MSMS 6211, BCOR 5585 - Genomics

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2008 - 2015

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1998 - 2015

The decade since the Human Genome Project ended has witnessed a remarkable sequencing technology explosion that has permitted a multitude of questions about the genome to be asked and answered, at unprecedented speed and resolution. Here I present examples of how the resulting information has both enhanced our knowledge and expanded the impact of the genome on biomedical research. New sequencing technologies have also introduced exciting new areas of biological endeavour. The continuing upward trajectory of sequencing technology development is enabling clinical applications that are aimed at improving medical diagnosis and treatment.

In just seven years, next-generation technologies have reduced the cost and increased the speed of DNA sequencing by four orders of magnitude, and experiments requiring many millions of sequencing reads are now routine. In research, sequencing is being applied not only to assemble genomes and to investigate the genetic basis of human disease, but also to explore myriad phenomena in organismic and cellular biology. In the clinic, the utility of sequence data is being intensively evaluated in diverse contexts, including reproductive medicine, oncology and infectious disease. A recurrent theme in the development of new sequencing applications is the creative 'recombination' of existing experimental building blocks. However, there remain many potentially high-impact applications of next-generation DNA sequencing that are not yet fully realized.

A DNA sequence isn't enough; to understand the workings of the genome, we must study chromosome structure.

Choanoflagellates are the closest known relatives of metazoans. To discover potential molecular mechanisms underlying the evolution of metazoan multicellularity, we sequenced and analysed the genome of the unicellular choanoflagellate Monosiga brevicollis. The genome contains approximately 9,200 intron-rich genes, including a number that encode cell adhesion and signalling protein domains that are otherwise restricted to metazoans. Here we show that the physical linkages among protein domains often differ between M. brevicollis and metazoans, suggesting that abundant domain shuffling followed the separation of the choanoflagellate and metazoan lineages. The completion of the M. brevicollis genome allows us to reconstruct with increasing resolution the genomic changes that accompanied the origin of metazoans.

Background
G protein-coupled receptors (GPCRs) are one of the largest families of genes in mammals. Branchiostoma floridae (amphioxus) is one of the species most closely related species to vertebrates.

Results
Mining and phylogenetic analysis of the amphioxus genome showed the presence of at least 664 distinct GPCRs distributed among all the main families of GPCRs; Glutamate (18), Rhodopsin (570), Adhesion (37), Frizzled (6) and Secretin (16). Surprisingly, the Adhesion GPCR repertoire in amphioxus includes receptors with many new domains not previously observed in this family. We found many Rhodopsin GPCRs from all main groups including many amine and peptide binding receptors and several previously uncharacterized expansions were also identified. This genome has however no genes coding for bitter taste receptors (TAS2), the sweet and umami (TAS1), pheromone (VR1 or VR2) or mammalian olfactory receptors.

Conclusion
The amphioxus genome is remarkably rich in various GPCR subtypes while the main GPCR groups known to sense exogenous substances (such as Taste 2, mammalian olfactory, nematode chemosensory, gustatory, vomeronasal and odorant receptors) in other bilateral species are absent.

In the past decade, numerous studies have made connections between sequence variants in human genomes and predisposition to complex diseases. However, most of these variants lie outside of the charted regions of thehuman genome whose function we understand; that is, the sequences that encode proteins. Consequently, the general concept of a mechanism that translates these variants into predisposition to diseases has been lacking, potentially calling into question the validity of these studies. Here we make a connection between the growing class of apparently functional RNAs that do not encode proteins and whose function we do not yet understand (the so-called ‘dark matter’ RNAs) and the disease-associated variants. We review advances made in a different genomic mapping effort – unbiased profiling of all RNA transcribed from the human genome – and provide arguments that the disease-associated variants exert their effects via perturbation of regulatory properties of non-coding RNAs existing in mammalian cells.

