Non-random autosome segregation: a stepping stone for the evolution of sex chromosome complexes?

A new study in Caenorhabditis elegans shows that homologous autosomes segregate non-randomly with the sex chromosome in the heterogametic sex. Segregation occurs according to size, small autosomes segregating with, and large autosomes segregating away from the X-chromosome. Such sex-biased transmission of autosomes could facilitate the spread of sexually antagonistic alleles whose effects favor the fitness of one sex at the expense of the other. This may provide a first step toward the evolution of new sex determination systems.

Life-history evolution and the polyphenic regulation of somatic maintenance and survival.

Here we discuss life-history evolution from the perspective of adaptive phenotypic plasticity, with a focus on polyphenisms for somatic maintenance and survival. Polyphenisms are adaptive discrete alternative phenotypes that develop in response to changes in the environment. We suggest that dauer larval diapause and its associated adult phenotypes in the nematode (Caenorhabditis elegans), reproductive dormancy in the fruit fly (Drosophila melanogaster) and other insects, and the worker castes of the honey bee (Apis mellifera) are examples of what may be viewed as the polyphenic regulation of somatic maintenance and survival. In these and other cases, the same genotype can--depending upon its environment--express either of two alternative sets of life-history phenotypes that differ markedly with respect to somatic maintenance, survival ability, and thus life span. This plastic modulation of somatic maintenance and survival has traditionally been underappreciated by researchers working on aging and life history. We review the current evidence for such adaptive life-history switches and their molecular regulation and suggest that they are caused by temporally and/or spatially varying, stressful environments that impose diversifying selection, thereby favoring the evolution of plasticity of somatic maintenance and survival under strong regulatory control. By considering somatic maintenance and survivorship from the perspective of adaptive life-history switches, we may gain novel insights into the mechanisms and evolution of aging.

High-throughput SELEX SAGE method for quantitative modeling of transcription-factor binding sites.

The ability to determine the location and relative strength of all transcription-factor binding sites in a genome is important both for a comprehensive understanding of gene regulation and for effective promoter engineering in biotechnological applications. Here we present a bioinformatically driven experimental method to accurately define the DNA-binding sequence specificity of transcription factors. A generalized profile was used as a predictive quantitative model for binding sites, and its parameters were estimated from in vitro-selected ligands using standard hidden Markov model training algorithms. Computer simulations showed that several thousand low- to medium-affinity sequences are required to generate a profile of desired accuracy. To produce data on this scale, we applied high-throughput genomics methods to the biochemical problem addressed here. A method combining systematic evolution of ligands by exponential enrichment (SELEX) and serial analysis of gene expression (SAGE) protocols was coupled to an automated quality-controlled sequence extraction procedure based on Phred quality scores. This allowed the sequencing of a database of more than 10,000 potential DNA ligands for the CTF/NFI transcription factor. The resulting binding-site model defines the sequence specificity of this protein with a high degree of accuracy not achieved earlier and thereby makes it possible to identify previously unknown regulatory sequences in genomic DNA. A covariance analysis of the selected sites revealed non-independent base preferences at different nucleotide positions, providing insight into the binding mechanism.

Simulations in Evolution. III. Randomness as a Generator of Opportunities.

In Neo-Darwinism, variation and natural selection are the two evolutionary mechanisms which propel biological evolution. Our previous reports presented a histogram model to simulate the evolution of populations of individuals classified into bins according to an unspecified, quantifiable phenotypic character, and whose number in each bin changed generation after generation under the influence of fitness, while the total population was maintained constant. The histogram model also allowed Shannon entropy (SE) to be monitored continuously as the information content of the total population decreased or increased. Here, a simple Perl (Practical Extraction and Reporting Language) application was developed to carry out these computations, with the critical feature of an added random factor in the percent of individuals whose offspring moved to a vicinal bin. The results of the simulations demonstrate that the random factor mimicking variation increased considerably the range of values covered by Shannon entropy, especially when the percentage of changed offspring was high. This increase in information content is interpreted as facilitated adaptability of the population.

