signaling. Likewise, several putative signaling pathway genes including LEUCINE-RICH REPEAT RECPTOR-LIKE KINASES and Myb domain containing transcription factors, were induced in roots by CEP1 treatment. Thus, a scalable method has been developed for screening synthetic peptides of potential use in agriculture, with CEP1 shown to be one such peptide.

RNA multimerization as an organizing force for liquid-liquid phase separation

RNA interactions are exceptionally strong and highly redundant. As such, nearly any two RNAs have the potential to interact with one another over relatively short stretches, especially at high RNA concentrations. This is especially true for pairs of RNAs that do not form strong self-structure. Such phenomena can drive liquid-liquid phase separation, either solely from RNA-RNA interactions in the presence of divalent or organic cations, or in concert with proteins. RNA interactions can drive multimerization of RNA strands via both base-pairing and tertiary interactions. In this article, we explore the tendency of RNA to form stable monomers, dimers, and higher order structures as a function of RNA length and sequence through a focus on the intrinsic thermodynamic, kinetic, and structural properties of RNA. The principles we discuss are independent of any specific type of biomolecular condensate, and thus widely applicable. We also speculate how external conditions experienced by living organisms can influence the formation of nonmembranous compartments, again focusing on the physical and structural properties of RNA. Plants, in particular, are subject to diverse abiotic stresses including extreme temperatures, drought, and salinity. These stresses and the cellular responses to them, including changes in the concentrations of small molecules such as polyamines, salts, and compatible solutes, have the potential to regulate condensate formation by melting or strengthening base-pairing. Reversible condensate formation, perhaps including regulation by circadian rhythms, could impact biological processes in plants, and other organisms.

A full-proteome, interaction-specific characterization of mutational hotspots across human cancers

Rapid accumulation of cancer genomic data has led to the identification of an increasing number of mutational hotspots with uncharacterized significance. Here we present a biologically informed computational framework that characterizes the functional relevance of all 1107 published mutational hotspots identified in approximately 25,000 tumor samples across 41 cancer types in the context of a human 3D interactome network, in which the interface of each interaction is mapped at residue resolution. Hotspots reside in network hub proteins and are enriched on protein interaction interfaces, suggesting that alteration of specific protein-protein interactions is critical for the oncogenicity of many hotspot mutations. Our framework enables, for the first time, systematic identification of specific protein interactions affected by hotspot mutations at the full proteome scale. Furthermore, by constructing a hotspot-affected network that connects all hotspot-affected interactions throughout the whole-human interactome, we uncover genome-wide relationships among hotspots and implicate novel cancer proteins that do not harbor hotspot mutations themselves. Moreover, applying our network-based framework to specific cancer types identifies clinically significant hotspots that can be used for prognosis and therapy targets. Overall, we show that our framework bridges the gap between the statistical significance of mutational hotspots and their biological and clinical significance in human cancers.

The Effect of the Number and Identification of Recipients on Organ-Donation Decisions

We examined how presentations of organ donation cases in the media may affect people’s decisions about organ donation issues. Specifically, we focused on the combined effect of the information about the number of recipients saved by the organs of one deceased person (one vs. four) and the identifiability of the donor and the recipient(s) in organ donation descriptions, on people’s willingness to donate the organs of a deceased relative. Results suggest that reading about more people who were saved by the organs of a deceased donor does not increase willingness to donate. Replicating earlier research, we found that reading about a case of organ donation involving an identified deceased donor, deceased willingness to donate. However, this effect was attenuated when participants read about more recipients who were saved by the donation. Importantly, the presentation that prompted the greatest willingness to donate a deceased relative’s organs was the one that featured an unidentified donor and only one identified recipient. Finally, an explorative investigation into participants’ subconscious thoughts of death following the organ donation story revealed that identifying a deceased organ donor prompts more thoughts of death in the perceiver (regardless of the number of recipients).

Time-dependent transformations of memory representations differ along the long axis of the hippocampus

Research has shown that sleep is beneficial for the long-term retention of memories. According to theories of memory consolidation, memories are gradually reorganized, becoming supported by widespread, distributed cortical networks, particularly during postencoding periods of sleep. However, the effects of sleep on the organization of memories in the hippocampus itself remains less clear. In a 3-d study, participants encoded separate lists of word-image pairs differing in their opportunity for sleep-dependent consolidation. Pairs were initially studied either before or after an overnight sleep period, and were then restudied in a functional magnetic resonance imaging (fMRI) scan session. We used multivariate pattern similarity analyses to examine fine-grained effects of consolidation on memory representations in the hippocampus. We provide evidence for a dissociation along the long axis of the hippocampus that emerges with consolidation, such that representational patterns for object-word memories initially formed prior to sleep become differentiated in anterior hippocampus and more similar, or overlapping, in posterior hippocampus. Differentiation in anterior hippocampal representations correlated with subsequent behavioral performance. Furthermore, representational overlap in posterior hippocampus correlated with the duration of intervening slow wave sleep. Together, these results demonstrate that sleep-dependent consolidation promotes the reorganization of memory traces along the long axis of the hippocampus.

