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Background: Whole genome sequencing is increasingly being used for the diagnosis of patients with rare diseases. However, the diagnostic yields of many studies, particularly those conducted in a healthcare setting, are often disappointingly low, at 25-30%. This is in part because although entire genomes are sequenced, analysis is often confined to in silico gene panels or coding regions of the genome., Methods: We undertook WGS on a cohort of 122 unrelated rare disease patients and their relatives (300 genomes) who had been pre-screened by gene panels or arrays. Patients were recruited from a broad spectrum of clinical specialties. We applied a bioinformatics pipeline that would allow comprehensive analysis of all variant types. We combined established bioinformatics tools for phenotypic and genomic analysis with our novel algorithms (SVRare, ALTSPLICE and GREEN-DB) to detect and annotate structural, splice site and non-coding variants., Results: Our diagnostic yield was 43/122 cases (35%), although 47/122 cases (39%) were considered solved when considering novel candidate genes with supporting functional data into account. Structural, splice site and deep intronic variants contributed to 20/47 (43%) of our solved cases. Five genes that are novel, or were novel at the time of discovery, were identified, whilst a further three genes are putative novel disease genes with evidence of causality. We identified variants of uncertain significance in a further fourteen candidate genes. The phenotypic spectrum associated with RMND1 was expanded to include polymicrogyria. Two patients with secondary findings in FBN1 and KCNQ1 were confirmed to have previously unidentified Marfan and long QT syndromes, respectively, and were referred for further clinical interventions. Clinical diagnoses were changed in six patients and treatment adjustments made for eight individuals, which for five patients was considered life-saving., Conclusions: Genome sequencing is increasingly being considered as a first-line genetic test in routine clinical settings and can make a substantial contribution to rapidly identifying a causal aetiology for many patients, shortening their diagnostic odyssey. We have demonstrated that structural, splice site and intronic variants make a significant contribution to diagnostic yield and that comprehensive analysis of the entire genome is essential to maximise the value of clinical genome sequencing., (© 2023. Crown.)

Background: ADP-A2M10 specific peptide enhanced affinity receptor (SPEAR) T cells (ADP-A2M10) are genetically engineered autologous T cells that express a high-affinity melanoma-associated antigen A10 (MAGE-A10)-specific T-cell receptor (TCR) targeting MAGE-A10 + tumors in the context of human leukocyte antigen (HLA)-A*02. ADP-0022-003 was a phase I dose-escalation trial that aimed to evaluate the safety and antitumor activity of ADP-A2M10 in non-small cell lung cancer (NSCLC) (NCT02592577)., Methods: Eligible patients were HLA-A*02 positive with advanced NSCLC expressing MAGE-A10. Patients underwent apheresis; T cells were isolated, transduced with a lentiviral vector containing the TCR targeting MAGE-A10, and expanded. Patients underwent lymphodepletion with varying doses/schedules of fludarabine and cyclophosphamide prior to receiving ADP-A2M10. ADP-A2M10 were administered at 0.08-0.12×10 9 (dose group 1), 0.5-1.2×10 9 (dose group 2), and 1.2-15×10 9 (dose group 3/expansion) transduced cells., Results: Eleven patients (male, n=6; female, n=5) with NSCLC (adenocarcinoma, n=8; squamous cell carcinoma, n=3) were treated. Five, three, and three patients received cells in dose group 1, dose group 2, and dose group 3/expansion, respectively. The most frequently reported grade ≥3 adverse events were lymphopenia (n=11), leukopenia (n=10), neutropenia (n=8), anemia (n=6), thrombocytopenia (n=5), and hyponatremia (n=5). Three patients presented with cytokine release syndrome (grades 1, 2, and 4, respectively). One patient received the highest dose of lymphodepletion (fludarabine 30 mg/m 2 on days -5 to -2 and cyclophosphamide 1800 mg/m 2 on days -5 to -4) prior to a second infusion of ADP-A2M10 and had a partial response, subsequently complicated by aplastic anemia and death. Responses included: partial response (after second infusion; one patient), stable disease (four patients), clinical or radiographic progressive disease (five patients), and not evaluable (one patient). ADP-A2M10 were detectable in peripheral blood and in tumor tissue. Peak persistence was higher in patients who received higher doses of ADP-A2M10., Conclusions: ADP-A2M10 demonstrated an acceptable safety profile and no evidence of toxicity related to off-target binding or alloreactivity. There was persistence of ADP-A2M10 in peripheral blood as well as ADP-A2M10 trafficking into the tumor. Given the discovery that MAGE-A10 and MAGE-A4 expression frequently overlap, this clinical program closed as trials with SPEAR T cells targeting MAGE-A4 are ongoing., Competing Interests: Competing interests: AG holds stock options in Adaptimmune. AJO has received research contracts from Alkermes, Antegene, Astellas, Checkmate, Gan & Lee, GlycoNex, InstilBio, Intensity, Istari, Kadmon, Kartos, NeoImmune, NGM, OncoSec, Sound Bio, SpringBank, Takeda; has received payment for advisory board roles from Merck, BMS, Pfizer, Takeda, Sanofi, Eisai. DB, DM, EN, JB, J-MN, JPS, MMP, NH, RB, SF are or were employees of Adaptimmune at the time of the study. GRB has received grants/contracts from Amgen, Bayer, Adaptimmune, Elelixis, Daiichi Sankyo, GlaxoSmithKline, Immatics, Immunocore, Incyte, Kite Pharma, Macrogenics, Torque, AstraZeneca, Bristol-Myers Squibb, Celgene, Genentech, MedImmune, Merck, Novartis, Roche, Xcovery, Tmunity Therapeutics, Regeneron, Beigene, Repertoire Immune Medicines, Verastem; consulting fees from Abbvie, Adicet, Amgen, Ariad, Bayer, Clovis Oncology, AstraZeneca, Bristol-Myers Squibb, Celgene, Daiichi Sankyo, Instil Bio, Genentech, Gilead, Lilly, Janssen, MedImmune, Merck, Novartis, Roche, Tyme Oncology, Xcovery, Virogin Biotech, Maverick Therapeutics; has participated on a Data Safety Monitoring Board or Advisory Board for Virogen Biotech, Maverick Therapeutics; holds stock or stock options in Virogin Biotech; and has other financial or non-financial interests in Johnson & Johnson/Janssen (immediate family member employed). JG has been a compensated consultant or received honoraria from Bristol-Myers Squibb, Genentech, Ariad/Takeda, Loxo, Pfizer, Incyte, Novartis, Merck, Agios, Amgen, Jounce, Karyopharm, GlydeBio, Mirati, AstraZeneca, Regeneron, Oncorus, Helsinn, Array, and Clovis Oncology; has been an employee (immediate family member) with equity in Ironwood Pharmaceuticals; has received research funding from Novartis, Genentech/Roche, and Ariad/Takeda; has received institutional research support from Tesaro, Moderna, Blueprint, Scholar Rock, BMS, Array, Adaptimmune, Novartis, Genentech/Roche, Alexo and Merck. JVH has received grants/contracts from AstraZeneca, GlaxoSmithKline and Spectrum; holds royalty/licenses in Spectrum; has received consulting fees from AstraZeneca, Boehringer-Ingelheim, Catalyst, Genentech, GlaxoSmithKline, Guardant Health, Foundation medicine, Hengrui Therapeutics, Eli Lilly, Novartis, Spectrum, EMD Serono, Sanofi, Takeda, Mirati Therapeutics, BMS, BrightPath Biotherapeutics, Janssen Global Services, Nexus Health Systems, EMD Serono, Pneuma Respiratory, Kairos Venture Investments, Roche, Leads Biolabs, RefleXion; has received honoraria from Medlinker, Peerview, Nexus Health Medicine, Targeted Oncology, MJH Events; has received support for attending meetings from IASLC Targeted Therapies, IASLC World Conference on Lung Cancer; has been in a leadership or fiduciary role in other board, society, committee or advocacy group, paid or unpaid for Mechanisms of Cancer Therapeutics-1 (MCT1) (Study Section – Chair). MJ has received research funding from AbbVie, Acerta, Adaptimmune, Apexigen, Array BioPharma, AstraZeneca, Atreca, BeiGene, Boehringer Ingelheim, Checkpoint Therapeutics, Corvus Pharmaceuticals, CytomX, Daiichi Sankyo, Dynavax, Lilly, EMD