Logo: to the web site of Uppsala University

uu.sePublications from Uppsala University
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Ancient proteins and the deepest population divergence among modern human populations
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Human Evolution. (Schlebusch group)ORCID iD: 0000-0003-4434-1934
Show others and affiliations
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Although ancient DNA significantly improved our understanding of human evolution and human history, it also has its limitations, such as the survival and preservation of DNA. Proteins also contain valuable molecular biological information and generally preserve longer than DNA. Especially for very old samples in hot and humid environments, protein data may provide unique information, which in turn could be extremely valuable for deciphering early human evolution in Africa. We investigate the potential differences in bone- and teeth-related protein sequences between the two groups representing the deepest population divergence in the Homo sapiens tree, namely southern African Khoe-San groups on the one hand, and the rest of humanity on the other hand. We generate protein sequences by both in silico translating modern genomic sequence data, and we obtain directly sequenced protein data from 18 ancient human samples from Africa. Our analysis led to the development of a machine learning model based on 88 amino acids that exhibit frequency differences between Khoe-San and non-Khoe-San individuals. We show that 14 of these amino acids can be investigated in ancient human remains through proteomic analysis. These findings pave the way towards the careful consideration and exploration of protein analysis in older samples, to shed light on population continuity and ancient population structure in sub-Saharan Africa.

National Category
Genetics and Genomics
Identifiers
URN: urn:nbn:se:uu:diva-537824OAI: oai:DiVA.org:uu-537824DiVA, id: diva2:1895251
Available from: 2024-09-05 Created: 2024-09-05 Last updated: 2025-02-07
In thesis
1. Investigating African population structure using genomes and proteomes
Open this publication in new window or tab >>Investigating African population structure using genomes and proteomes
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Modern humans originated in Africa and this continent is home to the highest genetic diversity in the world. Despite this, Africa remains an understudied region. In this thesis, I use genetic and proteomic data to unravel the demographic history of modern humans in Africa. By employing various methods on different data types, I explore both broad genetic patterns, as well as fine-scale structure in African populations. In Paper I, I focus on the methodological aspects of researching maternal histories in sub-Saharan Africa. I explore mitochondrial haplogroup assignment in African populations when genome-wide SNP array data is available and conclude that mitochondrial haplogroup frequencies inferred from most common SNP arrays used for human population analysis should be considered with caution. In Paper II, I developed a long-range sequence assay to amplify full-sequence mitochondrial genomes and used this novel method to generate complete mitogenomes from underrepresented regions in Africa. Combined with published mitogenomes, these data provide an overview of African mitochondrial haplogroups and give insights into the maternal background of the African continent. By analyzing female effective population sizes over time, I discovered that two population expansions happened earlier than previously thought. In the next paper, Paper III, I utilized target enrichment to sequence portions of the Y chromosome from sub-Saharan African men. This study gives an overview of the paternal lineages of the studied populations and identifies three types of geographical distributions across the Y haplogroups in our dataset. As DNA preservation and survival are limited, I shift the focus from DNA to proteins in Paper IV. I explored the use of proteomics in ancient individuals to decipher deep population structure in Africa by investigating the potential differences in protein sequences between the two groups representing the deepest population divergence in the tree of all modern humans. I identify amino acid variation between Southern African hunter-gatherer Khoe-San groups on the one hand, and the rest of humanity on the other hand and show that these amino acids can be investigated in ancient individuals from Africa. In Paper V, I investigate the demographic histories of one population specifically; the South African Coloured (SAC), the most admixed population of South Africa. Using genotype data from these individuals, I identify geographical differences in ancestry proportions and sex-biased admixture in the SAC. Taken altogether, my work has deepened our understanding of continental and regional genetic structure in African populations.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2024. p. 75
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 2446
Keywords
human evolutionary genetics, African population structure, proteomics, long-read sequencing, uniparental markers, SNP array data, NGS sequencing
National Category
Genetics and Genomics
Research subject
Biology with specialization in Evolutionary Genetics
Identifiers
urn:nbn:se:uu:diva-537831 (URN)978-91-513-2224-7 (ISBN)
Public defence
2024-10-25, Friessalen, Evolutionsbiologiskt centrum (EBC), Norbyvägen 14, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2024-10-01 Created: 2024-09-05 Last updated: 2025-02-07

Open Access in DiVA

No full text in DiVA

Search in DiVA

By author/editor
Lankheet, Imke
By organisation
Human Evolution
Genetics and Genomics

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 1076 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf