Journal of Data Mining in Genomics & Proteomics

Language Gene Polymorphism Patterns: Important Information on Human Evolution

Wei Xia^1,2 and Zhizhou Zhang^{1* 1}BIOX Center for Biotechnology, Harbin Institute of Technology, Weihai, China
²School of Languages and Literature, Harbin Institute of Technology, Weihai, China
^*Correspondence: Zhizhou Zhang, BIOX Center for Biotechnology, Harbin Institute of Technology, Weihai, China, Email:

Received: 14-Aug-2023, Manuscript No. JDMGP-23-22589; Editor assigned: 17-Aug-2023, Pre QC No. JDMGP-23-22589 (PQ); Reviewed: 31-Aug-2023, QC No. JDMGP-23-22589; Revised: 07-Sep-2023, Manuscript No. JDMGP-23-22589 (R); Published: 14-Sep-2023, DOI: 10.4172/2153- 0602.23.14.316

Introduction

Chimpanzee’s learning ability, including language ability, has been investigated for many years, one of the aims of which is to understand why and how human performs much better than Primates [1-6]. Especially, which differences in brain structures or (language) genes may contribute to the learning performance levels. Language ability has been a significant issue to investigate and compare among human individuals, chimpanzees (Pan troglodytes), bonobos (Pan paniscus) and other primates. Language ability can be tested from listening, speaking to reading and writing. Apparently, listening itself may be not a problem for a chimpanzee, but speaking, reading and writing are too far away from chimpanzees’ capacity, though some other animals already possess ability for word regularities (affixation) [7-9]. It seems that this big difference derives only from the 1%-2% difference in the genome sequences of human and chimpanzee. Communication between chimpanzees/bonobos in the wild takes the form of gestures, facial expressions, and a plenty of vocalization types, including grunts, roars, hoots, and screams. However, both kinds of Primates cannot orally speak like human even after many years of education. The less dependence on tools, likely the less need to develop languages, because description of tool activities cannot be well fulfilled simply by non-oral expressions.

Language is a structured system that consists of grammar and vocabulary, by which humans convey meaning in the forms of spoken, written or signs of language. There are over 7000 different human languages with significant variations in cultural and historical diversity. Meanwhile, language has its own biological root. By now over a dozen of human language genes have been preliminarily characterized (Table 1). Though the human version of the gene FOXP2 harbors changes not found in chimpanzees or other primates, it is not reasonable to explain key language puzzles by a single mutation in modern humans [10,11]. But thousands of such mutations in language genes may help to find some patterns pointing to important issues, such as the cause of leaning ability difference. As the first step to this direction, this study employed 189 SNPs from 13 language genes in 29 whole genomes and found some SNP points that can significantly distinguish between human and primates.

Table 1: Language genes employed in this study.

Language ability: An original complicated procedure of muscle movements

Language ability is one of the core features of the human species. The evolution of language ability should also witness the evolution of the human brain’s intelligence and the vocal organs, taking millions to tens of millions of years. The earliest primate’s record is about 60 million years. In 2022, it was reported that Sahelanthropus tchadensis (the known earliest human-like ape or early-stage Homo erectus) was able to walk upright 7 million years ago, and other studies inferred that late-stage Homo erectus should already be able to use language fluently [12-14]. The communication between apes is mainly gesture language, which goes through a long process with the evolution of the brain (and gradually solidified various gesture language and body language into a brain signal), and is reflected in the way of sound [15]. Sound is also produced by the muscle movement of specific organs of the body, which is the mechanical movement of the occurring organs. This can also explain why language control regions in the brain are highly overlapping with motor control regions [16].

Evolution after upright walking: Due to eating cooked food and genetic mutations, the brain organs have the ability to process complex signals; All of the organs are connected to the nervous system. The organs of the eyes, ears and mouth gradually produce a preliminary language; The movement of both hands also greatly promotes the generation of language; Language processes are actually similar to other limb movements, though language is the movement of the organs of the eyes, ears and mouth; The evolution process of language gene polymorphism is the process of the gradual improvement of language ability. The eyes, ears and mouths share the same neural control circuit as bimanual movements, because, in the course of evolution, the eyes, ears and mouths themselves are one of the most moving organs, or the most primitive motor organs. They received the most neural connections during evolution, laying the foundation for functional complexity. Meanwhile, technology for making and using tools in Homo erectus has also further evolved, and these techniques cannot be delivered precisely without the aid of language. Evolution may make the brain suddenly have a complex-enough computing power, opening an evolutionary opportunity for coordination between audiovisual stimulation signals and muscle movements, including opportunities to improve language levels [17].

Concepts of language gene and Language Gene Polymorphism Pattern (LGPP)

Genes directly related to language ability are called language genes. The direct correlation here means that if a gene is deleted or mutated or significantly altered in quantitative genetic traits, all or part of the language function is lost or weakened. At present, there are about 19 human language genes [13,18,19], with a large number of Single Nucleotide Polymorphism (SNP) or mutation sites of Single Nucleotide Variation (SNV) on the sequence of each language gene. A total of 19 language genes can be several million SNP/SNV loci that can be used to describe the patterns of language gene polymorphism and their evolution dynamics in different ancient human samples.