Serono, Genentech/Roche, Genmab, Genocea Biosciences, GlaxoSmithKline, Gritstone Oncology, Guardant Health, Hengrui Therapeutics, Immunocore, Incyte, Janssen, Jounce Therapeutics, Kadmon Pharmaceuticals, Loxo Oncology, Lycera, Merck, Mirati Therapeutics, Neovia Oncology, Novartis, OncoMed Pharmaceuticals, Pfizer, Regeneron Pharmaceuticals, Sanofi, Seven and Eight Biopharmaceuticals, Shattuck Labs, Stem CentRx, Syndax Pharmaceuticals, Takeda Pharmaceuticals, Tarveda, University of Michigan, WindMIL, TCR2 Therapeutics, Arcus Biosciences, Ribon Therapeutics, Amgen; has held a consulting/advisory role (spouse) for Astellas, Otsuka Pharmaceuticals; has held a consulting/advisory role (self) for AbbVie, Achilles Therapeutics, AstraZeneca, Atreca, Boehringer Ingelheim, Calithera Biosciences, Genentech, GlaxoSmithKline, Gritstone Oncology, Guardant Health, Incyte, Janssen, Lilly, Loxo Oncology, Merck, Mirati Therapeutics, Novartis, Pfizer, Ribon Therapeutics, Sanofi, Association of Community Cancer Center; has received food/beverage/travel expenses from Abbvie, Astellas, AstraZeneca, Boehringer Ingelheim, Clovis, Daiichi Sankyo, EMD Serono, Bristol Myers Squibb, Exelixis, Genentech/Roche, Incyte, Merck, Pfizer, Sysmex Inostics, Vapotherm, Janssen, Lilly, Novartis, Sanofi. MJE has received research funding from GlaxoSmithKline, Mersana, Amgen, Windmil, Nektar, Apexigen, Adaptimmune; has received consulting fees from Kanaph, Flame; has received honoraria from Sanofi; has been a member of DSMB for AstraZeneca, Seattle Genetics, GlaxoSmithKline, Takeda; has been a consultant/on an advisory board for Windmil, Regeneron, Syndax; has been the Chair, Scientific Advisory board for Lung Cancer Foundation of America; has been the Deputy Editor of 'Lung Cancer'; has received stock options from Biomarker strategies; Creatv Biotech. MJF has been a consultant for Arcellx, BMS, Novartis, Kite, Iovance. PMF is an employee of Adaptimmune; holds stock in Adaptimmune and Bristol-Myers Squib; has received compensation for travel and congress meetings. RG has received consulting fees from BMS, Abbvie, Geneplus; has participated on a Data Safety Monitoring Board or Advisory Board for Roche Genentech. SD has been a consultant for Jacobio pharmaceuticals (without financial compensation). TH was an employee of Adaptimmune at the time of the study; has been a consultant for Adaptimmune. VKL has been in a consulting or advisory role for Takeda, Bristol-Myers Squibb, Seattle Genetics; has received research funding from GlaxoSmithKline, Bristol-Myers Squibb, Guardant Health, Takeda. VM has received consulting fees from Roche, Bayer, Pieris, BMS, Janssen and Basilea. BDdS, CB, ZW have nothing to declare., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Purpose: The translation of genome sequencing into routine health care has been slow, partly because of concerns about affordability. The aspirational cost of sequencing a genome is $1000, but there is little evidence to support this estimate. We estimate the cost of using genome sequencing in routine clinical care in patients with cancer or rare diseases., Methods: We performed a microcosting study of Illumina-based genome sequencing in a UK National Health Service laboratory processing 399 samples/year. Cost data were collected for all steps in the sequencing pathway, including bioinformatics analysis and reporting of results. Sensitivity analysis identified key cost drivers., Results: Genome sequencing costs £6841 per cancer case (comprising matched tumor and germline samples) and £7050 per rare disease case (three samples). The consumables used during sequencing are the most expensive component of testing (68-72% of the total cost). Equipment costs are higher for rare disease cases, whereas consumable and staff costs are slightly higher for cancer cases., Conclusion: The cost of genome sequencing is underestimated if only sequencing costs are considered, and likely surpasses $1000/genome in a single laboratory. This aspirational sequencing cost will likely only be achieved if consumable costs are considerably reduced and sequencing is performed at scale.

