Denisovans are an extinct group of humans whose morphology is mostly unknown. The scarcity of verified Denisovan fossils makes it challenging to study their anatomy, and how well they were adapted to their environment. We previously developed a genetic phenotyping approach to gain insight into Denisovan anatomy by detecting gene regulatory changes that likely altered Denisovan skeletal morphology. Here, we scan Middle Pleistocene crania for unclassified or disputed specimens that match predicted Denisovan morphology and thus might be related to Denisovans. We found that Harbin, Dali, and Kabwe 1 show a particularly good alignment with the Denisovan profile, with most of their phenotypes matching predicted Denisovan anatomy. We conclude that our genetic phenotyping approach could help classify unidentified specimens, and that Harbin, Dali, and Kabwe 1 exhibit a Denisovan-like morphology and could be closely linked to the Denisovan lineage.

Maternal perceived prenatal stress (PPS) is a known risk factor for diverse developmental impairments in newborns, but the underlying molecular processes are incompletely understood. Here, we report that maternal PPS altered the birth profiles of blood transfer RNA fragments (tRFs), 16-50nt long non-random cleavage products of tRNAs, in a sex-dependent manner. Importantly, comparing stressed versus control maternal and umbilical cord blood serum presented alterations that were not limited to individual tRFs, but rather reflected selective changes in particular tRF families grouped by their mitochondrial or nuclear genome origin, parental tRNA coded amino acid, and cleavage type. tRF families that show stress- and sex-specific effects, revealed shared length and expression patterns which were strongest in the female newborns. Several of these tRFs carry complementary motifs to specific cholinergic mRNAs, suggesting possible translational regulation similar to microRNAs. Compatible with the cholinergic regulation of stress reactions, those “CholinotRFs” achieved an AUC of 95% when classifying female newborns according to maternal PPS. Moreover, we found altered catalytic activity of serum acetylcholinesterase, which was particularly elevated in male newborns, marking a second sex-specific effect. Our findings demonstrate an association of tRF families’ patterns with newborns’ sex-specific stress response to PPS and may lead to better diagnosis and therapeutic tools for these and other stressors.

Identifying evolutionary changes in DNA methylation bears a huge potential for unraveling adaptations that have occurred in modern humans. Over the past decade, computational methods to reconstruct DNA methylation patterns from ancient DNA sequences have been developed, allowing for the exploration of DNA methylation changes during the past hundreds of thousands of years of human evolution. Here, we introduce a new version of RoAM (Reconstruction of Ancient Methylation), a flexible tool that allows for the reconstruction of ancient methylomes, as well as the identification of differentially methylated regions between ancient populations. RoAM incorporates a series of filtering and quality control steps, resulting in highly reliable DNA methylation maps that exhibit similar characteristics to modern maps. To showcase RoAM’s capabilities, we used it to compare ancient methylation patterns between pre- and post-Neolithic revolution samples from the Balkans. Differentially methylated regions separating these populations are shown to be associated with genes related to regulation of sugar metabolism. Notably, we provide evidence for overexpression of the gene PTPRN2 in post-Neolithic revolution samples. PTPRN2 is a key regulator of insulin secretion, and our finding is compatible with hypoinsulinism in pre-Neolithic revolution hunter-gatherers. Additionally, we observe methylation changes in the genes EIF2AK4 and SLC2A5, which provide further evidence to metabolic adaptations to a changing diet during the Neolithic transition. RoAM offers powerful algorithms that position it as a key asset for researchers seeking to identify evolutionary regulatory changes through the lens of paleoepigenetics.

Genome-wide premortem DNA methylation patterns can be computationally reconstructed from high-coverage DNA sequences of ancient samples. Because DNA methylation is more conserved across species than across tissues, and ancient DNA is typically extracted from bones and teeth, previous works utilizing ancient DNA methylation maps focused on studying evolutionary changes in the skeletal system. Here we suggest that DNA methylation patterns in one tissue may, under certain conditions, be informative on DNA methylation patterns in other tissues of the same individual. Using the fact that tissue-specific DNA methylation builds up during embryonic development, we identified the conditions that allow for such cross-tissue inference and devised an algorithm that carries it out. We trained the algorithm on methylation data from extant species and reached high precisions of up to 0.92 for validation datasets. We then used the algorithm on archaic humans, and identified more than 1,850 positions for which we were able to observe differential DNA methylation in prefrontal cortex neurons. These positions are linked to hundreds of genes, many of which are involved in neural functions such as structural and developmental processes. Six positions are located in the neuroblastoma breaking point family (NBPF) gene family, which probably played a role in human brain evolution. The algorithm we present here allows for the examination of epigenetic changes in tissues and cell types that are absent from the palaeontological record, and therefore provides new ways to study the evolutionary impacts of epigenetic changes.

Reconstructing premortem DNA methylation levels in ancient DNA has led to breakthrough studies such as the prediction of anatomical features of the Denisovan. These studies rely on computationally inferring methylation levels from damage signals in naturally deaminated cytosines, which requires expensive high-coverage genomes. Here, we test two methods for direct methylation measurement developed for modern DNA based on either bisulfite or enzymatic methylation treatments. Bisulfite treatment shows the least reduction in DNA yields as well as the least biases during methylation conversion, demonstrating that this method can be successfully applied to ancient DNA.

Background: The Critical Assessment of Genome Interpretation (CAGI) aims to advance the state-of-the-art for computational prediction of genetic variant impact, particularly where relevant to disease. The five complete editions of the CAGI community experiment comprised 50 challenges, in which participants made blind predictions of phenotypes from genetic data, and these were evaluated by independent assessors.
Results: Performance was particularly strong for clinical pathogenic variants, including some difficult-to-diagnose cases, and extends to interpretation of cancer-related variants. Missense variant interpretation methods were able to estimate biochemical effects with increasing accuracy. Assessment of methods for regulatory variants and complex trait disease risk was less definitive and indicates performance potentially suitable for auxiliary use in the clinic.
Conclusions: Results show that while current methods are imperfect, they have major utility for research and clinical applications. Emerging methods and increasingly large, robust datasets for training and assessment promise further progress ahead.

Studying premortem DNA methylation from ancient DNA (aDNA) provides a proxy for ancient gene activity patterns, and hence valuable information on evolutionary changes in gene regulation. Due to statistical limitations, current methods to reconstruct aDNA methylation maps are constrained to high-coverage shotgun samples, which comprise a small minority of available ancient samples. Most samples are sequenced using in-situ hybridization capture sequencing which targets a predefined set of genomic positions. Here, we develop methods to reconstruct aDNA methylation maps of samples that were not sequenced using high-coverage shotgun sequencing, by way of pooling together individuals to obtain a DNA methylation map that is characteristic of a population. We show that the resulting DNA methylation maps capture meaningful biological information and allow for the detection of differential methylation across populations. We offer guidelines on how to carry out comparative studies involving ancient populations, and how to control the rate of falsely discovered differentially methylated regions. The ability to reconstruct DNA methylation maps of past populations allows for the development of a whole new frontier in paleoepigenetic research, tracing DNA methylation changes throughout human history, using data from thousands of ancient samples.

Motivation. The rise in the number of genotyped ancient individuals provides an opportunity to estimate population admixture models for many populations. However, in models describing modern populations as mixtures of ancient ones, it is typically difficult to estimate the model mixing coefficients and to evaluate its fit to the data.
Results. We present LINADMIX, designed to tackle this problem by solving a constrained linear model when both the ancient and the modern genotypes are represented in a low-dimensional space. LINADMIX estimates the mixing coefficients and their standard errors, and computes a P-value for testing the model fit to the data. We quantified the performance of LINADMIX using an extensive set of simulated studies. We show that LINADMIX can accurately estimate admixture coefficients, and is robust to factors such as population size, genetic drift, proportion of missing data and various types of model misspecification.
Availability and implementation. LINADMIX is available as a python code at https://github.com/swidler/linadmix.

Forty years ago, in a seminal paper published in Science, Settle and Patterson used archeological and historical data to estimate the rates of worldwide lead production since the discovery of cupellation, approximately 5000 years ago. Here, we record actual lead exposure of a human population by direct measurements of the concentrations of lead in petrous bones of individuals representing approximately 12 000 years of inhabitation in Italy. This documentation of lead pollution throughout human history indicates that, remarkably, much of the estimated dynamics in lead production is replicated in human exposure. Thus, lead pollution in humans has closely followed anthropogenic lead production. This observation raises concerns that the forecasted increase in the production of lead and other metals might affect human health in the near future.

Here we describe a new integrative approach for accurate annotation and quantification of circRNAs named Short Read circRNA Pipeline (SRCP). Our strategy involves two steps: annotation of validated circRNAs followed by a quantification step. We show that SRCP is more sensitive than other individual pipelines and allows for more comprehensive quantification of a larger number of differentially expressed circRNAs. To facilitate the use of SRCP, we generate a comprehensive collection of validated circRNAs in five different organisms, including humans. We then utilize our approach and identify a subset of circRNAs bound to the miRNA-effector protein AGO2 in human brain samples.

Background. Antisense oligonucleotide (ASO)-based drugs for splicing modulation were recently approved for various genetic diseases with unmet need. Here we aimed to develop an ASO-based splicing modulation therapy for Cystic Fibrosis (CF) patients carrying the 3849+10 kb C-to-T splicing mutation in the CFTR gene.
Methods. We have screened, in FRT cells expressing the 3849+10 kb C-to-T splicing mutation, ~30 2′-O-Methyl-modified phosphorothioate ASOs, targeted to prevent the recognition and inclusion of a cryptic exon generated due to the mutation. The effect of highly potent ASO candidates on the splicing pattern, protein maturation and CFTR function was further analyzed in well differentiated primary human nasal and bronchial epithelial cells, derived from patients carrying at least one 3849+10 kb C-to-T allele.
Results. A highly potent lead ASO, efficiently delivered by free uptake, was able to significantly increase the level of correctly spliced mRNA and completely restore the CFTR function to wild type levels in cells from a homozygote patient. This ASO led to CFTR function with an average of 43% of wild type levels in cells from various heterozygote patients. Optimized efficiency of the lead ASO was further obtained with 2′-Methoxy Ethyl modification (2′MOE).
Conclusion. The highly efficient splicing modulation and functional correction, achieved by free uptake of the selected lead ASO in various patients, demonstrate the ASO therapeutic potential benefit for CF patients carrying splicing mutations and is aimed to serve as the basis for our current clinical development.
Keywords. Cystic fibrosis, Antisense oligonucleotides, Drug development, Splicing modulation 3849+10 kb, C-to-T mutation.