Greater dependence on working memory and restricted familiarity in orangutans compared with rhesus monkeys

The prefrontal cortex is larger than would be predicted by body size or visual cortex volume in great apes compared with monkeys. Because prefrontal cortex is critical for working memory, we hypothesized that recognition memory tests would engage working memory in orangutans more robustly than in rhesus monkeys. In contrast to working memory, the familiarity response that results from repetition of an image is less cognitively taxing and has been associated with nonfrontal brain regions. Across three experiments, we observed a striking species difference in the control of behavior by these two types of memory. First, we found that recognition memory performance in orangutans was controlled by working memory under conditions in which this memory system plays little role in rhesus monkeys. Second, we found that unlike the case in monkeys, familiarity was not involved in recognition memory performance in orangutans, shown by differences with monkeys across three different measures. Memory in orangutans was not improved by use of novel images, was always impaired by a concurrent cognitive load, and orangutans did not accurately identify images seen minutes ago. These results are surprising and puzzling, but do support the view that prefrontal expansion in great apes favored working memory. At least in orangutans, increased dependence on working memory may come at a cost in terms of the availability of familiarity.

Deep reasoning neural network analysis to predict language deficits from psychometry-driven DWI connectome of young children with persistent language concerns

This study investigated whether current state-of-the-art deep reasoning network analysis on psychometry-driven diffusion tractography connectome can accurately predict expressive and receptive language scores in a cohort of young children with persistent language concerns (n = 31, age: 4.25 ± 2.38 years). A dilated convolutional neural network combined with a relational network (dilated CNN + RN) was trained to reason the nonlinear relationship between "dilated CNN features of language network" and "clinically acquired language score". Three-fold cross-validation was then used to compare the Pearson correlation and mean absolute error (MAE) between dilated CNN + RN-predicted and actual language scores. The dilated CNN + RN outperformed other methods providing the most significant correlation between predicted and actual scores (i.e., Pearson's R/p-value: 1.00/<.001 and .99/<.001 for expressive and receptive language scores, respectively) and yielding MAE: 0.28 and 0.28 for the same scores. The strength of the relationship suggests elevated probability in the prediction of both expressive and receptive language scores (i.e., 1.00 and 1.00, respectively). Specifically, sparse connectivity not only within the right precentral gyrus but also involving the right caudate had the strongest relationship between deficit in both the expressive and receptive language domains. Subsequent subgroup analyses inferred that the effectiveness of the dilated CNN + RN-based prediction of language score(s) was independent of time interval (between MRI and language assessment) and age of MRI, suggesting that the dilated CNN + RN using psychometry-driven diffusion tractography connectome may be useful for prediction of the presence of language disorder, and possibly provide a better understanding of the neurological mechanisms of language deficits in young children.

Competition for DNA binding between paralogous transcription factors determines their genomic occupancy and regulatory functions

Most eukaryotic transcription factors (TFs) are part of large protein families, with members of the same family (i.e., paralogous TFs) recognizing similar DNA-binding motifs but performing different regulatory functions. Many TF paralogs are coexpressed in the cell and thus can compete for target sites across the genome. However, this competition is rarely taken into account when studying the in vivo binding patterns of eukaryotic TFs. Here, we show that direct competition for DNA binding between TF paralogs is a major determinant of their genomic binding patterns. Using yeast proteins Cbf1 and Pho4 as our model system, we designed a high-throughput quantitative assay to capture the genomic binding profiles of competing TFs in a cell-free system. Our data show that Cbf1 and Pho4 greatly influence each other's occupancy by competing for their common putative genomic binding sites. The competition is different at different genomic sites, as dictated by the TFs' expression levels and their divergence in DNA-binding specificity and affinity. Analyses of ChIP-seq data show that the biophysical rules that dictate the competitive TF binding patterns in vitro are also followed in vivo, in the complex cellular environment. Furthermore, the Cbf1-Pho4 competition for genomic sites, as characterized in vitro using our new assay, plays a critical role in the specific activation of their target genes in the cell. Overall, our study highlights the importance of direct TF-TF competition for genomic binding and gene regulation by TF paralogs, and proposes an approach for studying this competition in a quantitative and high-throughput manner.

