Phylogenomic evidence for a common ancestor of mitochondria and the SAR11 clade

Mitochondria share a common ancestor with the Alphaproteobacteria, but determining their precise origins is challenging due to inherent difficulties in phylogenetically reconstructing ancient evolutionary events. Nonetheless, phylogenetic accuracy improves with more refined tools and expanded taxon sampling. We investigated mitochondrial origins with the benefit of new, deeply branching genome sequences from the ancient and prolific SAR11 clade of Alphaproteobacteria and publicly available alphaproteobacterial and mitochondrial genome sequences. Using the automated phylogenomic pipeline Hal, we systematically studied the effect of taxon sampling and missing data to accommodate small mitochondrial genomes. The evidence supports a common origin of mitochondria and SAR11 as a sister group to the Rickettsiales. The simplest explanation of these data is that mitochondria evolved from a planktonic marine alphaproteobacterial lineage that participated in multiple inter-specific cell colonization events, in some cases yielding parasitic relationships, but in at least one case producing a symbiosis that characterizes modern eukaryotic life

Association of HLA class I with severe acute respiratory syndrome coronavirus infection

BACKGROUND: The human leukocyte antigen (HLA) system is widely used as a strategy in the search for the etiology of infectious diseases and autoimmune disorders. During the Taiwan epidemic of severe acute respiratory syndrome (SARS), many health care workers were infected. In an effort to establish a screening program for high risk personal, the distribution of HLA class I and II alleles in case and control groups was examined for the presence of an association to a genetic susceptibly or resistance to SARS coronavirus infection. METHODS: HLA-class I and II allele typing by PCR-SSOP was performed on 37 cases of probable SARS, 28 fever patients excluded later as probable SARS, and 101 non-infected health care workers who were exposed or possibly exposed to SARS coronavirus. An additional control set of 190 normal healthy unrelated Taiwanese was also used in the analysis. RESULTS: Woolf and Haldane Odds ratio (OR) and corrected P-value (Pc) obtained from two tails Fisher exact test were used to show susceptibility of HLA class I or class II alleles with coronavirus infection. At first, when analyzing infected SARS patients and high risk health care workers groups, HLA-B*4601 (OR = 2.08, P = 0.04, Pc = n.s.) and HLA-B*5401 (OR = 5.44, P = 0.02, Pc = n.s.) appeared as the most probable elements that may be favoring SARS coronavirus infection. After selecting only a "severe cases" patient group from the infected "probable SARS" patient group and comparing them with the high risk health care workers group, the severity of SARS was shown to be significantly associated with HLA-B*4601 (P = 0.0008 or Pc = 0.0279). CONCLUSIONS: Densely populated regions with genetically related southern Asian populations appear to be more affected by the spreading of SARS infection. Up until recently, no probable SARS patients were reported among Taiwan indigenous peoples who are genetically distinct from the Taiwanese general population, have no HLA-B* 4601 and have high frequency of HLA-B* 1301. While increase of HLA-B* 4601 allele frequency was observed in the "Probable SARS infected" patient group, a further significant increase of the allele was seen in the "Severe cases" patient group. These results appeared to indicate association of HLA-B* 4601 with the severity of SARS infection in Asian populations. Independent studies are needed to test these results

A survey of visualization tools for biological network analysis

The analysis and interpretation of relationships between biological molecules, networks and concepts is becoming a major bottleneck in systems biology. Very often the pure amount of data and their heterogeneity provides a challenge for the visualization of the data. There are a wide variety of graph representations available, which most often map the data on 2D graphs to visualize biological interactions. These methods are applicable to a wide range of problems, nevertheless many of them reach a limit in terms of user friendliness when thousands of nodes and connections have to be analyzed and visualized. In this study we are reviewing visualization tools that are currently available for visualization of biological networks mainly invented in the latest past years. We comment on the functionality, the limitations and the specific strengths of these tools, and how these tools could be further developed in the direction of data integration and information sharing