Recent advances in ancient DNA extraction and high-throughput sequencing technologies enabled the high-quality sequencing of archaic genomes, including the Neanderthal and the Denisovan. While comparisons with modern humans revealed both archaic-specific and human-specific sequence changes, in the absence of gene expression information, understanding the functional implications of such genetic variations remains a major challenge. To study gene regulation in archaic humans, epigenetic research comes to our aid. DNA methylation, which is highly correlated with transcription, can be directly measured in modern samples, as well as reconstructed in ancient samples. This puts DNA methylation as a natural basis for comparative epigenetics between modern humans, archaic humans and nonhuman primates.

We report genome-wide DNA data for 73 individuals from five archaeological sites across the Bronze and Iron Ages Southern Levant. These individuals, who share the "Canaanite" material culture, can be modeled as descending from two sources: (1) earlier local Neolithic populations and (2) populations related to the Chalcolithic Zagros or the Bronze Age Caucasus. The non-local contribution increased over time, as evinced by three outliers who can be modeled as descendants of recent migrants. We show evidence that different "Canaanite" groups genetically resemble each other more than other populations. We find that Levant-related modern populations typically have substantial ancestry coming from populations related to the Chalcolithic Zagros and the Bronze Age Southern Levant. These groups also harbor ancestry from sources we cannot fully model with the available data, highlighting the critical role of post-Bronze-Age migrations into the region over the past 3,000 years.

Changes in potential regulatory elements are thought to be key drivers of phenotypic divergence. However, identifying changes to regulatory elements that underlie human-specific traits has proven very challenging. Here, we use 63 reconstructed and experimentally measured DNA methylation maps of ancient and present-day humans, as well as of six chimpanzees, to detect differentially methylated regions that likely emerged in modern humans after the split from Neanderthals and Denisovans. We show that genes associated with face and vocal tract anatomy went through particularly extensive methylation changes. Specifically, we identify widespread hypermethylation in a network of face- and voice-associated genes (SOX9, ACAN, COL2A1, NFIX and XYLT1). We propose that these repression patterns appeared after the split from Neanderthals and Denisovans, and that they might have played a key role in shaping the modern human face and vocal tract.

High coverage sequences of archaic humans enabled the reconstruction of their DNA methylation patterns. This allowed comparing gene regulation between human groups, and linking such regulatory changes to phenotypic differences. In a previous work, a detailed comparison of DNA methylation in modern humans, archaic humans, and chimpanzees revealed 873 modern human-derived differentially methylated regions (DMRs). To understand the regulatory implications of these DMRs, we defined differentially methylated genes (DMGs) as genes that harbor DMRs in their promoter or gene body. While most of the modern human-derived DMRs could be linked to DMGs, many others remained unassigned. Here, we used information on 3D genome organization to link ~70 out of the remaining 288 unassigned DMRs to genes. Combined with the previously identified DMGs, we reinforce the enrichment of these genes with vocal and facial anatomy, and additionally find significant enrichment with the spinal column, chin, hair, and scalp. These results reveal the importance of 3D genomic organization in understanding gene regulation by DNA methylation.
Keywords: ancient DNA; epigenetics; DNA methylation; genome organization; gene regulation; archaic humans; comparative epigenomics

The 3'-end of the coding sequence in several species is known to show specific codon usage bias. Several factors have been suggested to underlie this phenomenon, including selection against translation efficiency, selection for translation accuracy, and selection against RNA folding. All are supported by some evidence, but there is no general agreement as to which factors are the main determinants. Nor is it known how universal this phenomenon is, and whether the same factors explain it in different species. To answer these questions, we developed a measure that quantifies the codon usage bias at the gene end, and used it to compute this bias for 91 species that span the three domains of life. In addition, we characterized the codons in each species by features that allow discrimination between the different factors. Combining all these data, we were able to show that there is a universal trend to favor AT-rich codons toward the gene end. Moreover, we suggest that this trend is explained by avoidance from forming RNA secondary structures around the stop codon, which may interfere with normal translation termination.

Denisovans are an extinct group of humans whose morphology remains unknown. Here, we present a method for reconstructing skeletal morphology using DNA methylation patterns. Our method is based on linking unidirectional methylation changes to loss-of-function phenotypes. We tested performance by reconstructing Neanderthal and chimpanzee skeletal morphologies and obtained >85% precision in identifying divergent traits. We then applied this method to the Denisovan and offer a putative morphological profile. We suggest that Denisovans likely shared with Neanderthals traits such as an elongated face and a wide pelvis. We also identify Denisovan-derived changes, such as an increased dental arch and lateral cranial expansion. Our predictions match the only morphologically informative Denisovan bone to date, as well as the Xuchang skull, which was suggested by some to be a Denisovan. We conclude that DNA methylation can be used to reconstruct anatomical features, including some that do not survive in the fossil record.

Precision medicine and sequence-based clinical diagnostics seek to predict disease risk or to identify causative variants from sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype-phenotype prediction challenges; participants build models, undergo assessment, and share key findings. In the past, few CAGI challenges have addressed the impact of sequence variants on splicing. In CAGI 5, two challenges (Vex-seq and MaPSY) involved prediction of the effect of variants, primarily single nucleotide changes, on splicing. Although there are significant differences between these two challenges, both involved prediction of results from high-throughput exon-inclusion assays. Here, we discuss the methods used to predict the impact of these variants on splicing, their performance, strengths and weaknesses, and prospects for predicting the impact of sequence variation on splicing and disease phenotypes.

MicroRNAs (miRNAs) are short non-coding RNAs that negatively regulate the expression and translation of genes in healthy and diseased tissues. Herein, we characterize short RNAs from human HeLa cells found in the supraspliceosome, a nuclear dynamic machine in which pre-mRNA processing occurs. We sequenced small RNAs (<200 nt) extracted from the supraspliceosome, and identified sequences that are derived from 200 miRNAs genes. About three quarters of them are mature miRNAs, whereas the rest account for various defined regions of the pre-miRNA, and its hairpin-loop precursor. Out of these aligned sequences, 53 were undetected in cellular extract, and the abundance of additional 48 strongly differed from that in cellular extract. Notably, we describe seven abundant miRNA-derived sequences that overlap non-coding exons of their host gene. The rich collection of sequences identical to pre-miRNAs at the supraspliceosome suggests overlooked nuclear functions. Specifically, the abundant hsa-mir-99b may affect splicing of LINC01129 primary transcript through base-pairing with its exon-intron junction. Using suppression and overexpression experiments, we show that hsa-mir-7704 negatively regulates the level of the lncRNA HAGLR. We claim that in cases of extended base-pairing complementarity, such supraspliceosomal pre-miRNA sequences might have a role in transcription attenuation, maturation and processing.

In mammals, transposable elements are largely silenced, but under fortuitous circumstances may be co-opted to play a functional role. Here, we show that when Alu elements are inserted within or nearby genes in sense orientation, they may contribute to the transcriptome diversity by forming new cleavage and polyadenylation sites. We mapped these new gene ends in human onto the Alu sequence and identified three hotspots of cleavage and polyadenylation site formation. Interestingly, the native Alu sequence does not contain any canonical polyadenylation signal. We therefore studied what evolutionary processes might explain the formation of these specific hotspots of novel gene ends. We show that two of the three hotspots might have emerged from mutational processes that turned sequences that resemble polyadenylation signals into full-blown canonical signals, whereas one hotspot is tightly linked to the process of Alu insertion into the genome. Overall, Alu elements may lie behind the formation of 302 new gene end variants, affecting a total of 243 genes. Intergenic Alu elements may elongate genes by creating a downstream cleavage site, intronic Alu elements may lead to gene variants which code for truncated proteins, and 3'UTR Alu elements may result in gene variants with alternative 3'UTR.
Keywords: Alu elements, exaptation, polyadenylation signals, nicking signals, gene-end, transcriptome repertoire

Primate-specific Alu short interspersed elements (SINEs) as well as rodent-specific B and ID (B/ID) SINEs can promote Staufen-mediated decay (SMD) when present in mRNA 3'-untranslated regions (3'-UTRs). The transposable nature of SINEs, their presence in long noncoding RNAs, their interactions with Staufen, and their rapid divergence in different evolutionary lineages suggest they could have generated substantial modification of posttranscriptional gene-control networks during mammalian evolution. Some of the variation in SMD regulation produced by SINE insertion might have had a similar regulatory effect in separate mammalian lineages, leading to parallel evolution of the Staufen network by independent expansion of lineage-specific SINEs. To explore this possibility, we searched for orthologous gene pairs, each carrying a species-specific 3'-UTR SINE and each regulated by SMD, by measuring changes in mRNA abundance after individual depletion of two SMD factors, Staufen1 (STAU1) and UPF1, in both human and mouse myoblasts. We identified and confirmed orthologous gene pairs with 3'-UTR SINEs that independently function in SMD control of myoblast metabolism. Expanding to other species, we demonstrated that SINE-directed SMD likely emerged in both primate and rodent lineages >20-25 million years ago. Our work reveals a mechanism for the convergent evolution of posttranscriptional gene regulatory networks in mammals by species-specific SINE transposition and SMD.