Integrated methodologies to study human transcriptional regulation from functional genomics data

Abstract : The human body is composed of a huge number of cells acting together in a concerted manner. The current understanding is that proteins perform most of the necessary activities in keeping a cell alive. The DNA, on the other hand, stores the information on how to produce the different proteins in the genome. Regulating gene transcription is the first important step that can thus affect the life of a cell, modify its functions and its responses to the environment. Regulation is a complex operation that involves specialized proteins, the transcription factors. Transcription factors (TFs) can bind to DNA and activate the processes leading to the expression of genes into new proteins. Errors in this process may lead to diseases. In particular, some transcription factors have been associated with a lethal pathological state, commonly known as cancer, associated with uncontrolled cellular proliferation, invasiveness of healthy tissues and abnormal responses to stimuli. Understanding cancer-related regulatory programs is a difficult task, often involving several TFs interacting together and influencing each other's activity. This Thesis presents new computational methodologies to study gene regulation. In addition we present applications of our methods to the understanding of cancer-related regulatory programs. The understanding of transcriptional regulation is a major challenge. We address this difficult question combining computational approaches with large collections of heterogeneous experimental data. In detail, we design signal processing tools to recover transcription factors binding sites on the DNA from genome-wide surveys like chromatin immunoprecipitation assays on tiling arrays (ChIP-chip). We then use the localization about the binding of TFs to explain expression levels of regulated genes. In this way we identify a regulatory synergy between two TFs, the oncogene C-MYC and SP1. C-MYC and SP1 bind preferentially at promoters and when SP1 binds next to C-NIYC on the DNA, the nearby gene is strongly expressed. The association between the two TFs at promoters is reflected by the binding sites conservation across mammals, by the permissive underlying chromatin states 'it represents an important control mechanism involved in cellular proliferation, thereby involved in cancer. Secondly, we identify the characteristics of TF estrogen receptor alpha (hERa) target genes and we study the influence of hERa in regulating transcription. hERa, upon hormone estrogen signaling, binds to DNA to regulate transcription of its targets in concert with its co-factors. To overcome the scarce experimental data about the binding sites of other TFs that may interact with hERa, we conduct in silico analysis of the sequences underlying the ChIP sites using the collection of position weight matrices (PWMs) of hERa partners, TFs FOXA1 and SP1. We combine ChIP-chip and ChIP-paired-end-diTags (ChIP-pet) data about hERa binding on DNA with the sequence information to explain gene expression levels in a large collection of cancer tissue samples and also on studies about the response of cells to estrogen. We confirm that hERa binding sites are distributed anywhere on the genome. However, we distinguish between binding sites near promoters and binding sites along the transcripts. The first group shows weak binding of hERa and high occurrence of SP1 motifs, in particular near estrogen responsive genes. The second group shows strong binding of hERa and significant correlation between the number of binding sites along a gene and the strength of gene induction in presence of estrogen. Some binding sites of the second group also show presence of FOXA1, but the role of this TF still needs to be investigated. Different mechanisms have been proposed to explain hERa-mediated induction of gene expression. Our work supports the model of hERa activating gene expression from distal binding sites by interacting with promoter bound TFs, like SP1. hERa has been associated with survival rates of breast cancer patients, though explanatory models are still incomplete: this result is important to better understand how hERa can control gene expression. Thirdly, we address the difficult question of regulatory network inference. We tackle this problem analyzing time-series of biological measurements such as quantification of mRNA levels or protein concentrations. Our approach uses the well-established penalized linear regression models where we impose sparseness on the connectivity of the regulatory network. We extend this method enforcing the coherence of the regulatory dependencies: a TF must coherently behave as an activator, or a repressor on all its targets. This requirement is implemented as constraints on the signs of the regressed coefficients in the penalized linear regression model. Our approach is better at reconstructing meaningful biological networks than previous methods based on penalized regression. The method is tested on the DREAM2 challenge of reconstructing a five-genes/TFs regulatory network obtaining the best performance in the "undirected signed excitatory" category. Thus, these bioinformatics methods, which are reliable, interpretable and fast enough to cover large biological dataset, have enabled us to better understand gene regulation in humans.

Lateglacial evolution of the Brenno glacier (Southern Swiss Alps) : preliminary results

The Lateglacial evolution of the Ticino glacier and tributaries is poorly known because of the lack of research by Quaternary geomorphologists during the last decades. In spite of the interest for the cryosphere reactions during the Lateglacial climate warming, only few scientific studies were carried out about the history of the northern valleys of the Ticino Alps during the deglaciation (e.g. Seiffert 1953, Renner 1982, Hantke 1983). Within the framework of geomorphological investigations on the Lateglacial and Holocene glacier/permafrost evolution in the Ticino Alps, the history of the Brenno glacier (Blenio Valley, Eastern Ticino Alps) during the end of the Pleistocene has been studied. The deglaciation sequence of the Blenio Valley is still not complete (Scapozza et al. 2009). Only the first glacial stadial of the Brenno glacier and the last Lateglacial stadials of the Greina region (northern Blenio valley, see Fontana et al. 2008) and of the upper Malvaglia Valley (eastern Blenio Valley, see Scapozza et al. 2008) have been unequivocally defined. For every stadial, the surface of the palaeoglacier and the depression of the Equilibrium Line Altitude (ELA) have been reconstructed on the base of geomorphological mapping. The first individual glacial stadial of the Brenno glacier corresponds to the Biasca stadial of the Ticino glacier defined by Hantke (1983). The ELA depression of 1100-1200 meters and its morphological and glaciological characteristics allow us to correlate this stadial with the Weissbad stadial defined by Keller (1988). In the Greina region, three stadials corresponding to the end of the Lateglacial have been identified, with an ELA depression of 110, 210 and 310-350 meters (Fontana et al. 2008). In the upper Malvaglia Valley, three stadials corresponding to the end of the Oldest Dryas and the Younger Dryas have been identified for the Orino glacier, with an ELA depression of 290, 400-420 and 470-560 meters (Scapozza et al. 2008). If we consider the other (fragmentary) glacial deposits of the Blenio Valley, it is possible to define a regression sequence of the Brenno glacier with 8 stadials, from the Biasca stadial to the end of the Younger Dryas. An attempt of correlation with the model "Gothard" developed by Renner (1982) and Hantke (1983) and with the model "Eastern Swiss Alps" developed by Maisch (1982) is proposed in Table 1. The following chronological conclusions are, therefore, proposed: (1) the Biasca stadial is probably the first stadial after the transition Pleniglacial - Lateglacial; (2) the stadials BRE 7 to BRE 3 are positioned between the beginning of the Lateglacial and the Bølling-Allerød interstadial; (3) the stadials BRE 2 and BRE 1 are assumed to be related to the Younger Dryas event.

Identity [R]Evolution: Multi-Disciplinary Perpectives

The identity [r]evolution is happening. Who are you, who am I in the information society? In recent years, the convergence of several factors - technological, political, economic - has accelerated a fundamental change in our networked world. On a technological level, information becomes easier to gather, to store, to exchange and to process. The belief that more information brings more security has been a strong political driver to promote information gathering since September 11. Profiling intends to transform information into knowledge in order to anticipate one's behaviour, or needs, or preferences. It can lead to categorizations according to some specific risk criteria, for example, or to direct and personalized marketing. As a consequence, new forms of identities appear. They are not necessarily related to our names anymore. They are based on information, on traces that we leave when we act or interact, when we go somewhere or just stay in one place, or even sometimes when we make a choice. They are related to the SIM cards of our mobile phones, to our credit card numbers, to the pseudonyms that we use on the Internet, to our email addresses, to the IP addresses of our computers, to our profiles... Like traditional identities, these new forms of identities can allow us to distinguish an individual within a group of people, or describe this person as belonging to a community or a category. How far have we moved through this process? The identity [r]evolution is already becoming part of our daily lives. People are eager to share information with their "friends" in social networks like Facebook, in chat rooms, or in Second Life. Customers take advantage of the numerous bonus cards that are made available. Video surveillance is becoming the rule. In several countries, traditional ID documents are being replaced by biometric passports with RFID technologies. This raises several privacy issues and might actually even result in changing the perception of the concept of privacy itself, in particular by the younger generation. In the information society, our (partial) identities become the illusory masks that we choose -or that we are assigned- to interplay and communicate with each other. Rights, obligations, responsibilities, even reputation are increasingly associated with these masks. On the one hand, these masks become the key to access restricted information and to use services. On the other hand, in case of a fraud or negative reputation, the owner of such a mask can be penalized: doors remain closed, access to services is denied. Hence the current preoccupying growth of impersonation, identity-theft and other identity-related crimes. Where is the path of the identity [r]evolution leading us? The booklet is giving a glance on possible scenarios in the field of identity.

Comparative Genomics Suggests that the Fungal Pathogen Pneumocystis Is an Obligate Parasite Scavenging Amino Acids from Its Host's Lungs.

Pneumocystis jirovecii is a fungus causing severe pneumonia in immuno-compromised patients. Progress in understanding its pathogenicity and epidemiology has been hampered by the lack of a long-term in vitro culture method. Obligate parasitism of this pathogen has been suggested on the basis of various features but remains controversial. We analysed the 7.0 Mb draft genome sequence of the closely related species Pneumocystis carinii infecting rats, which is a well established experimental model of the disease. We predicted 8'085 (redundant) peptides and 14.9% of them were mapped onto the KEGG biochemical pathways. The proteome of the closely related yeast Schizosaccharomyces pombe was used as a control for the annotation procedure (4'974 genes, 14.1% mapped). About two thirds of the mapped peptides of each organism (65.7% and 73.2%, respectively) corresponded to crucial enzymes for the basal metabolism and standard cellular processes. However, the proportion of P. carinii genes relative to those of S. pombe was significantly smaller for the "amino acid metabolism" category of pathways than for all other categories taken together (40 versus 114 against 278 versus 427, P<0.002). Importantly, we identified in P. carinii only 2 enzymes specifically dedicated to the synthesis of the 20 standard amino acids. By contrast all the 54 enzymes dedicated to this synthesis reported in the KEGG atlas for S. pombe were detected upon reannotation of S. pombe proteome (2 versus 54 against 278 versus 427, P<0.0001). This finding strongly suggests that species of the genus Pneumocystis are scavenging amino acids from their host's lung environment. Consequently, they would have no form able to live independently from another organism, and these parasites would be obligate in addition to being opportunistic. These findings have implications for the management of patients susceptible to P. jirovecii infection given that the only source of infection would be other humans.

New Genetic and Linguistic Analyses Show Ancient Human Influence on Baobab Evolution and Distribution in Australia

This study investigates the role of human agency in the gene flow and geographical distribution of the Australian baobab, Adansonia gregorii. The genus Adansonia is a charismatic tree endemic to Africa, Madagascar, and northwest Australia that has long been valued by humans for its multiple uses. The distribution of genetic variation in baobabs in Africa has been partially attributed to human-mediated dispersal over millennia, but this relationship has never been investigated for the Australian species. We combined genetic and linguistic data to analyse geographic patterns of gene flow and movement of word-forms for A. gregorii in the Aboriginal languages of northwest Australia. Comprehensive assessment of genetic diversity showed weak geographic structure and high gene flow. Of potential dispersal vectors, humans were identified as most likely to have enabled gene flow across biogeographic barriers in northwest Australia. Genetic-linguistic analysis demonstrated congruence of gene flow patterns and directional movement of Aboriginal loanwords for A. gregorii. These findings, along with previous archaeobotanical evidence from the Late Pleistocene and Holocene, suggest that ancient humans significantly influenced the geographic distribution of Adansonia in northwest Australia.