Full Chromosomal Relationships Between Populations and the Origin of Humans.

A comprehensive description of human genomes is essential for understanding human evolution and relationships between modern populations. However, most published literature focuses on local alignment comparison of several genes rather than the complete evolutionary record of individual genomes. Combining with data from the 1,000 Genomes Project, we successfully reconstructed 2,504 individual genomes and propose Divided Natural Vector method to analyze the distribution of nucleotides in the genomes. Comparisons based on autosomes, sex chromosomes and mitochondrial genomes reveal the genetic relationships between populations, and different inheritance pattern leads to different phylogenetic results. Results based on mitochondrial genomes confirm the "out-of-Africa" hypothesis and assert that humans, at least females, most likely originated in eastern Africa. The reconstructed genomes are stored on our server and can be further used for any genome-scale analysis of humans (http://yaulab.math.tsinghua.edu.cn/2022_1000genomesprojectdata/). This project provides the complete genomes of thousands of individuals and lays the groundwork for genome-level analyses of the genetic relationships between populations and the origin of humans.

Convex hull principle for classification and phylogeny of eukaryotic proteins.

This study quantitatively validates the principle that the biological properties associated with a given genotype are determined by the distribution of amino acids. In order to visualize this central law of molecular biology, each protein was represented by a point in 250-dimensional space based on its amino acid distribution. Proteins from the same family are found to cluster together, leading to the principle that the convex hull surrounding protein points from the same family do not intersect with the convex hulls of other protein families. This principle was verified computationally for all available and reliable protein kinases and human proteins. In addition, we generated 2,328,761 figures to show that the convex hulls of different families were disjoint from each other. The classification performs well with high and robust accuracy (95.75% and 97.5%) together with reasonable phylogenetic trees validate our methods further.

A new method to cluster genomes based on cumulative Fourier power spectrum.

Analyzing phylogenetic relationships using mathematical methods has always been of importance in bioinformatics. Quantitative research may interpret the raw biological data in a precise way. Multiple Sequence Alignment (MSA) is used frequently to analyze biological evolutions, but is very time-consuming. When the scale of data is large, alignment methods cannot finish calculation in reasonable time. Therefore, we present a new method using moments of cumulative Fourier power spectrum in clustering the DNA sequences. Each sequence is translated into a vector in Euclidean space. Distances between the vectors can reflect the relationships between sequences. The mapping between the spectra and moment vector is one-to-one, which means that no information is lost in the power spectra during the calculation. We cluster and classify several datasets including Influenza A, primates, and human rhinovirus (HRV) datasets to build up the phylogenetic trees. Results show that the new proposed cumulative Fourier power spectrum is much faster and more accurately than MSA and another alignment-free method known as k-mer. The research provides us new insights in the study of phylogeny, evolution, and efficient DNA comparison algorithms for large genomes. The computer programs of the cumulative Fourier power spectrum are available at GitHub (https://github.com/YaulabTsinghua/cumulative-Fourier-power-spectrum).

Establishing the phylogeny of Prochlorococcus with a new alignment-free method.

Prochlorococcus marinus, one of the most abundant marine cyanobacteria in the global ocean, is classified into low-light (LL) and high-light (HL) adapted ecotypes. These two adapted ecotypes differ in their ecophysiological characteristics, especially whether adapted for growth at high-light or low-light intensities. However, some evolutionary relationships of Prochlorococcus phylogeny remain to be resolved, such as whether the strains SS120 and MIT9211 form a monophyletic group. We use the Natural Vector (NV) method to represent the sequence in order to identify the phylogeny of the Prochlorococcus. The natural vector method is alignment free without any model assumptions. This study added the covariances of amino acids in protein sequence to the natural vector method. Based on these new natural vectors, we can compute the Hausdorff distance between the two clades which represents the dissimilarity. This method enables us to systematically analyze both the dataset of ribosomal proteomes and the dataset of 16s-23s rRNA sequences in order to reconstruct the phylogeny of Prochlorococcus. Furthermore, we apply classification to inspect the relationship of SS120 and MIT9211. From the reconstructed phylogenetic trees and classification results, we may conclude that the SS120 does not cluster with MIT9211. This study demonstrates a new method for performing phylogenetic analysis. The results confirm that these two strains do not form a monophyletic clade in the phylogeny of Prochlorococcus.

A new method for studying the evolutionary origin of the SAR11 clade marine bacteria.

The free-living SAR11 clade is a globally abundant group of oceanic Alphaproteobacteria, with small genome sizes and rich genomic A+T content. However, the taxonomy of SAR11 has become controversial recently. Some researchers argue that the position of SAR11 is a sister group to Rickettsiales. Other researchers advocate that SAR11 is located within free-living lineages of Alphaproteobacteria. Here, we use the natural vector representation method to identify the evolutionary origin of the SAR11 clade. This alignment-free method does not depend on any model assumptions. With this approach, the correspondence between proteome sequences and their natural vectors is one-to-one. After fixing a set of proteins, each bacterium is represented by a set of vectors. The Hausdorff distance is then used to compute the dissimilarity distance between two bacteria. The phylogenetic tree can be reconstructed based on these distances. Using our method, we systematically analyze four data sets of alphaproteobacterial proteomes in order to reconstruct the phylogeny of Alphaproteobacteria. From this we can see that the phylogenetic position of the SAR11 group is within a group of other free-living lineages of Alphaproteobacteria.

Two Dimensional Yau-Hausdorff Distance with Applications on Comparison of DNA and Protein Sequences.

Comparing DNA or protein sequences plays an important role in the functional analysis of genomes. Despite many methods available for sequences comparison, few methods retain the information content of sequences. We propose a new approach, the Yau-Hausdorff method, which considers all translations and rotations when seeking the best match of graphical curves of DNA or protein sequences. The complexity of this method is lower than that of any other two dimensional minimum Hausdorff algorithm. The Yau-Hausdorff method can be used for measuring the similarity of DNA sequences based on two important tools: the Yau-Hausdorff distance and graphical representation of DNA sequences. The graphical representations of DNA sequences conserve all sequence information and the Yau-Hausdorff distance is mathematically proved as a true metric. Therefore, the proposed distance can preciously measure the similarity of DNA sequences. The phylogenetic analyses of DNA sequences by the Yau-Hausdorff distance show the accuracy and stability of our approach in similarity comparison of DNA or protein sequences. This study demonstrates that Yau-Hausdorff distance is a natural metric for DNA and protein sequences with high level of stability. The approach can be also applied to similarity analysis of protein sequences by graphic representations, as well as general two dimensional shape matching.

K-mer natural vector and its application to the phylogenetic analysis of genetic sequences.

Based on the well-known k-mer model, we propose a k-mer natural vector model for representing a genetic sequence based on the numbers and distributions of k-mers in the sequence. We show that there exists a one-to-one correspondence between a genetic sequence and its associated k-mer natural vector. The k-mer natural vector method can be easily and quickly used to perform phylogenetic analysis of genetic sequences without requiring evolutionary models or human intervention. Whole or partial genomes can be handled more effective with our proposed method. It is applied to the phylogenetic analysis of genetic sequences, and the obtaining results fully demonstrate that the k-mer natural vector method is a very powerful tool for analysing and annotating genetic sequences and determining evolutionary relationships both in terms of accuracy and efficiency.

Protein sequence comparison based on K-string dictionary.

The current K-string-based protein sequence comparisons require large amounts of computer memory because the dimension of the protein vector representation grows exponentially with K. In this paper, we propose a novel concept, the "K-string dictionary", to solve this high-dimensional problem. It allows us to use a much lower dimensional K-string-based frequency or probability vector to represent a protein, and thus significantly reduce the computer memory requirements for their implementation. Furthermore, based on this new concept, we use Singular Value Decomposition to analyze real protein datasets, and the improved protein vector representation allows us to obtain accurate gene trees.

Protein space: a natural method for realizing the nature of protein universe.

Current methods cannot tell us what the nature of the protein universe is concretely. They are based on different models of amino acid substitution and multiple sequence alignment which is an NP-hard problem and requires manual intervention. Protein structural analysis also gives a direction for mapping the protein universe. Unfortunately, now only a minuscule fraction of proteins' 3-dimensional structures are known. Furthermore, the phylogenetic tree representations are not unique for any existing tree construction methods. Here we develop a novel method to realize the nature of protein universe. We show the protein universe can be realized as a protein space in 60-dimensional Euclidean space using a distance based on a normalized distribution of amino acids. Every protein is in one-to-one correspondence with a point in protein space, where proteins with similar properties stay close together. Thus the distance between two points in protein space represents the biological distance of the corresponding two proteins. We also propose a natural graphical representation for inferring phylogenies. The representation is natural and unique based on the biological distances of proteins in protein space. This will solve the fundamental question of how proteins are distributed in the protein universe.

Protein map: an alignment-free sequence comparison method based on various properties of amino acids.

In this paper, we propose a new protein map which incorporates with various properties of amino acids. As a powerful tool for protein classification, this new protein map both considers phylogenetic factors arising from amino acid mutations and provides computational efficiency for the huge amount of data. The ten amino acid physico-chemical properties (the chemical composition of the side chain, two polarity measures, hydropathy, isoelectric point, volume, aromaticity, aliphaticity, hydrogenation, and hydroxythiolation) are utilized according to their relative importance. Moreover, during the course of calculation of genetic distances between pairs of proteins, this approach does not require any alignment of sequences. Therefore, the proposed model is easier and quicker in handling protein sequences than multiple alignment methods, and gives protein classification greater evolutionary significance at the amino acid sequence level.