Analyzing the conditions in which past individuals lived is key to understanding the environments and cultural transitions to which humans had to adapt. Here, we suggest a methodology to probe into past environments, using reconstructed pre-mortem DNA methylation maps of ancient individuals. We review a large body of research showing that differential DNA methylation is associated with changes in various external and internal factors, and propose that loci whose DNA methylation level is environmentally-responsive could serve as markers to infer about ancient daily life, diseases, nutrition, exposure to toxins and more. We demonstrate this approach by showing that hunger-related DNA methylation changes are found in ancient hunter-gatherers. The strategy we present here opens a window to reconstruct previously inaccessible aspects of the lives of past individuals.

Many human introns carry out a function, in the sense that they are critical to maintain normal cellular activity. Their identification is fundamental to understanding cellular processes and disease. However, being noncoding elements, such functional introns are poorly predicted based on traditional approaches of sequence and structure conservation. Here, we generated a dataset of human functional introns that carry out different types of functions. We showed that functional introns share common characteristics, such as higher positional conservation along the coding sequence and reduced loss rates, regardless of their specific function. A unique property of the data is that if an intron is unknown to be functional, it still does not mean that it is indeed non-functional. We developed a probabilistic framework that explicitly accounts for this unique property, and predicts which specific human introns are functional. We show that we successfully predict function even when the algorithm is trained on introns with a different type of function. This ability has many implications in studying regulatory networks, gene regulation, the effect of mutations outside exons on human disease, and on our general understanding of intron evolution and their functional exaptation in mammals.
Keywords: Intron evolution, intron function, uncertain labels, intron positional consrevation, gene architecture

One of the biggest challenges in studying how genes work is understanding their effect on the physiology and anatomy of the body. Existing tools try to address this using indirect features, such as expression levels and biochemical pathways. Here, we present Gene ORGANizer (geneorganizer.huji.ac.il), a phenotype-based tool that directly links human genes to the body parts they affect. It is built upon an exhaustive curated database that links >7000 genes to ?150 anatomical parts using >150 000 gene-organ associations. The tool offers user-friendly platforms to analyze the anatomical effects of individual genes, and identify trends within groups of genes. We demonstrate how Gene ORGANizer can be used to make new discoveries, showing that chromosome X is enriched with genes affecting facial features, that positive selection targets genes with more constrained phenotypic effects, and more. We expect Gene ORGANizer to be useful in a variety of evolutionary, medical and molecular studies aimed at understanding the phenotypic effects of genes.

A recent study conducted the first genome-wide scan for selection in Inuit from Greenland using SNP chip data. Here, we report that selection in the region with the second most extreme signal of positive selection in Greenlandic Inuit favored a deeply divergent haplotype that is closely related to the sequence in the Denisovan genome, and was likely introgressed from an archaic population. The region contains two genes, WARS2 and TBX15, and has previously been associated with body-fat distribution in humans. We show that the adaptively introgressed allele has been under selection in a much larger geographic region than just Greenland. Furthermore, it is associated with changes in expression of WARS2 and TBX15 in multiple tissues including the adrenal gland and subcutaneous adipose tissue.

Understanding the ecological function of an animal's pigmentation pattern is an intriguing research challenge. We used quantitative information on lizard foraging behavior to search for movement correlates of patterns across taxa. We hypothesized that noticeable longitudinal stripes that enhance escape by motion-dazzle are advantageous for mobile foragers that are highly detectable against the stationary background. Cryptic pigmentation patterns are beneficial for less-mobile foragers that rely on camouflage to reduce predation. Using an extensive literature survey and phylogenetically-controlled analyses, we found that striped lizards were substantially more mobile than lizards with cryptic patterns. The percent of time spent moving was the major behavioral index responsible for this difference. We provide empirical support for the hypothesized association between lizard dorsal pigmentation patterns and foraging behavior. Our simple yet comprehensive explanation may be relevant to many other taxa that present variation in body pigmentation patterns.

Background. It is not fully understood how a termination codon is recognized as premature (PTC) by the nonsense-mediated decay (NMD) machinery. This is particularly true for transcripts lacking an exon junction complex (EJC) along their 3' untranslated region (3'UTR), and thus degrade through the EJC-independent NMD pathway.
Results. Here, we analyzed data of transcript stability change following NMD repression and identified over 200 EJC-independent NMD-targets. We examined many features characterizing these transcripts, and compared them to NMD-insensitive transcripts, as well as to a group of transcripts that are destabilized following NMD repression (destabilized transcripts).
Conclusions. We found that none of the known NMD-triggering features, such as the presence of upstream open reading frames, significantly characterizes EJC-independent NMD-targets. Instead, we saw that NMD-targets are strongly enriched with G nucleotides upstream of the termination codon, and even more so along their 3'UTR. We suggest that high G content around the termination codon impedes translation termination as a result of mRNA folding, thus triggering NMD. We also suggest that high G content in the 3'UTR helps to activate NMD by allowing for the accumulation of UPF1, or other NMD-promoting proteins, along the 3'UTR.
Keywords. Nonsense-mediated decay (NMD), EJC-independent NMD, NMD-triggering features, Stop codon GC content, Stop codon nucleotide composition, RNA secondary structure, Exon junction complex (EJC), Transcription termination

Recessive CRB2 mutations were recently reported to cause both steroid resistant nephrotic syndrome and prenatal onset ventriculomegaly with kidney disease. We report two Ashkenazi Jewish siblings clinically diagnosed with ciliopathy. Both presented with severe congenital hydrocephalus and mild urinary tract anomalies. One affected sibling also has lung hypoplasia and heart defects. Exome sequencing and further CRB2 analysis revealed that both siblings are compound heterozygotes for CRB2 mutations p.N800K and p.Gly1036Alafs*43, and heterozygous for a deleterious splice variant in the ciliopathy gene TTCB21. CRB2 is a polarity protein which plays a role in ciliogenesis and ciliary function. Biallelic CRB2 mutations in animal models result in phenotypes consistent with ciliopathy. This report expands the phenotype of CRB2 mutations to include lung hypoplasia and uretero-pelvic renal anomalies, and confirms cardiac malformation as a feature. We suggest that CRB2-associated disease is a new ciliopathy syndrome with possible digenic/triallelic inheritance, as observed in other ciliopathies. Clinically, CRB2 should be assessed when ciliopathy is suspected, especially in Ashkenazi Jews, where we found that p.N800K carrier frequency is 1/64. Patients harboring CRB2 mutations should be tested for the full range of ciliopathy manifestations.

The T cell receptor (TCR) controls the cellular adaptive immune response to antigens, but our understanding of TCR repertoire diversity and response to challenge is still incomplete. For example, TCR clones shared by different individuals with minimal alteration to germline gene sequences (public clones) are detectable in all vertebrates, but their significance is unknown. Although small in size, the zebrafish TCR repertoire is controlled by processes similar to those operating in mammals. Thus, we studied the zebrafish TCR repertoire and its response to stimulation with self and foreign antigens. We found that cross-reactive public TCRs dominate the T cell response, endowing a limited TCR repertoire with the ability to cope with diverse antigenic challenges. These features of vertebrate public TCRs might provide a mechanism for the rapid generation of protective T cell immunity, allowing a short temporal window for the development of more specific private T cell responses.

Recent years have witnessed the rise of ancient DNA (aDNA) technology, allowing comparative genomics to be carried out at unprecedented time resolution. While it is relatively straightforward to use aDNA to identify recent genomic changes, it is much less clear how to utilize it to study changes in epigenetic regulation. Here we review recent works demonstrating that highly degraded aDNA still contains sufficient information to allow reconstruction of epigenetic signals, including DNA methylation and nucleosome positioning maps. We discuss challenges arising from the tissue specificity of epigenetics, and show how some of them might in fact turn into advantages. Finally, we introduce a method to infer methylation states in tissues that do not tend to be preserved over time.

Intron positions upon the mRNA transcript are sometimes remarkably conserved even across distantly related eukaryotic species. This has made the comparison of intron-exon architectures across orthologous transcripts a very useful tool for studying various evolutionary processes. Moreover, the wide range of functions associated with introns may confer biological meaning to evolutionary changes in gene architectures. Yet, there is currently no database that offers such comparative information. Here, we present JuncDB (http://juncdb.carmelab.huji.ac.il/), an exon-exon junction database dedicated to the comparison of architectures between orthologous transcripts. It covers nearly 40,000 sets of orthologous transcripts spanning 88 eukaryotic species. JuncDB offers a user-friendly interface, access to detailed information, instructive graphical displays of the comparative data and easy ways to download data to a local computer. In addition, JuncDB allows the analysis to be carried out either on specific genes, or at a genome-wide level for any selected group of species.

RNA-seq is becoming a preferred tool for genomics studies of model and non-model organisms. However, DNA-based analysis of organisms lacking sequenced genomes cannot rely on RNA-seq data alone to isolate most genes of interest, as DNA codes both exons and introns. With this in mind, we designed a novel tool, LEMONS, that exploits the evolutionary conservation of both exon/intron boundary positions and splice junction recognition signals to produce high throughput splice-junction predictions in the absence of a reference genome. When tested on multiple annotated vertebrate mRNA data, LEMONS accurately identified 87% (average) of the splice-junctions. LEMONS was then applied to our updated Mediterranean chameleon transcriptome, which lacks a reference genome, and predicted a total of 90,820 exon-exon junctions. We experimentally verified these splice-junction predictions by amplifying and sequencing twenty randomly selected genes from chameleon DNA templates. Exons and introns were detected in 19 of 20 of the positions predicted by LEMONS. To the best of our knowledge, LEMONS is currently the only experimentally verified tool that can accurately predict splice-junctions in organisms that lack a reference genome.

Background: Familial glucocorticoid deficiency (FGD) reflects specific failure of adrenocortical glucocorticoid production in response to adrenocorticotropic hormone (ACTH). Most cases are caused by mutations encoding ACTH-receptor components (MC2R, MRAP) or the general steroidogenesis protein (StAR). Recently, nicotinamide nucleotide transhydrogenase (NNT) mutations were found to cause FGD through a postulated mechanism resulting from decreased detoxification of reactive oxygen species (ROS) in adrenocortical cells.
Methods and Results: In a consanguineous Palestinian family with combined mineralocorticoid and glucocorticoid deficiency, whole-exome sequencing revealed a novel homozygous NNT_c.598 G>A, p.G200S, mutation. Another affected, unrelated Palestinian child was also homozygous for NNT_p.G200S. Haplotype analysis showed this mutation is ancestral; carrier frequency in ethnically matched controls is 1/200. Assessment of patient fibroblasts for ROS production, ATP content and mitochondrial morphology showed that biallelic NNT mutations result in increased levels of ROS, lower ATP content and morphological mitochondrial defects.
Conclusions: This report of a novel NNT mutation, p.G200S, expands the phenotype of NNT mutations to include mineralocorticoid deficiency. We provide the first patient-based evidence that NNT mutations can cause oxidative stress and both phenotypic and functional mitochondrial defects. These results directly demonstrate the importance of NNT to mitochondrial function in the setting of adrenocortical insufficiency.

Ancient DNA sequencing has recently provided high-coverage archaic human genomes. However, the evolution of epigenetic regulation along the human lineage remains largely unexplored. We reconstructed the full DNA methylation maps of the Neandertal and the Denisovan by harnessing the natural degradation processes of methylated and unmethylated cytosines. Comparing these ancient methylation maps to those of present-day humans, we identified ~2000 differentially methylated regions (DMRs). Particularly, we found substantial methylation changes in the HOXD cluster that may explain anatomical differences between archaic and present-day humans. Additionally, we found that DMRs are significantly more likely to be associated with diseases. This study provides insight into the epigenetic landscape of our closest evolutionary relatives, and opens a window to explore the epigenomes of extinct species.

An appreciable fraction of introns is thought to have some function, but there is no obvious way to predict which specific intron is likely to be functional. We hypothesize that functional introns experience a different selection regime than non-functional ones and will therefore show distinct evolutionary histories. In particular, we expect functional introns to be more resistant to loss, and that this would be reflected in high conservation of their position with respect to the coding sequence. To test this hypothesis, we focused on introns whose function comes about from microRNAs and snoRNAs that are embedded within their sequence. We built a data set of orthologous genes across 28 eukaryotic species, reconstructed the evolutionary histories of their introns and compared functional introns with the rest of the introns. We found that, indeed, the position of microRNA- and snoRNA-bearing introns is significantly more conserved. In addition, we found that both families of RNA genes settled within introns early during metazoan evolution. We identified several easily computable intronic properties that can be used to detect functional introns in general, thereby suggesting a new strategy to pinpoint non-coding cellular functions.

The serotonin 2C receptor (5-HT_2CR) - a key regulator of diverse neurological processes - exhibits functional variability derived from editing of its pre-mRNA by site-specific adenosine deamination (A-to-I pre-mRNA editing) in five distinct sites. Here we describe a statistical technique that was developed for analysis of the dependencies among the editing states of the five sites. The statistical significance of the observed correlations was estimated by comparing editing patterns in multiple individuals. For both human and rat 5-HT2CR, the editing states of the physically proximal sites A and B were found to be strongly dependent. In contrast, the editing states of sites C and D, which are also physically close, seem not to be directly dependent but instead are linked through the dependencies on sites A and B, respectively. We observed pronounced differences between the editing patterns in humans and rats: in humans site A is the key determinant of the editing state of the other sites, whereas in rats this role belongs to site B. The structure of the dependencies among the editing sites is notably simpler in rats than it is in humans implying more complex regulation of 5-HT_2CR editing and, by inference, function in the human brain. Thus, exhaustive statistical analysis of the 5-HT_2CR editing patterns indicates that the editing state of sites A and B is the primary determinant of the editing states of the other three sites, and hence the overall editing pattern. Taken together, these findings allow us to propose a mechanistic model of concerted action of ADAR1 and ADAR2 in 5-HT_2CR editing. Statistical approach developed here can be applied to other cases of interdependencies among modification sites in RNA and proteins.

Despite the clear major contribution of hyperlipidemia to the prevalence of cardiovascular disease in the developed world, the direct effects of lipoproteins on endothelial cells have remained obscure and are under debate. Here we report a previously uncharacterized mechanism of vessel growth modulation by lipoprotein availability. Using a genetic screen for vascular defects in zebrafish, we initially identified a mutation, stalactite (stl), in the gene encoding microsomal triglyceride transfer protein (mtp), which is involved in the biosynthesis of apolipoprotein B (ApoB)-containing lipoproteins. By manipulating lipoprotein concentrations in zebrafish, we found that ApoB negatively regulates angiogenesis and that it is the ApoB protein particle, rather than lipid moieties within ApoB-containing lipoproteins, that is primarily responsible for this effect. Mechanistically, we identified downregulation of vascular endothelial growth factor receptor 1 (VEGFR1), which acts as a decoy receptor for VEGF, as a key mediator of the endothelial response to lipoproteins, and we observed VEGFR1 downregulation in hyperlipidemic mice. These findings may open new avenues for the treatment of lipoprotein-related vascular disorders.

Evolution of exon-intron structure of eukaryotic genes has been a matter of long-standing, intensive debate. The introns-early concept, later rebranded 'introns first' held that protein-coding genes were interrupted by numerous introns even at the earliest stages of life's evolution and that introns played a major role in the origin of proteins by facilitating recombination of sequences coding for small protein/peptide modules. The introns-late concept held that introns emerged only in eukaryotes and new introns have been accumulating continuously throughout eukaryotic evolution. Analysis of orthologous genes from completely sequenced eukaryotic genomes revealed numerous shared intron positions in orthologous genes from animals and plants and even between animals, plants and protists, suggesting that many ancestral introns have persisted since the last eukaryotic common ancestor (LECA). Reconstructions of intron gain and loss using the growing collection of genomes of diverse eukaryotes and increasingly advanced probabilistic models convincingly show that the LECA and the ancestors of each eukaryotic supergroup had intron-rich genes, with intron densities comparable to those in the most intron-rich modern genomes such as those of vertebrates. The subsequent evolution in most lineages of eukaryotes involved primarily loss of introns, with only a few episodes of substantial intron gain that might have accompanied major evolutionary innovations such as the origin of metazoa. The original invasion of self-splicing Group II introns, presumably originating from the mitochondrial endosymbiont, into the genome of the emerging eukaryote might have been a key factor of eukaryogenesis that in particular triggered the origin of endomembranes and the nucleus. Conversely, splicing errors gave rise to alternative splicing, a major contribution to the biological complexity of multicellular eukaryotes. There is no indication that any prokaryote has ever possessed a spliceosome or introns in protein-coding genes, other than relatively rare mobile self-splicing introns. Thus, the introns-first scenario is not supported by any evidence but exon-intron structure of protein-coding genes appears to have evolved concomitantly with the eukaryotic cell, and introns were a major factor of evolution throughout the history of eukaryotes.

The intron-exon architecture of many eukaryotic genes raises the intriguing question of whether this unique organization serves any function, or is it simply a result of the spread of functionless introns in eukaryotic genomes. In this review, we show that introns in contemporary species fulfill a broad spectrum of functions, and are involved in virtually every step of mRNA processing. We propose that this great diversity of intronic functions supports the notion that introns were indeed selfish elements in early eukaryotes, but then independently gained numerous functions in different eukaryotic lineages. We suggest a novel criterion of evolutionary conservation, dubbed intron positional conservation, which can identify functional introns.

Intron density is highly variable across eukaryotic species. It seems that different lineages have experienced considerably different levels of intron gain and loss events, but the reasons for this are not well-known. A large number of mechanisms for intron loss and gain have been suggested, and most of them have at least some level of indirect support. We therefore figured out that the variability in intron density can be a reflection of the fact that different mechanisms are active in different lineages. Quite a number of these putative mechanisms, both for intron loss and for intron gain, postulate that the enzyme reverse-transcriptase has a key role in the process. In this paper we lay out three predictions whose approval or falsification gives indication for the involvement of reverse-transcriptase in intron gain and loss processes. Testing these predictions requires data on the intron gain and loss rates of individual genes along different branches of the eukaryotic phylogenetic tree. So far, such rates could not be computed, and hence these predictions could not be rigorously evaluated. Here, we use a maximum likelihood algorithm that we have devised in the past, EREM, which allows the estimation of such rates. Using this algorithm, we computed the intron loss and gain rates of more than 300 genes, in each branch of the phylogenetic tree of 19 eukaryotic species. Based on that, we found only little support for reverse-transcriptase activity in intron gain. In contrast, we suggest that reverse-transcriptase-mediated intron loss is a mechanism that is very efficient in removing introns, and thus its levels of activity may be a major determinant of intron number. Moreover, we found that intron gain and loss rates are negatively correlated in intron-poor species, but are positively correlated for intron-rich species. One explanation to this is that intron gain and loss mechanisms in intron-rich species (like metazoans) share a common mechanistic component, albeit not a reverse-transcriptase.

