ALBERT

Description: Background In contrast to other agents able to induce apoptosis of cultured cells, Ca2+ ionophore A23187 was shown to elicit direct activation of intracellular signal(s). The phenotype of the cells derived from patients having the hemorrhagic disease Scott syndrome, is associated with an abnormally high proportion of apoptotic cells, both in basal culture medium and upon addition of low ionophore concentrations in long-term cultures. These features are presumably related to the mutation also responsible for the defective procoagulant plasma membrane remodeling. We analyzed the specific transcriptional re-programming induced by A23187 to get insights into the effect of this agent on gene expression and a defective gene regulation in Scott cells. Results The changes in gene expression upon 48 hours treatment with 200 nM A23187 were measured in Scott B lymphoblasts compared to B lymphoblasts derived from the patient's daughter or unrelated individuals using Affymetrix microarrays. In a similar manner in all of the B cell lines, results showed up-regulation of 55 genes, out of 12,000 represented sequences, involved in various pathways of the cell metabolism. In contrast, a group of 54 down-regulated genes, coding for histones and proteins involved in the cell cycle progression, was more significantly repressed in Scott B lymphoblasts than in the other cell lines. These data correlated with the alterations of the cell cycle phases in treated cells and suggested that the potent effect of A23187 in Scott B lymphoblasts may be the consequence of the underlying molecular defect. Conclusion The data illustrate that the ionophore A23187 exerts its pro-apoptotic effect by promoting a complex pattern of genetic changes. These results also suggest that a subset of genes participating in various steps of the cell cycle progress can be transcriptionally regulated in a coordinated fashion. Furthermore, this research brings a new insight into the defect in cultured Scott B lymphoblasts, leading to hypothesize that a mutated gene plays a role not only in membrane remodeling but also in signal transduction pathway(s) leading to altered transcriptional regulation of cell cycle genes.

Description: One of the surprising insights gained from research in evolutionary developmental biology (evo-devo) is that increasing diversity in body plans and morphology in organisms across animal phyla are not reflected in similarly dramatic changes at the level of gene composition of their genomes. For instance, simplicity at the tissue level of organization often contrasts with a high degree of genetic complexity. Also intriguing is the observation that the coding regions of several genes of invertebrates show high sequence similarity to those in humans. This lack of change (conservation) indicates that evolutionary novelties may arise more frequently through combinatorial processes, such as changes in gene regulation and the recruitment of novel genes into existing regulatory gene networks (co-option), and less often through adaptive evolutionary processes in the coding portions of a gene. As a consequence, it is of great interest to examine whether the widespread conservation of the genetic machinery implies the same developmental function in a last common ancestor, or whether homologous genes acquired new developmental roles in structures of independent phylogenetic origin. To distinguish between these two possibilities one must refer to current concepts of phylogeny reconstruction and carefully investigate homology relationships. Particularly problematic in terms of homology decisions is the use of gene expression patterns of a given structure. In the future, research on more organisms other than the typical model systems will be required since these can provide insights that are not easily obtained from comparisons among only a few distantly related model species.

Description: Background: Baum-Welch training is an expectation-maximisation algorithm for training the emission and transition probabilities of hidden Markov models in a fully automated way. It can be employed as long as a training set of annotated sequences is known, and provides a rigorous way to derive parameter values which are guaranteed to be at least locally optimal. For complex hidden Markov models such as pair hidden Markov models and very long training sequences, even the most efficient algorithms for Baum-Welch training are currently too memory-consuming. This has so far effectively prevented the automatic parameter training of hidden Markov models that are currently used for biological sequence analyses. Results: We introduce the first linear space algorithm for Baum-Welch training. For a hidden Markov model with M states, T free transition and E free emission parameters, and an input sequence of length L, our new algorithm requires O(M) memory and O(LMT max (T + E)) time for one Baum-Welch iteration, where T max is the maximum number of states that any state is connected to. The most memory efficient algorithm until now was the checkpointing algorithm with O(log(L)M) memory and O(log(L)LMT max ) time requirement. Our novel algorithm thus renders the memory requirement completely independent of the length of the training sequences. More generally, for an n-hidden Markov model and n input sequences of length L, the memory requirement of O(log(L)L n-1 M) is reduced to O(L n-1 M) memory while the running time is changed from O(log(L)L n MT max + L n (T + E)) to O(L n MT max (T + E)). An added advantage of our new algorithm is that a reduced time requirement can be traded for an increased memory requirement and vice versa, such that for any c ∈ {1, ..., (T + E)}, a time requirement of L n MT max c incurs a memory requirement of L n-1 M(T + E - c). Conclusion For the large class of hidden Markov models used for example in gene prediction, whose number of states does not scale with the length of the input sequence, our novel algorithm can thus be both faster and more memory-efficient than any of the existing algorithms.