Sponges (Porifera) are among the earliest evolving metazoans. Their filter-feeding body plan based on choanocyte chambers organized into a complex aquiferous system is so unique among metazoans that it either reflects an early divergence from other animals prior to the evolution of features such as muscles and nerves, or that sponges lost these characters. Analyses of the Amphimedon and Oscarella genomes support this view of uniqueness—many key metazoan genes are absent in these sponges—but whether this is generally true of other sponges remains unknown. We studied the transcriptomes of eight sponge species in four classes (Hexactinellida, Demospongiae, Homoscleromorpha, and Calcarea) specifically seeking genes and pathways considered to be involved in animal complexity. For reference, we also sought these genes in transcriptomes and genomes of three unicellular opisthokonts, two sponges (A. queenslandica and O. carmela), and two bilaterian taxa. Our analyses showed that all sponge classes share an unexpectedly large complement of genes with other metazoans. Interestingly, hexactinellid, calcareous, and homoscleromorph sponges share more genes with bilaterians than with nonbilaterian metazoans. We were surprised to find representatives of most molecules involved in cell–cell communication, signaling, complex epithelia, immune recognition, and germ-lineage/sex, with only a few, but potentially key, absences. A noteworthy finding was that some important genes were absent from all demosponges (transcriptomes and the Amphimedon genome), which might reflect divergence from main-stem lineages including hexactinellids, calcareous sponges, and homoscleromorphs. Our results suggest that genetic complexity arose early in evolution as shown by the presence of these genes in most of the animal lineages, which suggests sponges either possess cryptic physiological and morphological complexity and/or have lost ancestral cell types or physiological processes.

Considering that 80 genomes have been sequenced, providing us with the static information of the genome, it is still a long way to reveal the relationship between complex genotypes and phenotypes. The transcriptional regulation process is one of the obstacles that need to be understood to bridge our current information gap. It describes the first step from the genomic sequence information to RNA templates used for protein production or as direct functional units, like non-coding RNAs (e.g. micro RNAs). This introduction aims to highlight the key aspects of the transcriptional process from our current understanding.

How microbial communities change over time in response to the environment is poorly understood. Previously a six-year time series of 16S rRNA V6 data from the Western English Channel demonstrated robust seasonal structure within the bacterial community, with diversity negatively correlated with day-length. Here we determine whether metagenomes and metatranscriptomes follow similar patterns. We generated 16S rRNA datasets, metagenomes (1.2 GB) and metatranscriptomes (157 MB) for eight additional time points sampled in 2008, representing three seasons (Winter, Spring, Summer) and including day and night samples. This is the first microbial ‘multi-omic’ study to combine 16S rRNA amplicon sequencing with metagenomic and metatranscriptomic profiling. Five main conclusions can be drawn from analysis of these data: 1) Archaea follow the same seasonal patterns as Bacteria, but show lower relative diversity; 2) Higher 16S rRNA diversity also reflects a higher diversity of transcripts; 3) Diversity is highest in winter and at night; 4) Community-level changes in 16S-based diversity and metagenomic profiles are better explained by seasonal patterns (with samples closest in time being most similar), while metatranscriptomic profiles are better explained by diel patterns and shifts in particular categories (i.e., functional groups) of genes; 5) Changes in key genes occur among seasons and between day and night (i.e., photosynthesis); but these samples contain large numbers of orphan genes without known homologues and it is these unknown gene sets that appear to contribute most towards defining the differences observed between times. Despite the huge diversity of these microbial communities, there are clear signs of predictable patterns and detectable stability over time. Renewed and intensified efforts are required to reveal fundamental deterministic patterns in the most complex microbial communities. Further, the presence of a substantial proportion of orphan sequences underscores the need to determine the gene products of sequences with currently unknown function.

Background
Understanding the normal temporal variation in the human microbiome is critical to developing treatments for putative microbiome-related afflictions such as obesity, Crohn's disease, inflammatory bowel disease and malnutrition. Sequencing and computational technologies, however, have been a limiting factor in performing dense time series analysis of the human microbiome. Here, we present the largest human microbiota time series analysis to date, covering two individuals at four body sites over 396 timepoints.
Results
We find that despite stable differences between body sites and individuals, there is pronounced variability in an individual's microbiota across months, weeks and even days. Additionally, only a small fraction of the total taxa found within a single body site appear to be present across all time points, suggesting that no core temporal microbiome exists at high abundance (although some microbes may be present but drop below the detection threshold). Many more taxa appear to be persistent but non-permanent community members.
Conclusions
DNA sequencing and computational advances described here provide the ability to go beyond infrequent snapshots of our human-associated microbial ecology to high-resolution assessments of temporal variations over protracted periods, within and between body habitats and individuals. This capacity will allow us to define normal variation and pathologic states, and assess responses to therapeutic interventions.