XX female gonadal dysgenesis (XX-GD) is a rare, genetically heterogeneous disorder characterized by lack of spontaneous pubertal development, primary amenorrhea, uterine hypoplasia, and hypergonadotropic hypogonadism as a result of streak gonads. Most cases are unexplained but thought to be autosomal recessive. We elucidated the genetic basis of XX-GD in a highly consanguineous Palestinian family by using homozygosity mapping and candidate-gene and whole-exome sequencing. Affected females were homozygous for a 3 bp deletion (NM_016556.2, c.600_602del) in the PSMC3IP gene, leading to deletion of a glutamic acid residue (p.Glu201del) in the highly conserved C-terminal acidic domain. Proteasome 26S subunit, ATPase, 3-Interacting Protein (PSMC3IP)/Tat Binding Protein Interacting Protein (TBPIP) is a nuclear, tissue-specific protein with multiple functions. It is critical for meiotic recombination as indicated by the known role of its yeast ortholog, Hop2. Through the C terminus (not present in yeast), PSMC3IP also coactivates ligand-driven transcription mediated by estrogen, androgen, glucocorticoid, progesterone, and thyroid nuclear receptors. In cell lines, the p.Glu201del mutation abolished PSMC3IP activation of estrogen-driven transcription. Impaired estrogenic signaling can lead to ovarian dysgenesis both by affecting the size of the follicular pool created during fetal development and by failing to counteract follicular atresia during puberty. PSMC3IP joins previous genes known to be mutated in XX-GD, the FSH receptor, and BMP15, highlighting the importance of hormonal signaling in ovarian development and maintenance and suggesting a common pathway perturbed in isolated XX-GD. By analogy to other XX-GD genes, PSMC3IP is also a candidate gene for premature ovarian failure, and its role in folliculogenesis should be further investigated.

We describe the draft genome of the microcrustacean Daphnia pulex, which is only 200 megabases and contains at least 30,907 genes. The high gene count is a consequence of an elevated rate of gene duplication resulting in tandem gene clusters. More than a third of Daphnia's genes have no detectable homologs in any other available proteome, and the most amplified gene families are specific to the Daphnia lineage. The coexpansion of gene families interacting within metabolic pathways suggests that the maintenance of duplicated genes is not random, and the analysis of gene expression under different environmental conditions reveals that numerous paralogs acquire divergent expression patterns soon after duplication. Daphnia-specific genes, including many additional loci within sequenced regions that are otherwise devoid of annotations, are the most responsive genes to ecological challenges.

Evolutionary binary characters are features of species or genes, indicating the absence (value zero) or presence (value one) of some property. Examples include eukaryotic gene architecture (the presence or absence of an intron in a particular locus), gene content, and morphological characters. In many studies, the acquisition of such binary characters is assumed to represent a rare evolutionary event, and consequently, their evolution is analyzed using various flavors of parsimony. However, when gain and loss of the character are not rare enough, a probabilistic analysis becomes essential. Here, we present a comprehensive probabilistic model to describe the evolution of binary characters on a bifurcating phylogenetic tree. A fast software tool, EREM, is provided, using maximum likelihood to estimate the parameters of the model and to reconstruct ancestral states (presence and absence in internal nodes) and events (gain and loss events along branches).

Analysis of gene architecture and expression levels of four organisms, Homo sapiens, Caenorhabiditis elegans, Drosophila melanogaster, and Arabidopsis thaliana, reveals a surprising, nonmonotonic, universal relationship between expression level and gene compactness. With increasing expression level, the genes tend at first to become longer but, from a certain level of expression, they become more and more compact, resulting in an approximate bell-shaped dependence. There are two leading hypotheses to explain the compactness of highly expressed genes. The selection hypothesis predicts that gene compactness is predominantly driven by the level of expression whereas the genomic design hypothesis predicts that expression breadth across tissues is the driving force. We observed that the connection between gene expression breadth in humans and gene compactness to be significantly weaker than the connection between expression level and compactness, a result that is compatible with the selection hypothesis but not the genome design hypothesis. The initial gene elongation with increasing expression level could be explained, at least in part, by accumulation of regulatory elements enhancing expression, in particular, in introns. This explanation is compatible with the observed positive correlation between intron density and expression level of a gene. Conversely, the trend toward increasing compactness for highly expressed genes could be caused by selection for minimization of energy and time expenditure during transcription and splicing, and for increased fidelity of transcription, splicing and/or translation that is likely to be particularly critical for highly expressed genes. Regardless of the exact nature of the forces that shape the gene architecture, we present evidence that, at least, in animals, coding and noncoding parts of genes show similar architectonic trends.
Keywords: eukaryotic gene structure, eukaryotic gene architecture, selection on gene compactness, genomic design, intron functionality, intron density

Enteric neural crest-derived cells (ENCCs) migrate along the intestine to form a highly organized network of ganglia that comprises the enteric nervous system (ENS). The signals driving the migration and patterning of these cells are largely unknown. Examining the spatiotemporal development of the intestinal neurovasculature in avian embryos, we find endothelial cells (ECs) present in the gut prior to the arrival of migrating ENCCs. These ECs are patterned in concentric rings that are predictive of the positioning of later arriving crest-derived cells, leading us to hypothesize that blood vessels may serve as a substrate to guide ENCC migration. Immunohistochemistry at multiple stages during ENS development reveals that ENCCs are positioned adjacent to vessels as they colonize the gut. A similar close anatomic relationship between vessels and enteric neurons was observed in zebrafish larvae. When EC development is inhibited in cultured avian intestine, ENCC migration is arrested and distal aganglionosis results, suggesting that ENCCs require the presence of vessels to colonize the gut. Neural tube and avian midgut were explanted onto a variety of substrates, including components of the extracellular matrix and various cell types, such as fibroblasts, smooth muscle cells, and endothelial cells. We find that crest-derived cells from both the neural tube and the midgut migrate avidly onto cultured endothelial cells. This EC-induced migration is inhibited by the presence of CSAT antibody, which blocks binding to β1 integrins expressed on the surface of crest-derived cells. These results demonstrate that ECs provide a substrate for the migration of ENCCs via an interaction between β1 integrins on the ENCC surface and extracellular matrix proteins expressed by the intestinal vasculature. These interactions may play an important role in guiding migration and patterning in the developing ENS.
Keywords: Enteric nervous system, Endothelial cells, Blood vessels, Hirschsprung's disease, Integrins, Avian, Zebrafish

To realize the promise of stem cell biology, it is important to identify the precise time in the history of the cell when developmental potential is restricted. To achieve this goal, we developed a real-time imaging system that captures the transitions in fate, generating neurons, astrocytes, and oligodendrocytes from single CNS stem cells in vitro. In the presence of bFGF, tripotent cells normally produce specified progenitors through a bipotent intermediate cell type. Surprisingly, the tripotent state is reset at each passage. The cytokine CNTF is thought to instruct multipotent cells to an astrocytic fate. We demonstrate that CNTF both directs astrogliogenesis from tripotent cells, bypassing two of the three normal bipotent intermediates, and later promotes the expansion of specified astrocytic progenitors. These results show how discrete cell types emerge from a multipotent cell and provide a strong basis for future studies to determine the molecular basis of fate specification.

We introduce a novel technique to determine the expression state of a gene from quantitative information measuring its expression. Adopting a productive abstraction from current thinking in molecular biology, we consider two expression states for a gene - Up or Down. We determine this state by using a statistical model that assumes the data behaves as a combination of two biological distributions. Given a cohort of hybridizations, our algorithm predicts, for the single reading, the probability of each gene's being in an Up or a Down state in each hybridization. Using a series of publicly available gene expression data sets, we demonstrate that our algorithm outperforms the prevalent algorithm. We also show that our algorithm can be used in conjunction with expression adjustment techniques to produce a more biologically sound gene-state call. The technique we present here enables a routine update, where the continuously evolving expression level adjustments feed into gene-state calculations. The technique can be applied in almost any multi-sample gene expression experiment, and holds equal promise for protein abundance experiments.

Background: Rare genomic changes (RGCs) that are thought to comprise derived shared characters of individual clades are becoming an increasingly important class of markers in genome-wide phylogenetic studies. Recently, we proposed a new type of RGCs designated RGC_CAMs (after Conserved Amino acids-Multiple substitutions) that were inferred using genome-wide identification of amino acid replacements that were: i) located in unambiguously aligned regions of orthologous genes, ii) shared by two or more taxa in positions that contain a different, conserved amino acid in a much broader range of taxa, and iii) require two or three nucleotide substitutions. When applied to animal phylogeny, the RGC_CAM approach supported the coelomate clade that unites deuterostomes with arthropods as opposed to the ecdysozoan (molting animals) clade. However, a non-negligible level of homoplasy was detected.
Results: We provide a direct estimate of the level of homoplasy caused by parallel changes and reversals among the RGC_CAMs using 462 alignments of orthologous genes from 19 eukaryotic species. It is shown that the impact of parallel changes and reversals on the results of phylogenetic inference using RGC_CAMs cannot explain the observed support for the Coelomata clade. In contrast, the evidence in support of the Ecdysozoa clade, in large part, can be attributed to parallel changes. It is demonstrated that parallel changes are significantly more common in internal branches of different subtrees that are separated from the respective common ancestor by relatively short times than in terminal branches separated by longer time intervals. A similar but much weaker trend was detected for reversals. The observed evolutionary trend of parallel changes is explained in terms of the covarion model of molecular evolution. As the overlap between the covarion sets in orthologous genes from different lineages decreases with time after divergence, the likelihood of parallel changes decreases as well.
Conclusions: The level of homoplasy observed here appears to be low enough to justify the utility of RGC_CAMs and other types of RGCs for resolution of hard problems in phylogeny. Parallel changes, one of the major classes of events leading to homoplasy, occur much more often in relatively recently diverged lineages than in those separated from their last common ancestor by longer time intervals of time. This pattern seems to provide the molecular-evolutionary underpinning of Vavilov's law of homologous series and is readily interpreted within the framework of the covarion model of molecular evolution.
Reviewers: This article was reviewed by Alex Kondrashov, Nicolas Galtier, and Maximilian Telford and Robert Lanfear (nominated by Laurence Hurst)

Numerous eukaryotic proteins contain multiple domains. Certain domains show a tendency to occur in diverse domain architectures and can be considered "promiscuous". These promiscuous domains are, typically, involved in protein-protein interactions and play crucial roles in interaction networks, particularly, those that contribute to signal transduction. A systematic comparative-genomic analysis of promiscuous domains in eukaryotes is described. Two quantitative measures of domain promiscuity are introduced and applied to the analysis of 28 genomes of diverse eukaryotes. Altogether, 215 domains are identified as strongly promiscuous. The fraction of promiscuous domains in animals is shown to be significantly greater than that in fungi or plants. Evolutionary reconstructions indicate that domain promiscuity is a volatile, relatively fast-changing feature of eukaryotic proteins, with few domains remaining promiscuous throughout the evolution of eukaryotes. Some domains appear to have attained promiscuity independently in different lineages, e.g., animals and plants. It is proposed that promiscuous domains persist within a relatively small pool of evolutionarily stable domain combinations from which numerous rare architectures emerge during evolution. Domain promiscuity positively correlates with the number of experimentally detected domain interactions and with the strength of purifying selection affecting a domain. Thus, evolution of promiscuous domains seems to be constrained by the diversity of their interaction partners. The set of promiscuous domains is enriched for domains mediating protein-protein interactions that are involved in various forms of signal transduction, especially, in the ubiquitin system and in the chromatin. Thus, a limited repertoire of promiscuous domains makes a major contribution to the diversity and evolvability of eukaryotic proteomes and signaling networks.

Although the family of genes encoding for olfactory receptors was identified more than 15 years ago, the difficulty of functionally expressing these receptors in an heterologous system has, with only some exceptions, rendered the receptive range of given olfactory receptors largely unknown. Furthermore, even when successfully expressed, the task of probing such a receptor with thousands of odors/ligands remains daunting. Here we provide proof of concept for a solution to this problem. Using computational methods we tune an electronic nose to the receptive range of an olfactory receptor. We then use this electronic nose to predict the receptors' response to other odorants. Our method can be used to identify the receptive range of olfactory receptors, and can also be applied to other questions involving receptor-ligand interactions in non-olfactory settings.

Although the family of genes encoding for olfactory receptors was identified more than 15 years ago, the difficulty of functionally expressing these receptors in an heterologous system has, with only some exceptions, rendered the receptive range of given olfactory receptors largely unknown. Furthermore, even when successfully expressed, the task of probing such a receptor with thousands of odors/ligands remains daunting. Here we provide proof of concept for a solution to this problem. Using computational methods we tune an electronic nose to the receptive range of an olfactory receptor. We then use this electronic nose to predict the receptors' response to other odorants. Our method can be used to identify the receptive range of olfactory receptors, and can also be applied to other questions involving receptor-ligand interactions in non-olfactory settings.

Background: The presence of introns in protein-coding genes is a universal feature of eukaryotic genome organization, and the genes of multicellular eukaryotes, typically, contain multiple introns, a substantial fraction of which share position in distant taxa, such as plants and animals. Depending on the methods and data sets used, researchers have reached opposite conclusions on the causes of the high fraction of shared introns in orthologous genes from distant eukaryotes. Some studies conclude that shared intron positions reflect, almost entirely, a remarkable evolutionary conservation, whereas others attribute it to parallel gain of introns. To resolve these contradictions, it is crucial to analyze the evolution of introns by using a model that minimally relies on arbitrary assumptions.
Results: We developed a probabilistic model of evolution that allows for variability of intron gain and loss rates over branches of the phylogenetic tree, individual genes, and individual sites. Applying this model to an extended set of conserved eukaryotic genes, we find that parallel gain, on average, accounts for only ~8% of the shared intron positions. However, the distribution of parallel gains over the phylogenetic tree of eukaryotes is highly non-uniform. There are, practically, no parallel gains in closely related lineages, whereas for distant lineages, such as animals and plants, parallel gains appear to contribute up to 20% of the shared intron positions. In accord with these findings, we estimated that ancestral introns have a high probability to be retained in extant genomes, and conversely, that a substantial fraction of extant introns have retained their positions since the early stages of eukaryotic evolution. In addition, the density of sites that are available for intron insertion is estimated to be, approximately, one in seven basepairs.
Conclusions: We obtained robust estimates of the contribution of parallel gain to the observed sharing of intron positions between eukaryotic species separated by different evolutionary distances. The results indicate that, although the contribution of parallel gains varies across the phylogenetic tree, the high level of intron position sharing is due, primarily, to evolutionary conservation. Accordingly, numerous introns appear to persist in the same position over hundreds of millions of years of evolution. This is compatible with recent observations of a negative correlation between the rate of intron gain and coding sequence evolution rate of a gene, suggesting that at least some of the introns are functionally relevant.

Arrays of chemical sensors, known as electronic noses, yield a unique pattern for a given mixture of odors. Recently, there has been increasing interest in trying to mix odors such as to generate a desired response in the electronic nose. For the time being, this intriguing problem had been tackled only experimentally with the aid of specific apparatus. Here, we present an algorithmic solution to the problem. We demonstrate the algorithm on data that includes mixtures of up to five ingredients.
Keywords: odor communication, sniffer, whiffer, within-sniffer mix-to-mimic algorithm, electronic nose

Evolution of protein sequences is largely governed by purifying selection, with a small fraction of proteins evolving under positive selection. The evolution at synonymous positions in protein-coding genes is not nearly as well understood, with the extent and types of selection remaining, largely, unclear. A statistical test to identify purifying and positive selection at synonymous sites in protein-coding genes was developed. The method compares the rate of evolution at synonymous sites (Ks) to that in intron sequences of the same gene after sampling the aligned intron sequences to mimic the statistical properties of coding sequences. We detected purifying selection at synonymous sites in 28% of the 1562 analyzed orthologous genes from mouse and rat, and positive selection in 12% of the genes. Thus, the fraction of genes with readily detectable positive selection at synonymous sites is much greater than the fraction of genes with comparable positive selection at non-synonymous sites, i.e., at the level of the protein sequence. Unlike other genes, the genes with positive selection at synonymous sites showed no correlation between Ks and the rate of evolution in non-synonymous sites (Ka), indicating that evolution of synonymous sites under positive selection is decoupled from protein evolution. The genes with purifying selection at synonymous sites showed significant anticorrelation between Ks and expression level and breadth indicating that highly expressed genes evolve slowly. The genes with positive selection at synonymous sites showed the opposite trend, i.e., highly expressed genes had, on average, higher Ks. For the genes with positive selection at synonymous sites, a significantly lower mRNA stability is predicted compared to the genes with negative selection. Thus, mRNA destabilization could be an important factor driving positive selection in non-synonymous sites, probably, through regulation of expression at the level of mRNA degradation and, possibly, also translation rate. So, unexpectedly, we found that positive selection at synonymous sites of mammalian genes is substantially more common than positive selection at the level of protein sequences. Positive selection at synonymous sites migh.
Keywords: synonymous sites, non-synonymous sites, positive selection, purifying selection, introns

Introns that interrupt eukaryotic protein-coding sequences are generally thought to be nonfunctional. However, for reasons still poorly understood, positions of many introns are highly conserved in evolution. Previous reconstructions of intron gain and loss events during eukaryotic evolution used a variety of simplified evolutionary models that yielded contradicting conclusions and are not suited to reveal some of the key underlying processes. We combine a comprehensive probabilistic model and an extended data set, including 391 conserved genes from 19 eukaryotes, to uncover previously unnoticed aspects of intron evolution - in particular, to assign intron gain and loss rates to individual genes. The rates of intron gain and loss in a gene show moderate positive correlation. A gene's intron gain rate shows a highly significant negative correlation with the coding-sequence evolution rate; intron loss rate also significantly, but positively, correlates with the sequence evolution rate. Correlations of the opposite signs, albeit less significant ones, are observed between intron gain and loss rates and gene expression level. It is proposed that intron evolution includes a neutral component, which is manifest in the positive correlation between the gain and loss rates and a selection-driven component as reflected in the links between intron gain and loss and sequence evolution. The increased intron gain and decreased intron loss in evolutionarily conserved genes indicate that intron insertion often might be adaptive, whereas some of the intron losses might be deleterious. This apparent functional importance of introns is likely to be due, at least in part, to their multiple effects on gene expression.

Several contrasting scenarios have been proposed for the origin and evolution of spliceosomal introns, a hallmark of eukaryotic genes. A comprehensive probabilistic model to obtain a definitive reconstruction of intron evolution was developed and applied to 391 sets of conserved genes from 19 eukaryotic species. It is inferred that a relatively high intron density was reached early, i.e., the last common ancestor of eukaryotes contained >2.15 introns/kilobase, and the last common ancestor of multicellular life forms harbored 3.4 introns/kilobase, a greater intron density than in most of the extant fungi and in some animals. The rates of intron gain and intron loss appear to have been dropping during the last 1.3 billion years, with the decline in the gain rate being much steeper. Eukaryotic lineages exhibit three distinct modes of evolution of the intron-exon structure. The primary, balanced mode, apparently, operates in all lineages. In this mode, intron gain and loss are strongly and positively correlated, in contrast to previous reports on inverse correlation between these processes. The second mode involves an elevated rate of intron loss and is prevalent in several lineages, such as fungi and insects. The third mode, characterized by elevated rate of intron gain, is seen only in deep branches of the tree, indicating that bursts of intron invasion occurred at key points in eukaryotic evolution, such as the origin of animals. Intron dynamics could depend on multiple mechanisms, and in the balanced mode, gain and loss of introns might share common mechanistic features.

