Paul Brennan, Stephen Chanock and colleagues performed a genome-wide association study for renal carcinoma. They identified two genetic susceptibility loci. We conducted a two-stage genome-wide association study of renal cell carcinoma (RCC) in 3,772 affected individuals (cases) and 8,505 controls of European background from 11 studies and followed up 6 SNPs in 3 replication studies of 2,198 cases and 4,918 controls. Two loci on the regions of 2p21 and 11q13.3 were associated with RCC susceptibility below genome-wide significance. Two correlated variants (r2 = 0.99 in controls), rs11894252 (P = 1.8 × 10−8) and rs7579899 (P = 2.3 × 10−9), map to EPAS1 on 2p21, which encodes hypoxia-inducible-factor-2 alpha, a transcription factor previously implicated in RCC. The second locus, rs7105934, at 11q13.3, contains no characterized genes (P = 7.8 × 10−14). In addition, we observed a promising association on 12q24.31 for rs4765623, which maps to SCARB1, the scavenger receptor class B, member 1 gene (P = 2.6 × 10−8). Our study reports previously unidentified genomic regions associated with RCC risk that may lead to new etiological insights.

Recent advancements in genomics, propelled by artificial intelligence, have unlocked unprecedented capabilities in interpreting genomic sequences, mitigating the need for exhaustive experimental analysis of complex, intertwined molecular processes inherent in DNA function. A significant challenge, however, resides in accurately decoding genomic sequences, which inherently involves comprehending rich contextual information dispersed across thousands of nucleotides. To address this need, we introduce GENA language model (GENA-LM), a suite of transformer-based foundational DNA language models capable of handling input lengths up to 36 000 base pairs. Notably, integrating the newly developed recurrent memory mechanism allows these models to process even larger DNA segments. We provide pre-trained versions of GENA-LM, including multispecies and taxon-specific models, demonstrating their capability for fine-tuning and addressing a spectrum of complex biological tasks with modest computational demands. While language models have already achieved significant breakthroughs in protein biology, GENA-LM showcases a similarly promising potential for reshaping the landscape of genomics and multi-omics data analysis. All models are publicly available on GitHub (https://github.com/AIRI-Institute/GENA_LM) and on HuggingFace (https://huggingface.co/AIRI-Institute). In addition, we provide a web service (https://dnalm.airi.net/) allowing user-friendly DNA annotation with GENA-LM models.

Recent advancements in genomics, propelled by artificial intelligence, have unlocked unprecedented capabilities in interpreting genomic sequences, mitigating the need for exhaustive experimental analysis of complex, intertwined molecular processes inherent in DNA function. A significant challenge, however, resides in accurately decoding genomic sequences, which inherently involves comprehending rich contextual information dispersed across thousands of nucleotides. To address this need, we introduce GENA-LM, a suite of transformer-based foundational DNA language models capable of handling input lengths up to 36,000 base pairs. Notably, integration of the newly-developed Recurrent Memory mechanism allows these models to process even larger DNA segments. We provide pre-trained versions of GENA-LM, demonstrating their capability for fine-tuning and addressing a spectrum of complex biological tasks with modest computational demands. While language models have already achieved significant breakthroughs in protein biology, GENA-LM showcases a similarly promising potential for reshaping the landscape of genomics and multi-omics data analysis. All models are publicly available on GitHub https://github.com/AIRI-Institute/GENA LM and HuggingFace https://huggingface.co/AIRI-Institute.

Mammalian forms of the transcription repressor, Kaiso, can reportedly bind methylated DNA and non-methylated CTGCNA motifs. Here we compare the DNA-binding properties of Kaiso from frog, fish and chicken and demonstrate that only the methyl-CpG-binding function of Kaiso is evolutionarily conserved. We present several independent experimental lines of evidence that the phenotypic abnormalities associated with xKaiso-depleted Xenopus laevis embryos are independent of the putative CTGCNA-dependent DNA-binding function of xKaiso. Our analysis suggests that xKaiso does not play a role in the regulation of either xWnt11 or Siamois, key signalling molecules in the Wnt pathway during X. laevis gastrulation. The major phenotypic defects associated with xKaiso depletion are premature transcription activation before the mid-blastula transition and concomitant activation of a p53-dependent cell-death pathway.