Intrahepatic cholangiocarcinoma (ICC) is an aggressive cancer arising from the bile ducts with a need for earlier diagnosis and a greater range of treatment options. KRAS/NRAS mutations are common in ICC tumours and 6-32% of patients also have isocitrate dehydrogenase 1 and 2 ( IDH1 and IDH2 ) gene mutations associated with metabolic changes. This feasibility study investigated sequencing circulating tumour DNA (ctDNA) combined with metabolite profiling of plasma as a method for biomarker discovery in ICC patients. Plasma was collected from four ICC patients receiving radio-embolisation and healthy controls at multiple time points. ctDNA was sequenced using Ampliseq cancer hotspot panel-v2 on Ion Torrent PGM for single nucleotide variants (SNV) detection and with Illumina whole genome sequencing for copy number variants (CNV) and further targeted examination for SNVs. Untargeted analysis of metabolites from patient and control plasma was performed using liquid chromatography coupled with high-resolution tandem mass spectrometry (LC-MS/MS). Metabolite identification was performed using multi-parameter comparisons with analysis of authentic standards, and univariate statistical analysis was performed to identify differences in metabolite abundance between patient and control samples. Recurrent somatic SNVs and CNVs were identified in ctDNA from three out of four patients that included both NRAS and IDH1 mutations linked to ICC. Plasma metabolite analysis revealed biomarker metabolites associated with ICC and in particular 2-hydroxyglutarate (2-HG) levels were elevated in both samples from the only patient showing a variant allele in IDH1 . A reduction in the number of CNVs was observed with treatment. This study demonstrates that ctDNA and metabolite levels can be identified and correlated in ICC patient blood samples and differentiated from healthy controls. We conclude that combining genomic and metabolic analysis of plasma offers an effective approach to biomarker identification with potential for disease stratification and early detection studies.

Next-generation sequencing (NGS) efforts have established catalogs of mutations relevant to cancer development. However, the clinical utility of this information remains largely unexplored. Here, we present the results of the first eight patients recruited into a clinical whole-genome sequencing (WGS) program in the United Kingdom. We performed PCR-free WGS of fresh frozen tumors and germline DNA at 75× and 30×, respectively, using the HiSeq2500 HTv4. Subtracted tumor VCFs and paired germlines were subjected to comprehensive analysis of coding and noncoding regions, integration of germline with somatically acquired variants, and global mutation signatures and pathway analyses. Results were classified into tiers and presented to a multidisciplinary tumor board. WGS results helped to clarify an uncertain histopathological diagnosis in one case, led to informed or supported prognosis in two cases, leading to de-escalation of therapy in one, and indicated potential treatments in all eight. Overall 26 different tier 1 potentially clinically actionable findings were identified using WGS compared with six SNVs/indels using routine targeted NGS. These initial results demonstrate the potential of WGS to inform future diagnosis, prognosis, and treatment choice in cancer and justify the systematic evaluation of the clinical utility of WGS in larger cohorts of patients with cancer., (© 2018 Schuh et al.; Published by Cold Spring Harbor Laboratory Press.)

Use of circulating tumour DNA (ctDNA) as a liquid biopsy has been proposed for potential identification and monitoring of solid tumours. We investigate a next-generation sequencing approach for mutation detection in ctDNA in two related studies using a targeted panel. The first study was retrospective, using blood samples taken from melanoma patients at diverse timepoints before or after treatment, aiming to evaluate correlation between mutations identified in biopsy and ctDNA, and to acquire a first impression of influencing factors. We found good concordance between ctDNA and tumour mutations of melanoma patients when blood samples were collected within one year of biopsy or before treatment. In contrast, when ctDNA was sequenced after targeted treatment in melanoma, mutations were no longer found in 9 out of 10 patients, suggesting the method might be useful for detecting treatment response. Building on these findings, we focused the second study on ctDNA obtained before biopsy in lung patients, i.e. when a tentative diagnosis of lung cancer had been made, but no treatment had started. The main objective of this prospective study was to evaluate use of ctDNA in diagnosis, investigating the concordance of biopsy and ctDNA-derived mutation detection. Here we also found positive correlation between diagnostic lung biopsy results and pre-biopsy ctDNA sequencing, providing support for using ctDNA as a cost-effective, non-invasive solution when the tumour is inaccessible or when biopsy poses significant risk to the patient., Competing Interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: PJK has received royalties in the past two years from a license agreement with Correlagen, related to early-onset non-insulin-dependent diabetes mellitus. AG has received fees from Bristol Myers Squibb for leading webinar based teaching sessions organised by them for other doctors. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Rafflesia is a genus of holoparasitic plants endemic to Southeast Asia that has lost the ability to undertake photosynthesis. With short-read sequencing technology, we assembled a draft sequence of the mitochondrial genome of Rafflesia lagascae Blanco, a species endemic to the Philippine island of Luzon, with ∼350× sequencing depth coverage. Using multiple approaches, however, we were only able to identify small fragments of plastid sequences at low coverage depth (<2×) and could not recover any substantial portion of a chloroplast genome. The gene fragments we identified included photosynthesis and energy production genes (atp, ndh, pet, psa, psb, rbcL), ribosomal RNA genes (rrn16, rrn23), ribosomal protein genes (rps7, rps11, rps16), transfer RNA genes, as well as matK, accD, ycf2, and multiple nongenic regions from the inverted repeats. None of the identified plastid gene sequences had intact reading frames. Phylogenetic analysis suggests that ∼33% of these remnant plastid genes may have been horizontally transferred from the host plant genus Tetrastigma with the rest having ambiguous phylogenetic positions (<50% bootstrap support), except for psaB that was strongly allied with the plastid homolog in Nicotiana. Our inability to identify substantial plastid genome sequences from R. lagascae using multiple approaches--despite success in identifying and developing a draft assembly of the much larger mitochondrial genome--suggests that the parasitic plant genus Rafflesia may be the first plant group for which there is no recognizable plastid genome, or if present is found in cryptic form at very low levels.