As the number of sequenced genomes from diverse walks of life rapidly increases, phylogenetic analysis is entering a new era: reconstruction of the evolutionary history of organisms on the basis of full-scale comparison of their genomes. In addition to brute force, genome-wide analysis of alignments, rare genomic characters (RGCs) that are thought to comprise derived shared characters of individual clades are increasingly used in genome-wide phylogenetic studies. We propose a new type of RGCs designated RGC_CAMs (after Conserved Amino acids-Multiple substitutions), which are inferred using a genome-scale analysis of protein and underlying nucleotide sequence alignments. The RGC_CAM approach utilizes amino acid residues conserved in major eukaryotic lineages, with the exception of a few species comprising a putative clade, and selects for phylogenetic inference only those amino acid replacements that require 2 or 3 nucleotide substitutions, in order to reduce homoplasy. The RGC_CAM analysis was combined with a procedure for rigorous statistical testing of competing phylogenetic hypotheses. The RGC_CAM method is shown to be robust to branch length differences and taxon sampling. When applied to animal phylogeny, the RGC_CAM approach strongly supports the coelomate clade that unites chordates with arthropods as opposed to the ecdysozoan (molting animals) clade. This conclusion runs against the view of animal evolution that is currently prevailing in the evo-devo community. The final solution to the coelomate-ecdysozoa controversy will require a much larger set of complete genome sequences representing diverse animal taxa. It is expected that RGC_CAM and other RGC-based methods will be crucial for these future, definitive phylogenetic studies.
Keywords: Phylogenetic analysis, cladistics, rare genomic changes, coelomata, ecdysozoa, microsporidia

The Lorentzian model is a powerful feature extraction technique for electronic noses. In a previous work, it was applied to single-peak transient signals and was shown to achieve lower classification error rate than other feature extraction techniques. Here, we generalize the Lorentzian model by showing how to apply it to transient signals that are comprised of more than a single peak. The model is based on a fast and robust fitting of the measured signals to a physically meaningful analytic curve. We show that this model fits equally well to sensors of different technologies and embeddings, suggesting its applicability to a diverse repertoire of sensors and analytic devices.
Keywords: feature extraction, electronic nose, signal processing, multiple peaks

Members of enzyme families catalyze similar reactions, but have evolved specific biological functions. Comprehensive determination of the substrate specificities of enzymes is the essential first step toward elucidation of these functions and contributions of individual enzymes to the metabolome. Haloacid dehalogenase (HAD)-like hydrolases are a vast superfamily of largely uncharacterized enzymes, with a few members shown to possess phosphatase, β-phosphoglucomutase, phosphonatase, and dehalogenase activities. Using a representative set of 80 phosphorylated substrates, we characterized the substrate specificities of 23 soluble HADs encoded in the Escherichia coli genome. We identified small molecule phosphatase activity in 21 HADs and β-phosphoglucomutase activity in one protein. The E. coli HAD phosphatases show high catalytic efficiency and affinity to a wide range of phosphorylated metabolites (sugars, nucleotides, organic acids, cofactors) that are intermediates of various metabolic reactions (glycolysis, pentose phosphate pathway, gluconeogenesis, intermediary sugar and nucleotide metabolism). Rather than following the classical "one enzyme - one substrate" model, most of the E. coli HADs show remarkably broad and overlapping substrate spectra. At least 12 reactions catalyzed by HADs currently have no EC numbers assigned in the Enzyme Nomenclature. Surprisingly, most HADs hydrolyzed small phosphodonors (acetyl phosphate, carbamoyl phosphate, phosphoramidate), which also serve as substrates for autophoshorylation of the receiver domains of the two-component signal transduction systems. The physiological relevance of the phosphatase activity with the preferred substrate was validated in vivo for one of the HADs, YniC. Many of the secondary activities of HADs might have no immediate physiological function, but could comprise a reservoir for evolution of novel phosphatases.

Recent genome analyses revealed intriguing correlations between variables characterizing the functioning of a gene, such as expression level (EL), connectivity of genetic and protein-protein interaction networks, and knockout effect, and variables describing gene evolution, such as sequence evolution rate (ER) and propensity for gene loss. Typically, variables within each of these classes are positively correlated, e.g. products of highly expressed genes also have a propensity to be involved in many protein-protein interactions, whereas variables between classes are negatively correlated, e.g. highly expressed genes, on average, evolve slower than weakly expressed genes. Here, we describe principal component (PC) analysis of seven genome-related variables and propose biological interpretations for the first three PCs. The first PC reflects a gene's 'importance', or the 'status' of a gene in the genomic community, with positive contributions from knockout lethality, EL, number of protein-protein interaction partners and the number of paralogues, and negative contributions from sequence ER and gene loss propensity. The next two PCs define a plane that seems to reflect the functional and evolutionary plasticity of a gene. Specifically, PC2 can be interpreted as a gene's 'adaptability' whereby genes with high adaptability readily duplicate, have many genetic interaction partners and tend to be non-essential. PC3 also might reflect the role of a gene in organismal adaptation albeit with a negative rather than a positive contribution of genetic interactions; we provisionally designate this PC 'reactivity'. The interpretation of PC2 and PC3 as measures of a gene's plasticity is compatible with the observation that genes with high values of these PCs tend to be expressed in a condition- or tissue-specific manner. Functional classes of genes substantially vary in status, adaptability and reactivity, with the highest status characteristic of the translation system and cytoskeletal proteins, highest adaptability seen in cellular processes and signalling genes, and top reactivity characteristic of metabolic enzymes.
Keywords: gene expression, gene dispensability, protein-protein interaction, sequence evolution rate, gene loss, principal component analysis

Objectives: To identify the underlying gene expression profiles of unexplained chronic fatigue subjects classified into five or six class solutions by principal component (PCA) and latent class analyses (LCA).
Methods: Microarray expression data were available for 15,315 genes and 111 female subjects enrolled from a population-based study on chronic fatigue syndrome. Algorithms were developed to assign gene scores and threshold values that signified the contribution of each gene to discriminate the multiclasses in each LCA solution. Unsupervised dimensionality reduction was first used to remove noise or otherwise uninformative gene combinations, followed by supervised dimensionality reduction to isolate gene combinations that best separate the classes.
Results: The authors' gene score and threshold algorithms identified 32 and 26 genes capable of discriminating the five and six multiclass solutions, respectively. Pair-wise comparisons suggested that some genes (zinc finger protein 350 [ZNF350], solute carrier family 1, member 6 [SLC1A6], F-box protein 7 [FBX07] and vacuole 14 protein homolog [VAC14]) distinguished most classes of fatigued subjects from healthy subjects, whereas others (patched homolog 2 [PTCH2] and T-cell leukemia/lymphoma [TCL1A]) differentiated specific fatigue classes.
Conclusion: A computational approach was developed for general use to identify discriminatory genes in any multiclass problem. Using this approach, differences in gene expression were found to discriminate some classes of unexplained chronic fatigue, particularly one termed interoception.
Keywords: chronic fatigue syndrome, Fisher quotient and discriminatory genes, gene expression and gene scores, interoception, latent class analysis, principal component analysis

A fundamental question in studying odor patterns in electronic noses is how to estimate the response to a mixture, given the response curves of the pure chemicals. We study this question by proposing two mixture-predicting models, and verify them against real data collected using quartz microbalance sensors. We find that a simple additive law explains fairly well the measured response patterns of binary mixtures, but that a slightly more complicated mixing model is required in order to produce good estimations of the response patterns of mixtures that are comprised of more than two compounds.
Keywords: electronic noses, mixtures, response prediction, mixing model, law of mixing, quartz microbalance sensors

When measuring over-concentrated stimuli, chemical sensors tend to exhibit corrupted time signals, which are normally categorized as missing data. Such a failure of one or more sensors occurs frequently in applications where an eNose is exposed to a diverse repertoire of chemicals. As a rule, missing data are removed from the dataset by leaving a potentially large portion of the original dataset unutilized. Here we propose an algorithm to handle such missing data by utilizing intact regions of corrupted signals to restore the damaged regions. We do so by fitting a parametric model of the sensor response over time to the intact regions, and using the resulting model for the restoration. We show that the restoration is both accurate and consistent, thus allowing for the restored signals to take part in any subsequent data analysis process.
Keywords: electronic nose, signal restoration, missing data, signal corruption, signal failure

We consider the task of finding a mapping between two eNoses that employ two different sensor technologies, quartz microbalance and conducting polymers. Such a mapping is a model that predicts the response of one eNose based on the response of the other. eNose mappings are important for odor communication and synthesis, as well as for eNose data integration. We investigated a number of methods for performing this task, including principal components regression, partial least squares, neural networks and tessellation-based linear interpolation. Our measure of success is the percentage of predictions that are correctly classifiable. Using two different techniques for splitting our data set, we achieved success rates of 67% and 100%.

We present a novel family of data-driven linear transformations, aimed at finding low dimensional embeddings of multivariate data, in a way that optimally preserves the structure of the data. The well-studied PCA and Fisher's LDA are shown to be special members in this family of transformations, and we demonstrate how to generalize these two methods such as to enhance their performance. Furthermore, our technique is the only one, to the best of our knowledge, that reflects in the resulting embedding both the data coordinates and pairwise similarities and/or dissimilarities between the data elements. Even more so, when information on the clustering (labeling) decomposition of the data is known, this information can also be integrated in the linear transformation, resulting in embeddings that clearly show the separation between the clusters, as well as their internal structure. All this makes our technique very flexible and powerful, and lets us cope with kinds of data that other techniques fail to describe properly.
Index terms: dimensionality reduction, visualization, classification, feature extraction, projection, linear transformation, principal component analysis, Fisher's linear discriminant analysis

We present an algorithm for drawing directed graphs, which is based on rapidly solving a unique one-dimensional optimization problem for each of the axes. The algorithm results in a clear description of the hierarchy structure of the graph. Nodes are not restricted to lie on fixed horizontal layers, resulting in layouts that convey the symmetries of the graph very naturally. The algorithm can be applied without change to cyclic or acyclic digraphs, and even to graphs containing both directed and undirected edges. We also derive a hierarchy index from the input digraph, which quantitatively measures its amount of hierarchy.
Keywords: Directed graph drawing, force directed layout, hierarchy energy, Fiedler vector, minimum linear arrangement