The history of human populations occupying the plains and mountain ridges separating Europe from Asia has been eventful, as these natural obstacles were crossed westward by multiple waves of Turkic and Uralic-speaking migrants as well as eastward by Europeans. Unfortunately, the material records of history of this region are not dense enough to reconstruct details of population history. These considerations stimulate growing interest to obtain a genetic picture of the demographic history of migrations and admixture in Northern Eurasia. We genotyped and analyzed 1076 individuals from 30 populations with geographical coverage spanning from Baltic Sea to Baikal Lake. Our dense sampling allowed us to describe in detail the population structure, provide insight into genomic history of numerous European and Asian populations, and significantly increase quantity of genetic data available for modern populations in region of North Eurasia. Our study doubles the amount of genome-wide profiles available for this region. We detected unusually high amount of shared identical-by-descent (IBD) genomic segments between several Siberian populations, such as Khanty and Ket, providing evidence of genetic relatedness across vast geographic distances and between speakers of different language families. Additionally, we observed excessive IBD sharing between Khanty and Bashkir, a group of Turkic speakers from Southern Urals region. While adding some weight to the “Finno-Ugric” origin of Bashkir, our studies highlighted that the Bashkir genepool lacks the main “core”, being a multi-layered amalgamation of Turkic, Ugric, Finnish and Indo-European contributions, which points at intricacy of genetic interface between Turkic and Uralic populations. Comparison of the genetic structure of Siberian ethnicities and the geography of the region they inhabit point at existence of the “Great Siberian Vortex” directing genetic exchanges in populations across the Siberian part of Asia. Slavic speakers of Eastern Europe are, in general, very similar in their genetic composition. Ukrainians, Belarusians and Russians have almost identical proportions of Caucasus and Northern European components and have virtually no Asian influence. We capitalized on wide geographic span of our sampling to address intriguing question about the place of origin of Russian Starovers, an enigmatic Eastern Orthodox Old Believers religious group relocated to Siberia in seventeenth century. A comparative reAdmix analysis, complemented by IBD sharing, placed their roots in the region of the Northern European Plain, occupied by North Russians and Finno-Ugric Komi and Karelian people. Russians from Novosibirsk and Russian Starover exhibit ancestral proportions close to that of European Eastern Slavs, however, they also include between five to 10 % of Central Siberian ancestry, not present at this level in their European counterparts. Our project has patched the hole in the genetic map of Eurasia: we demonstrated complexity of genetic structure of Northern Eurasians, existence of East-West and North-South genetic gradients, and assessed different inputs of ancient populations into modern populations.

The three epidemiologically important Opisthorchiidae liver flukes Opisthorchis felineus, O. viverrini, and Clonorchis sinensis, are believed to harbour similar potencies to provoke hepatobiliary diseases in their definitive hosts, although their populations have substantially different ecogeographical aspects including habitat, preferred hosts, population structure. Lack of O. felineus genomic data is an obstacle to the development of comparative molecular biological approaches necessary to obtain new knowledge about the biology of Opisthorchiidae trematodes, to identify essential pathways linked to parasite-host interaction, to predict genes that contribute to liver fluke pathogenesis and for the effective prevention and control of the disease. Here we present the first draft genome assembly of O. felineus and its gene repertoire accompanied by a comparative analysis with that of O. viverrini and Clonorchis sinensis. We observed both noticeably high heterozygosity of the sequenced individual and substantial genetic diversity in a pooled sample. This indicates that potency of O. felineus population for rapid adaptive response to control and preventive measures of opisthorchiasis is higher than in O. viverrini and C. sinensis. We also have found that all three species are characterized by more intensive involvement of trans-splicing in RNA processing compared to other trematodes. All revealed peculiarities of structural organization of genomes are of extreme importance for a proper description of genes and their products in these parasitic species. This should be taken into account both in academic and applied research of epidemiologically important liver flukes. Further comparative genomics studies of liver flukes and non-carcinogenic flatworms allow for generation of well-grounded hypotheses on the mechanisms underlying development of cholangiocarcinoma associated with opisthorchiasis and clonorchiasis as well as species-specific mechanisms of these diseases.