LGPPs of human species and the primates

Research on advanced primates has been conducted for nearly a century. One of the main questions to clarify in such studies is understanding how higher primates such as chimpanzees evolved into humans. The differences between humans and chimpanzees, especially abilities in language and cognition are of primary concern. A large number of studies can be seen in some reviews, but few studies have been observed between the two from the perspective of language gene polymorphism patterns [20,21]. In this study, 189 SNPs (Single Nucleotide Polymorphism) in 13 language genes were scanned in 29 whole genomes from different human and primates populations.

Materials and Methods

Language genes and their SNPs

In the Table 1 listed 13 language genes (as a preliminary observation, only 13 language genes were employed at the time the manuscript was written), and a total 189 SNPs from these 13 genes were selected for this study. Language gene SNP data were all semi-randomly selected (Figure 1) for each gene in the dbSNP database: https://www.ncbi.nlm.nih.gov/snp/; Some SNPs have limited information on clinical effects as shown in dbSNP and GeneCards databases (Table 2).

Table 2: Tested 189 SNPs of thirteen language genes.

Figure 1: Semi-randomly selected SNP sites and their approximate positions in 13 language genes. Note: The + sign above a triangle means the SNP site had a known clinical phenotype as indicated in dbSNP and GeneCards databases.

Genome sequences

All genome sequences were downloaded from ENA database (https://www.ebi.ac.uk/ena/browser/) in the fast format (Table 3). In all 29 genomes, the sizes mainly range from 41G to 200G. There are 14 representative human samples in which 10 were ancient samples. Four from Asia, one from Africa, eight from Europe and one from South America. In the 15 primate samples, two from China, four from Indonesia and the left eight from Africa.

Table 3: The 29 whole genomes employed in this study.

SNP information extraction from genome sequences

This study was actually to investigate in what extent the primates and ancient human samples still hold the same SNP sequences as modern human, since the query sequences of SNPs were all downloaded from present dbSNP database that harbors mainly modern human SNPs. The authors used 010 Editor software to extract all 189 SNP information from each genome. For each SNP, about 20 left-flanking or right-flanking query nucleotides were searched in the genome in both DNA strands. Perfectly matched sequences were recorded using the SNP base A, T, G, C or combinations with them. For example, some sample may have both bases in a specific SNP sites, and thus recorded as AC, GC, etc. For those target sequences not perfectly matched around the SNP site, if the imperfect match occurs at the left flanking region, the SNP was recorded like A-, T-, C- or G-; if the imperfection occurs at the right flanking region, the SNP was recorded like A+, T+, C+ or G+ as in Table 4. Both-sides imperfection may occur such as A-G+, T-C- and A+C+. Imperfection counts only in the 4 bases left or right around the SNP site when at least one base in the four was mismatched (while the other bases in the query sequence were all matched). Any other cases of imperfection will be recorded as zero. The collected SNP data and the digitalized version can be requested from the authors.

PCA analysis with R codes

Principal Component Analysis (PCA) was performed using R packages FactoMineR, factoextra and ggplot2. The main R codes are listed as follow. SNP data had to be digitalized before PCA performance. All SNP alleles were written in the sequence of A, T, G and C. For example, A, T, GC (not CG), TC (not CT) and ATC. For A, T, G and C, 999000000000, 999000000, 999000, and 999 were assigned, respectively. For two-letter SNP cases, such as AT, AC and GC, 999999000000, 999000000999 and 999999 were used, respectively.

For those with left-flanking im-perfection, for example, A-,G- and C-, 997000000000, 997000 and 997 were assigned, respectively; for those with right-flanking im-perfection or both-sides im-perfection, such as T+, G+, A-C+, A+GC- and A+C-, 998000000, 998000, 997000000998, 998000999997 and 998000000997 were assigned, respectively.

> library(FactoMineR)

> library(factoextra)

> library(ggplot2)

> country <- read.delim(‘C:/RBook/20230315fastqSNPdata.txt’, row.names = 1, sep = ‘\t’)

> country <- t(country)

> country.pca <- PCA(country, ncp = 2, scale.unit = TRUE, graph = FALSE)

> plot(country.pca)

> pca_sample <- data.frame(country.pca$ind$coord[,1:2])

> head(pca_sample)

> pca_eig1 <- round(country.pca$eig[1,2], 2)

> pca_eig2 <- round(country.pca$eig[2,2],2 )

> pca_eig1

> pca_eig2

> group <- read.delim(‘C:/RBook/group3.txt’, row.names = 1, sep = ‘\t’, check.names = FALSE)

> group <- group [rownames(pca_sample), ]

> pca_sample <- cbind(pca_sample, group)

> pca_sample$samples <- rownames(pca_sample)

> head(pca_sample)

> library(ggrepel)