A new distribution vector and its application in genome clustering.

In this paper we report a novel mathematical method to transform the DNA sequences into the distribution vectors which correspond to points in the sixty dimensional space. Each component of the distribution vector represents the distribution of one kind of nucleotide in k segments of the DNA sequences. The mathematical and statistical properties of the distribution vectors are demonstrated and examined with huge datasets of human DNA sequences and random sequences. The determined expectation and standard deviation can make the mapping stable and practicable. Moreover, we apply the distribution vectors to the clustering of the Haemagglutinin (HA) gene of 60 H1N1 viruses from Human, Swine and Avian, the complete mitochondrial genomes from 80 placental mammals and the complete genomes from 50 bacteria. The 60 H1N1 viruses, 80 placental mammals and 50 bacteria are classified accurately and rapidly compared to the multiple sequence alignment methods. The results indicate that the distribution vectors can reveal the similarity and evolutionary relationship among homologous DNA sequences based on the distances between any two of these distribution vectors. The advantage of fast computation offers the distribution vectors the opportunity to deal with a huge amount of DNA sequences efficiently.

A novel method of characterizing genetic sequences: genome space with biological distance and applications.

BACKGROUND: Most existing methods for phylogenetic analysis involve developing an evolutionary model and then using some type of computational algorithm to perform multiple sequence alignment. There are two problems with this approach: (1) different evolutionary models can lead to different results, and (2) the computation time required for multiple alignments makes it impossible to analyse the phylogeny of a whole genome. This motivates us to create a new approach to characterize genetic sequences. METHODOLOGY: To each DNA sequence, we associate a natural vector based on the distributions of nucleotides. This produces a one-to-one correspondence between the DNA sequence and its natural vector. We define the distance between two DNA sequences to be the distance between their associated natural vectors. This creates a genome space with a biological distance which makes global comparison of genomes with same topology possible. We use our proposed method to analyze the genomes of the new influenza A (H1N1) virus, human rhinoviruses (HRV) and mammalian mitochondrial. The result shows that a triple-reassortant swine virus circulating in North America and the Eurasian swine virus belong to the lineage of the influenza A (H1N1) virus. For the HRV and mammalian mitochondrial genomes, the results coincide with biologists' analyses. CONCLUSIONS: Our approach provides a powerful new tool for analyzing and annotating genomes and their phylogenetic relationships. Whole or partial genomes can be handled more easily and more quickly than using multiple alignment methods. Once a genome space has been constructed, it can be stored in a database. There is no need to reconstruct the genome space for subsequent applications, whereas in multiple alignment methods, realignment is needed to add new sequences. Furthermore, one can make a global comparison of all genomes simultaneously, which no other existing method can achieve.

A novel construction of genome space with biological geometry.

A genome space is a moduli space of genomes. In this space, each point corresponds to a genome. The natural distance between two points in the genome space reflects the biological distance between these two genomes. Currently, there is no method to represent genomes by a point in a space without losing biological information. Here, we propose a new graphical representation for DNA sequences. The breakthrough of the subject is that we can construct the moment vectors from DNA sequences using this new graphical method and prove that the correspondence between moment vectors and DNA sequences is one-to-one. Using these moment vectors, we have constructed a novel genome space as a subspace in R(N). It allows us to show that the SARS-CoV is most closely related to a coronavirus from the palm civet not from a bird as initially suspected, and the newly discovered human coronavirus HCoV-HKU1 is more closely related to SARS than to any other known member of group 2 coronavirus. Furthermore, we reconstructed the phylogenetic tree for 34 lentiviruses (including human immunodeficiency virus) based on their whole genome sequences. Our genome space will provide a new powerful tool for analyzing the classification of genomes and their phylogenetic relationships.

A rapid method for characterization of protein relatedness using feature vectors.

We propose a feature vector approach to characterize the variation in large data sets of biological sequences. Each candidate sequence produces a single feature vector constructed with the number and location of amino acids or nucleic acids in the sequence. The feature vector characterizes the distance between the actual sequence and a model of a theoretical sequence based on the binomial and uniform distributions. This method is distinctive in that it does not rely on sequence alignment for determining protein relatedness, allowing the user to visualize the relationships within a set of proteins without making a priori assumptions about those proteins. We apply our method to two large families of proteins: protein kinase C, and globins, including hemoglobins and myoglobins. We interpret the high-dimensional feature vectors using principal components analysis and agglomerative hierarchical clustering. We find that the feature vector retains much of the information about the original sequence. By using principal component analysis to extract information from collections of feature vectors, we are able to quickly identify the nature of variation in a collection of proteins. Where collections are phylogenetically or functionally related, this is easily detected. Hierarchical agglomerative clustering provides a means of constructing cladograms from the feature vector output.

