ARPIP: Ancestral Sequence Reconstruction with Insertions and Deletions under the Poisson Indel Process.

Modern phylogenetic methods allow inference of ancestral molecular sequences given an alignment and phylogeny relating present-day sequences. This provides insight into the evolutionary history of molecules, helping to understand gene function and to study biological processes such as adaptation and convergent evolution across a variety of applications. Here, we propose a dynamic programming algorithm for fast joint likelihood-based reconstruction of ancestral sequences under the Poisson Indel Process (PIP). Unlike previous approaches, our method, named ARPIP, enables the reconstruction with insertions and deletions based on an explicit indel model. Consequently, inferred indel events have an explicit biological interpretation. Likelihood computation is achieved in linear time with respect to the number of sequences. Our method consists of two steps, namely finding the most probable indel points and reconstructing ancestral sequences. First, we find the most likely indel points and prune the phylogeny to reflect the insertion and deletion events per site. Second, we infer the ancestral states on the pruned subtree in a manner similar to FastML. We applied ARPIP (Ancestral Reconstruction under PIP) on simulated data sets and on real data from the Betacoronavirus genus. ARPIP reconstructs both the indel events and substitutions with a high degree of accuracy. Our method fares well when compared to established state-of-the-art methods such as FastML and PAML. Moreover, the method can be extended to explore both optimal and suboptimal reconstructions, include rate heterogeneity through time and more. We believe it will expand the range of novel applications of ancestral sequence reconstruction. [Ancestral sequences; dynamic programming; evolutionary stochastic process; indel; joint ancestral sequence reconstruction; maximum likelihood; Poisson Indel Process; phylogeny; SARS-CoV.].

High-density EEG power topography and connectivity during confusional arousal.

Confusional arousal is the milder expression of a family of disorders known as Disorders of Arousal (DOA) from non-REM sleep. These disorders are characterized by recurrent abnormal behaviors that occur in a state of reduced awareness for the external environment. Despite frequent amnesia for the nocturnal events, when actively probed, patients are able to report vivid hallucinatory/dream-like mental imagery. Traditional (low-density) scalp and stereo-electroencephalographic (EEG) recordings previously showed a pathological admixture of slow oscillations typical of NREM sleep and wake-like fast-mixed frequencies during these phenomena. However, our knowledge about the specific neural EEG dynamics over the entire brain is limited. We collected 2 consecutive in-laboratory sleep recordings using high-density (hd)-EEG (256 vertex-referenced geodesic system) coupled with standard video-polysomnography (v-PSG) from a 12-year-old drug-naïve and otherwise healthy child with a long-lasting history of sleepwalking. Source power topography and functional connectivity were computed during 20 selected confusional arousal episodes (from -6 to +18 sec after motor onset), and during baseline slow wave sleep preceding each episode (from - 3 to -2 min before onset). We found a widespread increase in slow wave activity (SWA) theta, alpha, beta, gamma power, associated with a parallel decrease in the sigma range during behavioral episodes compared to baseline sleep. Bilateral Broadman area 7 and right Broadman areas 39 and 40 were relatively spared by the massive increase in SWA power. Functional SWA connectivity analysis revealed a drastic increase in the number and complexity of connections from baseline sleep to full-blown episodes, that mainly involved an increased out-flow from bilateral fronto-medial prefrontal cortex and left temporal lobe to other cortical regions. These effects could be appreciated in the 6 sec window preceding behavioral onset. Overall, our results support the idea that DOA are the expression of peculiar brain states, compatible with a partial re-emergence of consciousness.

Flu vaccination in multiple sclerosis patients: a monocentric prospective vaccine-vigilance study.

BACKGROUND: In 2020, the Italian Medicines Agency recommended to bring forward the flu vaccination campaign, whose importance was also emphasized for patients with Multiple Sclerosis (MS). We aimed to assess the safety profile of flu vaccines in terms of occurrence of short-term and long-term Adverse Events Following Immunization (AEFIs). METHODS: This is an observational study that enrolled MS patients who were eligible for any of the flu vaccines recommended by the Italian medicines Agency. RESULTS: 194 patients were enrolled. Out of 133 patients who accepted to be vaccinated, 45 experienced not serious short-term AEFIs (pain at the injection site, headache, flu-like symptoms, fatigue). Long-term AEs were detected in 12 vaccinated patients (flu-like symptoms, COVID-19 and MS relapse). No statistically significant differences in terms of infections or MS relapse were found between vaccinated and unvaccinated groups. Using Kaplan-Meier analysis we observed no differences in the cumulative survival rate in both groups. CONCLUSION: Flu vaccines were well tolerated in MS patients, who mainly experienced not serious short term AEFIs. Considering that COVID-19 vaccines campaign is still ongoing among MS patients, our results might bring new knowledge concerning the safety profile of vaccines in this frail population.