We present an extremely fast graph drawing algorithm for very large graphs, which we term ACE (for Algebraic multigrid Computation of Eigenvectors). ACE exhibits a vast improvement over the fastest algorithms we are currently aware of; using a serial PC, it draws graphs of millions of nodes in less than a minute. ACE finds an optimal drawing by minimizing a quadratic energy function. The minimization problem is expressed as a generalized eigenvalue problem, which is solved rapidly using a novel algebraic multigrid technique. The same generalized eigenvalue problem seems to come up also in other fields, hence ACE appears to be applicable outside graph drawing too.
Keywords: algebraic multigrid, multiscale/multilevel optimization, graph drawing, generalized eigenvalue problem, Fiedler vector, force directed layout, the Hall energy

We propose an algorithm for use with multisensor systems that is capable of the following: a) identify an analyte independently of its concentration; b) estimate the concentration of the analyte, even if the system was not previously exposed to this concentration; c) tell when an analyte is of a chemical type not previously presented to the system. The algorithm, based upon recent work of Hopfield, uses the multiplicity of sensors explicitly, and is intuitive and easy to implement. We have tested it against real data, and it exhibits high quality performance.
Keywords: electronic noses, classification, identification, concentration estimation, reject option, Hopfield algorithm

We propose a new feature extraction method for use with chemical sensors. It is based on fitting a parametric analytic model of the sensor's response over time to the measured signal, and taking the set of best-fitting parameters as the features. The process of finding the features is fast and robust, and the resulting set of features is shown to significantly enhance the performance of subsequent classification algorithms. Moreover, the model that we have developed fits equally well to sensors of different technologies and embeddings, suggesting its applicability to a diverse repertoire of sensors and analytic devices.
Keywords: feature extraction, electronic nose, curve fitting, quartz-microbalance sensors, metal-oxide sensors

We propose a setup for an odor communication system. Its different parts are described, and ways to realize them are outlined. Our scheme enables an output device --- the whiffer --- to release an imitation of an odorant read in by an input device --- the sniffer --- upon command. The heart of the system is the novel algorithmic scheme that makes the scheme feasible. We are currently at work researching and developing some of the components that constitute the algorithm, and we hope that the description of the overall scheme in this paper will help to get other groups to join in this effort.
Keywords: odor communication system, palette odorants, odor space, odorant mixing, sniffer, whiffer

The concept of shape space, which has been successfully implemented in immunology, is used here to construct a model for the discrimination power of the olfactory system. Using reasonable assumptions on the behavior of the biological system, we are able to estimate the number of distinct olfactory receptor types. Our estimated value of around 1000 receptor types is in high agreement with experimental data.

Two-level systems were shown to be fully described by a single function, known sometimes as the Stueckelberg parameter. Using concepts from differential geometry, we give geometrical meaning to the Stueckelberg parameter and to other related quantities. As a result, a generalization of the Stueckelberg parameter is introduced, and a relation obtained between two-level systems and spatial one-dimensional curves in three-dimensional space. Previous authors used this Stueckelberg parameter to solve analytically several two-level models. We further develop this idea, and solve analytically three fundamental models, from which many other known models emerge as special cases. We present the detailed analysis of these models.

An appreciable fraction of introns is thought to be involved in cellular functions, but there is no obvious way to predict which specific intron is likely to be functional. For each intron we are given a feature representation that is based on its evolutionary patterns. For a small subsets of introns we are also given indication that they are functional. For all other introns it is not known whether they are functional or not. Our task is to estimate what fraction of introns are functional and, moreover, to estimate how likely each individual intron is to be functional. We define a probabilistic classification model treating the given functionality labels as noisy versions of labels created by a Deep Neural Network model. The maximum-likelihood model parameters are found by utilizing the Expectation-Maximization algorithm. As a result of our analysis we found that roughly 80% of the functional introns are still not recognized as such, and that roughly a third of all introns are functional.

We propose a detailed model of evolution of exon-intron structure of eukaryotic genes that takes into account gene-specific intron gain and loss rates, branch-specific gain and loss coefficients, invariant sites incapable of intron gain, and rate variability of both gain and loss which is gamma-distributed across sites. We develop an expectation-maximization algorithm to estimate the parameters of this model, and study its performance using simulated data.

We present a novel family of data-driven linear transformations, aimed at visualizing multivariate data in a low-dimensional space in a way that optimally preserves the structure of the data. The well-studied PCA and Fisher's LDA are shown to be special members in this family of transformations, and we demonstrate how to generalize these two methods such as to enhance their performance. Furthermore, our technique is the only one, to the best of our knowledge, that reflects in the resulting embedding both the data coordinates and pairwise similarities and/or dissimilarities between the data elements. Even more so, when information on the clustering (labeling) decomposition of the data is known, this information can be integrated in the linear transformation, resulting in embeddings that clearly show the separation between the clusters, as well as their intra-structure. All this make our technique very flexible and powerful, and let us cope with kinds of data that other techniques fail to describe properly.
Keywords: visualization, dimensionality-reduction, projection, principal component analysis, linear discriminant analysis, eigen-projection, classification

The Lorentzian model is an analytic expression that describes the time response of electronic nose sensors. We show how this model can be utilized to calculate a normalized similarity index between any two measurements. The set of similarity indices is then used for two purposes: visualization of the data, and classification of new samples. The visualization is carried out using graph drawing tools, and the results are shown to bear some desired properties. The classification is done using a majority-decision type algorithm, and is demonstrated to have very low error rate.
Keywords: electronic noses, similarity index, feature extraction, Lorentzian model, graph drawing, visualization, classification

We present an algorithm for drawing directed graphs, which is based on rapidly solving a unique one-dimensional optimization problem for each of the axes. The algorithm results in a clear description of the hierarchy structure of the graph. Nodes are not restricted to lie on fixed horizontal layers, resulting in layouts that convey the symmetries of the graph very naturally. The algorithm can be applied without change to cyclic or acyclic digraphs, and even to graphs containing both directed and undirected edges. We also derive a hierarchy index from the input digraph, which quantitatively measures its amount of hierarchy.

We present an extremely fast graph drawing algorithm for very large graphs, which we term ACE (for Algebraic multigrid Computation of Eigenvectors). ACE exhibits an improvement of something like two orders of magnitude over the fastest algorithms we are aware of; it draws graphs of millions of nodes in less than a minute. ACE finds an optimal drawing by minimizing a quadratic energy function. The minimization problem is expressed as a generalized eigenvalue problem, which is rapidly solved using a novel algebraic multigrid technique. The same generalized eigenvalue problem seems to come up also in other fields, hence ACE appears to be applicable outside of graph drawing too.
Keywords: algebraic multigrid, multiscale/multilevel optimization, graph drawing, generalized eigenvalue problem, Fiedler vector, force directed layout, the Hall energy

Spliceosomal introns are one of the principal distinctive features of eukaryotes. Nevertheless, different large-scale studies disagree about even the most basic features of their evolution. In order to come up with a more reliable reconstruction of intron evolution, we developed a model that is far more comprehensive than previous ones. This model is rich in parameters, and estimating them accurately is infeasible by straightforward likelihood maximization. Thus, we have developed an expectation-maximization algorithm that allows for efficient maximization. Here, we outline the model and describe the expectation-maximization algorithm in detail. Since the method works with intron presence-absence maps, it is expected to be instrumental for the analysis of the evolution of other binary characters as well.
Keywords: maximum likelihood, expectation-maximization, intron evolution, profile likelihood, ancestral reconstruction, eukaryotic gene structure

In addition to multiple, complete genome sequences, genome-wide data on biological prop properties of genes, such as knockout effect, expression levels, protein-protein interactions, and others, are rapidly accumulating. Numerous attempts were made by many groups to examine connections between these properties and quantitative measures of gene evolution. The questions addressed pertain to the most fundamental aspects of biology: what determines the effect of the knockout of a given gene on the phenotype (in particular, is it essential or not) and the rate of a gene's evolution and how are the phenotypic properties and evolution connected? Many significant correlations were detected, e.g., positive correlation between the tendency of a gene to be lost during evolution and sequence evolution rate, and negative correlations between each of the above measures of evolutionary variability and expression level or the phenotypic effect of gene knockout. However, most of these correlations are relatively weak and explain a small fraction of the variation present in the data. We propose that the majority of the relationships between the phenotypic ("input") and evolutionary ("output") variables can be described with a single, composite variable, the gene's "social status in the genomic community", which reflects the biological role of the gene and its mode of evolution. "High-status" genes, involved in house-keeping processes, are more likely to be higher and broader expressed, to have more interaction partners, and to produce lethal or severely impaired knockout mutants. These genes also tend to evolve slower and are less prone to gene loss across various taxonomic groups. "Low-status" genes are expected to be weakly expressed, have fewer interaction partners, and exhibit narrower (and less coherent) phyletic distribution. On average, these genes evolve faster and are more often lost during evolution than high-status genes. The "gene status" notion may serve as a generator of null hypotheses regarding the connections between phenotypic and evolutionary parameters associated with genes. Any deviation from the expected pattern calls for attention - to the quality of the data, the nature of the analyzed relationship, or both.

DNA is a central component in the cells of all living organisms, which contains information that is critical for building the body and maintaining its biological processes. Today, owing to new technological developments, scientists can obtain DNA directly from remains of humans and other organisms that died a long time ago. The ability to read ancient DNA allows us to learn new facts about the lives of the ancients, to study genetic changes that shaped present-day organisms, and to get new insights on fascinating evolutionary questions. For example, how did the Neanderthals evolve, and why did mammoths go extinct. Owing to ancient DNA, a mysterious group of archaic humans was discovered, although almost nothing is known about their appearance. Additional important information can be revealed by looking at epigenetics, which are mechanisms that affect the way genes work. For example, through epigenetics we identified fascinating changes in the structure of the voice box (formally called larynx) of modern humans, which possibly allowed us to produce rich sounds and develop a complex language. The ancient DNA revolution provides us with new tools to learn about event that happened many years ago.