Serum amyloid A induces G-CSF expression and neutrophilia via Toll-like receptor 2.

The acute-phase protein serum amyloid A (SAA) is commonly considered a marker for inflammatory diseases; however, its precise role in inflammation and infection, which often result in neutrophilia, remains ambiguous. In this study, we demonstrate that SAA is a potent endogenous stimulator of granulocyte colony-stimulated factor (G-CSF), a principal cytokine-regulating granulocytosis. This effect of SAA is dependent on Toll-like receptor 2 (TLR2). Our data demonstrate that, in mouse macrophages, both G-CSF mRNA and protein were significantly increased after SAA stimulation. The induction of G-CSF was blocked by an anti-TLR2 antibody and markedly decreased in the TLR2-deficient macrophages. SAA stimulation results in the activation of nuclear factor-kappaB and binding activity to the CK-1 element of the G-CSF promoter region. In vitro reconstitution experiments also support that TLR2 mediates SAA-induced G-CSF expression. In addition, SAA-induced secretion of G-CSF was sensitive to heat and proteinase K treatment, yet insensitive to polymyxin B treatment, indicating that the induction is a direct effect of SAA. Finally, our in vivo studies confirmed that SAA treatment results in a significant increase in plasma G-CSF and neutrophilia, whereas these responses are ablated in G-CSF- or TLR2-deficient mice.

beta-Arrestin1 interacts with the G-protein subunits beta1gamma2 and promotes beta1gamma2-dependent Akt signalling for NF-kappaB activation.

beta-Arrestins are known to regulate G-protein signalling through interactions with their downstream effectors. In the present study, we report that beta-arrestin1 associates with the G-protein beta1gamma2 subunits in transfected cells, and purified beta-arrestin1 interacts with G(beta1gamma2) derived from in vitro translation. Deletion mutagenesis of beta-arrestin1 led to the identification of a region, comprising amino acids 181-280, as being responsible for its interaction with G(beta1gamma2). Overexpression of beta-arrestin1 facilitates G(beta1gamma2)-mediated Akt phosphorylation, and inhibition of endogenous beta-arrestin1 expression by siRNA (small interfering RNA) diminishes this effect. Through investigation of NF-kappaB (nuclear factor kappaB), a transcription factor regulated by Akt signalling, we have found that overexpression of beta-arrestin1 significantly enhances G(beta1gamma2)-mediated nuclear translocation of NF-kappaB proteins and expression of a NF-kappaB-directed luciferase reporter. Overexpression of beta-arrestin1 also promotes bradykinin-induced, G(betagamma)-mediated NF-kappaB luciferase-reporter expression, which is reverted by silencing the endogenous beta-arrestin1 with a specific siRNA. These results identify novel functions of beta-arrestin1 in binding to the beta1gamma2 subunits of heterotrimeric G-proteins and promoting G(betagamma)-mediated Akt signalling for NF-kappaB activation.

Identification of formyl peptides from Listeria monocytogenes and Staphylococcus aureus as potent chemoattractants for mouse neutrophils.

The prototypic formyl peptide N-formyl-Met-Leu-Phe (fMLF) is a major chemoattractant found in Escherichia coli culture supernatants and a potent agonist at human formyl peptide receptor (FPR) 1. Consistent with this, fMLF induces bactericidal functions in human neutrophils at nanomolar concentrations. However, it is a much less potent agonist for mouse FPR (mFPR) 1 and mouse neutrophils, requiring micromolar concentrations for cell activation. To determine whether other bacteria produce more potent agonists for mFPR1, we examined formyl peptides from Listeria monocytogenes and Staphylococcus aureus for their abilities to activate mouse neutrophils. A pentapeptide (N-formyl-Met-Ile-Val-Ile-Leu (fMIVIL)) from L. monocytogenes and a tetrapeptide (N-formyl-Met-Ile-Phe-Leu (fMIFL)) from S. aureus were found to induce mouse neutrophil chemotaxis at 1-10 nM and superoxide production at 10-100 nM, similar to the potency of fMLF on human neutrophils. Using transfected cell lines expressing mFPR1 and mFPR2, which are major forms of FPRs in mouse neutrophils, we found that mFPR1 is responsible for the high potency of fMIVIL and fMIFL. In comparison, activation of mFPR2 requires micromolar concentrations of the two peptides. Genetic deletion of mfpr1 resulted in abrogation of neutrophil superoxide production and degranulation in response to fMIVIL and fMIFL, further demonstrating that mFPR1 is the primary receptor for detection of these formyl peptides. In conclusion, the formyl peptides from L. monocytogenes and S. aureus are approximately 100-fold more potent than fMLF in activating mouse neutrophils. The ability of mFPR1 to detect bacterially derived formyl peptides indicates that this important host defense mechanism is conserved in mice.