> ggplot (data = pca_sample, aes (x = Dim.1, y = Dim.2)) + geom_ point (aes (color = group), size = 3) + scale_color_manual (values = c(‘purple’, ‘red’, ‘green’, ‘blue’, ‘brown’, ‘pink’, ‘yellow’, ‘orange’, ‘grey’)) + theme (panel.grid = element_blank(), panel.background = element_rect (color = ‘black’, fill = ‘transparent’), legend.key = element_rect (fill=’transparent’)) + labs(x=paste(‘PCA1:’, pca_eig1, ‘%’), y=paste(‘PCA2:’, pca_eig2, ‘%’), color = ‘’) + geom_text_repel (aes (label = samples), size = 3, show.legend = FALSE, box.padding = unit(0.25, ‘lines’))

Result and Discussion

There is a very high density between primate samples, and there is a significant gap from the primates to human samples, though human samples are very dispersed (Figure 2). This result implies that either there are other intermediate species between primates and modern human, for example, different types of Homo erectus, or that the key LGPPs arise only after the emergence of Homo erectus. By now, the authors haven’t been able to collect genome sequences from fossil Australopithecus, including different types of Homo erectus. Hopefully their LGPPs just lie in the gap between the primates and modern human. There are some literature suggesting that the key events in language evolution occurred only in the hominin lineage, after the divergence from panins [20].

Figure 2: Principle Component Analysis (PCA) demonstration for 29 samples in Table 3. Euro: Europe; SEAsia: South Asia; SouthAm: South America. Note: South Asia.

It is also possible that the language gene polymorphism pattern is not enough to distinguish humans from other animals, such as dolphins and parrots whose LGPPs are actually close to humans (data not shown). People probably have to rely on a mixed polymorphism pattern from both language genes and cognition genes. In theory, this hybrid model may be a better point to support the development of completely new language learning methods. This needs to be seen in much subsequent research [22-39].

Language evolution involves several important processes of Australopithecus-Man ape-Ape man-Homo erectus-Homo sapiens. Several key breakthroughs in language evolution were believed to be completed at some points in some above process. LGPP investigation may open a window for us to reach some breakthroughs. In Table 4, those SNPs with apparent difference between human and the primates were listed in nine language genes, and several SNP sites were significantly different between human samples and the primates, such as s1,s2,s3,s7,and s8 (also Table 5). The potential interaction network of the nine genes was described in Figure 3 with STRING tool in GeneCards database. Is that possible that human alleles and the primates alleles will lead to emergence of language ability’s great leap-forward? S7 and s8 refer to TPK1 gene, which has evidences to be associated with syntax and lexical retrieval, two important prerequisites for grammar function [40].

Table 4: Nineteen SNPs (s1-s19) that may separate human from primates (I).

Table 5: Nineteen SNPs (s1-s19) that may separate human from primates (II).

Figure 3: Potential molecular interactions among nine genes (as proteins) in Table 5. TPK1 and TM4SF20 were not linked in the potential network. Colors of the lines between two genes represent interaction types (see String database).

Since language ability was first just a muscle movement behavior, from this perspective, language must have evolved much earlier than human evolution. This is evident from other results (data not shown) of our preliminary study. We found that the language gene polymorphism pattern of parrots and dolphins is very close to that of some ancient humans and some modern humans, but the language gene polymorphism pattern of advanced primates such as chimpanzees is obviously or relatively far from human beings; From the ratio of brain weight to body weight, the human brain accounted for 2.1% of body weight, dolphins 1.17%, and chimpanzees only 0.7%. The fossils of parrots reached 55 million years; the 50 million year ancestor of Dolphin, Pakicetus, was found in Pakistan, and the dolphin family emerged from the Miocene about 12 million years ago [40]. But the earliest known upright Homo species was Chad Australopithecus only 7 million years ago. So these data indicate that some language gene polymorphism patterns appeared in the evolution much earlier than dolphins and other animals. If this is true, LGPP itself may be not enough to explain language ability difference between human and chimpanzees.

Conclusion

In this study, 189 semi-randomly selected SNPs (Single Nucleotide Polymorphism) in 13 language genes were scanned in 29 whole genomes from different human and primates populations. The 19 distinct SNPs in primates genomes were found in several language genes including TPK1 that correlates with human’s syntactic and lexical ability. PCA result indicated that the language gene polymorphism pattern does demonstrate differences between primates and representative human samples, and the difference appears quite obvious. This difference suggests that there may be some intermediate states between primates and modern humans, presumably among the late-stage apes, Homo erectus, or early Homo sapiens. But these intermediate species samples are the most scarce, usually very precious fossil-bone, skeleton or tooth samples. Given that investigations by 189 SNPs in 13 language genes cannot distinguish dolphins and human samples (data not shown) but already enable the separation Primates from human, a large number of SNPs and genes plus more various human samples are expected to employ in the near future.

Acknowledgment

This study was supported by State Language Commission Research Grant (YB135-117) and National Research Center for Foreign Language Education Grant (ZGWYJYJJ10A042).

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