ProPIP: a tool for progressive multiple sequence alignment with Poisson Indel Process.

BACKGROUND: Current alignment tools typically lack an explicit model of indel evolution, leading to artificially short inferred alignments (i.e., over-alignment) due to inconsistencies between the indel history and the phylogeny relating the input sequences. RESULTS: We present a new progressive multiple sequence alignment tool ProPIP. The process of insertions and deletions is described using an explicit evolutionary model-the Poisson Indel Process or PIP. The method is based on dynamic programming and is implemented in a frequentist framework. The source code can be compiled on Linux, macOS and Microsoft Windows platforms. The algorithm is implemented in C++ as standalone program. The source code is freely available on GitHub at https://github.com/acg-team/ProPIP and is distributed under the terms of the GNU GPL v3 license. CONCLUSIONS: The use of an explicit indel evolution model allows to avoid over-alignment, to infer gaps in a phylogenetically consistent way and to make inferences about the rates of insertions and deletions. Instead of the arbitrary gap penalties, the parameters used by ProPIP are the insertion and deletion rates, which have biological interpretation and are contextualized in a probabilistic environment. As a result, indel rate settings may be optimised in order to infer phylogenetically meaningful gap patterns.

Accelerating phylogeny-aware alignment with indel evolution using short time Fourier transform.

Recently we presented a frequentist dynamic programming (DP) approach for multiple sequence alignment based on the explicit model of indel evolution Poisson Indel Process (PIP). This phylogeny-aware approach produces evolutionary meaningful gap patterns and is robust to the 'over-alignment' bias. Despite linear time complexity for the computation of marginal likelihoods, the overall method's complexity is cubic in sequence length. Inspired by the popular aligner MAFFT, we propose a new technique to accelerate the evolutionary indel based alignment. Amino acid sequences are converted to sequences representing their physicochemical properties, and homologous blocks are identified by multi-scale short-time Fourier transform. Three three-dimensional DP matrices are then created under PIP, with homologous blocks defining sparse structures where most cells are excluded from the calculations. The homologous blocks are connected through intermediate 'linking blocks'. The homologous and linking blocks are aligned under PIP as independent DP sub-matrices and their tracebacks merged to yield the final alignment. The new algorithm can largely profit from parallel computing, yielding a theoretical speed-up estimated to be proportional to the cubic power of the number of sub-blocks in the DP matrices. We compare the new method to the original PIP approach and demonstrate it on real data.

Progressive multiple sequence alignment with indel evolution.

BACKGROUND: Sequence alignment is crucial in genomics studies. However, optimal multiple sequence alignment (MSA) is NP-hard. Thus, modern MSA methods employ progressive heuristics, breaking the problem into a series of pairwise alignments guided by a phylogeny. Changes between homologous characters are typically modelled by a Markov substitution model. In contrast, the dynamics of indels are not modelled explicitly, because the computation of the marginal likelihood under such models has exponential time complexity in the number of taxa. But the failure to model indel evolution may lead to artificially short alignments due to biased indel placement, inconsistent with phylogenetic relationship. RESULTS: Recently, the classical indel model TKF91 was modified to describe indel evolution on a phylogeny via a Poisson process, termed PIP. PIP allows to compute the joint marginal probability of an MSA and a tree in linear time. We present a new dynamic programming algorithm to align two MSAs -represented by the underlying homology paths- by full maximum likelihood under PIP in polynomial time, and apply it progressively along a guide tree. We have corroborated the correctness of our method by simulation, and compared it with competitive methods on an illustrative real dataset. CONCLUSIONS: Our MSA method is the first polynomial time progressive aligner with a rigorous mathematical formulation of indel evolution. The new method infers phylogenetically meaningful gap patterns alternative to the popular PRANK, while producing alignments of similar length. Moreover, the inferred gap patterns agree with what was predicted qualitatively by previous studies. The algorithm is implemented in a standalone C++ program: https://github.com/acg-team/ProPIP . Supplementary data are available at BMC Bioinformatics online.

Synthesis, structure and molecular modelling of anionic carbosilane dendrimers.

Anionic carbosilane dendrimers of generations 1-3 have been synthesized containing carboxylate G(n)X(C(2)H(4)CO(2)Na)(m) and sulfonate G(n)X(C(2)H(4)SO(3)Na)(m) peripheral groups and derived from two different cores, 1,3,5-(HO)(3)C(6)H(3) (X = O(3)) and Si(C(3)H(5))(4) (X = Si). The peripheral anionic groups make these dendrimers water soluble, despite their highly hydrophobic framework. These dendrimers present a net negative charge in water, which was influenced by the pH of the medium. This characteristic was studied by pH titration. Also molecular modeling calculations have been performed to study differences in an aqueous medium between carboxylate and sulfonate dendrimers of different cores. The results obtained were also compared with those obtained from DOSY NMR experiments and zeta-potential measurements.