Background: Gene duplication events have been proposed to be involved in the adaptation of plants to stress conditions; precisely how is unclear. To address this question, we studied the evolution of two families of antiporters. Cation/proton exchangers are important for normal cell function and in plants, Na+,K+/H+ antiporters have also been implicated in salt tolerance. Two well-known plant cation/proton antiporters are NHX1 and SOS1, which perform Na+ and K+ compartmentalization into the vacuole and Na+ efflux from the cell, respectively. However, our knowledge about the evolution of NHX and SOS1 stress responsive gene families is still limited., Results: In this study we performed a comprehensive molecular evolutionary analysis of the NHX and SOS1 families. Using available sequences from a total of 33 plant species, we estimated gene family phylogenies and gene duplication histories, as well as examined heterogeneous selection pressure on amino acid sites. Our results show that, while the NHX family expanded and specialized, the SOS1 family remained a low copy gene family that appears to have undergone neofunctionalization during its evolutionary history. Additionally, we found that both families are under purifying selection although SOS1 is less constrained., Conclusions: We propose that the different evolution histories are related with the proteins' function and localization, and that the NHX and SOS1 families are examples of two different evolutionary paths through which duplication events may result in adaptive evolution of stress tolerance.

Despite its importance, relatively little is known about the relationship between the structure, function, and evolution of proteins, particularly in land plant species. We have developed a database with predicted protein domains for five plant proteomes (http://pfp.bio.nyu.edu) and used both protein structural fold recognition and de novo Rosetta-based protein structure prediction to predict protein structure for Arabidopsis and rice proteins. Based on sequence similarity, we have identified ~15,000 orthologous/paralogous protein family clusters among these species and used codon-based models to predict positive selection in protein evolution within 175 of these sequence clusters. Our results show that codons that display positive selection appear to be less frequent in helical and strand regions and are overrepresented in amino acid residues that are associated with a change in protein secondary structure. Like in other organisms, disordered protein regions also appear to have more selected sites. Structural information provides new functional insights into specific plant proteins and allows us to map positively selected amino acid sites onto protein structures and view these sites in a structural and functional context.

Proteomics attempts to characterise the gene products expressed in a cell or tissue via a range of biophysical techniques including crystallography and NMR and, more relevantly to this volume, chromatography and mass spectrometry. It is becoming increasingly clear that the native states of segments of many of the cellular proteins are not stable, folded structures, and much of the proteome is in an unfolded, disordered state. These proteins and their disordered segments have functionally interesting properties and provide novel challenges for the biophysical techniques that are used to study them. This chapter focuses on computational approaches to predicting such regions and analyzing the functions linked to them, and has implications for protein scientists who wish to study such properties as molecular recognition and post-translational modifications. We also discuss resources where the results of predictions have been collated, making them publicly available to the wider biological community.

Predicting regions of disorder has become of increasing interest when determining protein structure and function. With approximately 33% of eukaryotic proteins having significant disordered regions, and an increasing occurrence of disorder in higher organisms, an analysis of the importance of disorder from an evolutionary perspective was clearly warranted. Focusing on the human proteome, we have studied how abundant disorder is and its relevance to protein function and structure. We have shown that disordered regions frequently appear to be independent functional units, and judged by complete association to certain protein domains, may be evolutionarily conserved. Our work also supports previous analyses on association between disorder and alternate splicing and provides support for the modularity of disorder by showing that with respect to splicing events, disordered regions frequently appear to be spliced as whole units., ((c) 2009 Wiley-Liss, Inc.)

Background: Disordered proteins need to be expressed to carry out specified functions; however, their accumulation in the cell can potentially cause major problems through protein misfolding and aggregation. Gene expression levels, mRNA decay rates, microRNA (miRNA) targeting and ubiquitination have critical roles in the degradation and disposal of human proteins and transcripts. Here, we describe a study examining these features to gain insights into the regulation of disordered proteins., Results: In comparison with ordered proteins, disordered proteins have a greater proportion of predicted ubiquitination sites. The transcripts encoding disordered proteins also have higher proportions of predicted miRNA target sites and higher mRNA decay rates, both of which are indicative of the observed lower gene expression levels. The results suggest that the disordered proteins and their transcripts are present in the cell at low levels and/or for a short time before being targeted for disposal. Surprisingly, we find that for a significant proportion of highly disordered proteins, all four of these trends are reversed. Predicted estimates for miRNA targets, ubiquitination and mRNA decay rate are low in the highly disordered proteins that are constitutively and/or highly expressed., Conclusions: Mechanisms are in place to protect the cell from these potentially dangerous proteins. The evidence suggests that the enrichment of signals for miRNA targeting and ubiquitination may help prevent the accumulation of disordered proteins in the cell. Our data also provide evidence for a mechanism by which a significant proportion of highly disordered proteins (with high expression levels) can escape rapid degradation to allow them to successfully carry out their function.