Liquid-liquid phase separation of Tau by self and complex coacervation

The liquid-liquid phase separation (LLPS) of Tau has been postulated to play a role in modulating the aggregation property of Tau, a process known to be critically associated with the pathology of a broad range of neurodegenerative diseases including Alzheimer's Disease. Tau can undergo LLPS by homotypic interaction through self-coacervation (SC) or by heterotypic association through complex-coacervation (CC) between Tau and binding partners such as RNA. What is unclear is in what way the formation mechanisms for self and complex coacervation of Tau are similar or different, and the addition of a binding partner to Tau alters the properties of LLPS and Tau. A combination of in vitro experimental and computational study reveals that the primary driving force for both Tau CC and SC is electrostatic interactions between Tau-RNA or Tau-Tau macromolecules. The liquid condensates formed by the complex coacervation of Tau and RNA have distinctly higher micro-viscosity and greater thermal stability than that formed by the SC of Tau. Our study shows that subtle changes in solution conditions, including molecular crowding and the presence of binding partners, can lead to the formation of different types of Tau condensates with distinct micro-viscosity that can coexist as persistent and immiscible entities in solution. We speculate that the formation, rheological properties and stability of Tau droplets can be readily tuned by cellular factors, and that liquid condensation of Tau can alter the conformational equilibrium of Tau.

Connective appendages in Huberia bradeana (Melastomataceae) affect pollen release during buzz pollination

Floral structures, such as stamen appendages, play crucial roles in pollinator attraction, pollen release dynamics and, ultimately, the reproductive success of plants. The pollen-rewarding, bee buzz-pollinated flowers of Melastomataceae often bear conspicuous staminal appendages. Surprisingly, their functional role in the pollination process remains largely unclear. We use Huberia bradeana Bochorny & R. Goldenb. (Melastomataceae) with conspicuously elongated, twisted stamen appendages to investigate their functional role in the pollination process. We studied the effect of stamen appendages on pollinator behaviour and reproductive success by comparing manipulated flowers (appendages removed) with unmanipulated flowers. To assess bee pollinator behaviour, we measured three properties of buzzes (vibrations) produced by bees on Huberia flowers: frequency, duration and number of buzzes per flower visit. We measured male and female reproductive success by monitoring pollen release and deposition after single bee visits. Finally, we used artificial vibrations and laser vibrometry to assess how flower vibrational properties change with the removal of stamen appendages. Our results show that the absence of staminal appendages does not modify bee buzzing behaviour. Pollen release was higher in unmanipulated flowers, but stigmatic pollen loads differ only marginally between the two treatments. We also detected lower vibration amplitudes in intact flowers as compared to manipulated flowers in artificial vibration experiments. The presence of connective appendages are crucial in transmitting vibrations and assuring optimal pollen release. Therefore, we propose that the high diversity of colours, shapes and sizes of connective appendages in buzz-pollinated flowers may have evolved by selection through male fitness.

Haplotype diversity and sequence heterogeneity of human telomeres

Telomeres are regions of repetitive nucleotide sequences capping the ends of eukaryotic chromosomes that protect against deterioration, and whose lengths can be correlated with age and adverse health risk factors. Yet, given their length and repetitive nature, telomeric regions are not easily reconstructed from short-read sequencing, thus making telomere sequencing, mapping, and variant resolution challenging problems. Recently, long-read sequencing, with read lengths measuring in hundreds of kilobase pairs, has made it possible to routinely read into telomeric regions and inspect their sequence structure. Here, we describe a framework for extracting telomeric reads from whole-genome single-molecule sequencing experiments, including de novo identification of telomere repeat motifs and repeat types, and also describe their sequence variation. We find that long, complex telomeric stretches and repeats can be accurately captured with long-read sequencing, observe extensive sequence heterogeneity of human telomeres, discover and localize noncanonical telomere sequence motifs (both previously reported, as well as novel), and validate them in short-read sequence data. These data reveal extensive intra- and inter-population diversity of repeats in telomeric haplotypes, reveal higher paternal inheritance of telomeric variants, and represent the first motif composition maps of multi-kilobase-pair human telomeric haplotypes across three distinct ancestries (Ashkenazi, Chinese, and Utah), which can aid in future studies of genetic variation, aging, and genome biology.

The genomic basis of evolutionary differentiation among honey bees

In contrast to the western honey bee, Apis mellifera, other honey bee species have been largely neglected despite their importance and diversity. The genetic basis of the evolutionary diversification of honey bees remains largely unknown. Here, we provide a genome-wide comparison of three honey bee species, each representing one of the three subgenera of honey bees, namely the dwarf (Apis florea), giant (A. dorsata), and cavity-nesting (A. mellifera) honey bees with bumblebees as an outgroup. Our analyses resolve the phylogeny of honey bees with the dwarf honey bees diverging first. We find that evolution of increased eusocial complexity in Apis proceeds via increases in the complexity of gene regulation, which is in agreement with previous studies. However, this process seems to be related to pathways other than transcriptional control. Positive selection patterns across Apis reveal a trade-off between maintaining genome stability and generating genetic diversity, with a rapidly evolving piRNA pathway leading to genomes depleted of transposable elements, and a rapidly evolving DNA repair pathway associated with high recombination rates in all Apis species. Diversification within Apis is accompanied by positive selection in several genes whose putative functions present candidate mechanisms for lineage-specific adaptations, such as migration, immunity, and nesting behavior.