Despite the growing volumes of proteomic data, integration of the underlying results remains problematic owing to differences in formats, data captured, protein accessions and services available from the individual repositories. To address this, we present the ISPIDER Central Proteomic Database search (http://www.ispider.manchester.ac.uk/cgi-bin/ProteomicSearch.pl), an integration service offering novel search capabilities over leading, mature, proteomic repositories including PRoteomics IDEntifications database (PRIDE), PepSeeker, PeptideAtlas and the Global Proteome Machine. It enables users to search for proteins and peptides that have been characterised in mass spectrometry-based proteomics experiments from different groups, stored in different databases, and view the collated results with specialist viewers/clients. In order to overcome limitations imposed by the great variability in protein accessions used by individual laboratories, the European Bioinformatics Institute's Protein Identifier Cross-Reference (PICR) service is used to resolve accessions from different sequence repositories. Custom-built clients allow users to view peptide/protein identifications in different contexts from multiple experiments and repositories, as well as integration with the Dasty2 client supporting any annotations available from Distributed Annotation System servers. Further information on the protein hits may also be added via external web services able to take a protein as input. This web server offers the first truly integrated access to proteomics repositories and provides a unique service to biologists interested in mass spectrometry-based proteomics.

Background: In recent years, model based approaches such as maximum likelihood have become the methods of choice for constructing phylogenies. A number of authors have shown the importance of using adequate substitution models in order to produce accurate phylogenies. In the past, many empirical models of amino acid substitution have been derived using a variety of different methods and protein datasets. These matrices are normally used as surrogates, rather than deriving the maximum likelihood model from the dataset being examined. With few exceptions, selection between alternative matrices has been carried out in an ad hoc manner., Results: We start by highlighting the potential dangers of arbitrarily choosing protein models by demonstrating an empirical example where a single alignment can produce two topologically different and strongly supported phylogenies using two different arbitrarily-chosen amino acid substitution models. We demonstrate that in simple simulations, statistical methods of model selection are indeed robust and likely to be useful for protein model selection. We have investigated patterns of amino acid substitution among homologous sequences from the three Domains of life and our results show that no single amino acid matrix is optimal for any of the datasets. Perhaps most interestingly, we demonstrate that for two large datasets derived from the proteobacteria and archaea, one of the most favored models in both datasets is a model that was originally derived from retroviral Pol proteins., Conclusion: This demonstrates that choosing protein models based on their source or method of construction may not be appropriate.

The extent to which prokaryotic evolution has been influenced by horizontal gene transfer (HGT) and therefore might be more of a network than a tree is unclear. Here we use supertree methods to ask whether a definitive prokaryotic phylogenetic tree exists and whether it can be confidently inferred using orthologous genes. We analysed an 11-taxon dataset spanning the deepest divisions of prokaryotic relationships, a 10-taxon dataset spanning the relatively recent gamma-proteobacteria and a 61-taxon dataset spanning both, using species for which complete genomes are available. Congruence among gene trees spanning deep relationships is not better than random. By contrast, a strong, almost perfect phylogenetic signal exists in gamma-proteobacterial genes. Deep-level prokaryotic relationships are difficult to infer because of signal erosion, systematic bias, hidden paralogy and/or HGT. Our results do not preclude levels of HGT that would be inconsistent with the notion of a prokaryotic phylogeny. This approach will help decide the extent to which we can say that there is a prokaryotic phylogeny and where in the phylogeny a cohesive genomic signal exists.