By Liming Li, Troy J. Comi, Rob F. Bierman, and Joshua M. Akey

INTRODUCTION

For much of modern human history, we were only one of several different groups of hominins that existed. Studies of ancient and modern DNA have shown that admixture occurred multiple times among different hominin lineages, including between the ancestors of modern humans and Neanderthals. A number of methods have been developed to identify Neanderthal-introgressed sequences in the DNA of modern humans, which have provided insight into how admixture with Neanderthals shaped the biology and evolution of modern human genomes. Although gene flow from an early modern human population to Neanderthals has been described, the consequences of admixture on the Neanderthal genome have received comparatively less attention.

RATIONALE

A better understanding of how admixture with modern humans influenced patterns of Neanderthal genomic variation may provide insights into hominin evolutionary history. For example, DNA sequences inherited from modern human ancestors in Neanderthals can be used to test hypotheses on the frequency, magnitude, and timing of admixture and the population genetics characteristics of Neanderthals. Introgressed modern human sequences in Neanderthals can also be used to refine estimates of Neanderthal ancestry in contemporary individuals. We developed a simple framework to investigate introgressed human sequences in Neanderthals that is predicated on the expectation that sequences inherited from modern human ancestors would be, on average, more genetically diverse and would result in local increases in heterozygosity across the Neanderthal genome.

RESULTS

We first used a method we previously created called IBDmix to identify introgressed Neanderthal sequences in 2000 modern humans sequenced by the 1000 Genomes Project. We found that sequences identified by IBDmix as Neanderthal in African individuals from the 1000 Genomes Project are significantly enriched in regions of high heterozygosity in the Neanderthal genome, whereas no such enrichment is observed with sequences detected as introgressed in non-African individuals. We show that these patterns are caused by gene flow from modern humans to Neanderthals and estimate that the Vindija and Altai Neanderthal genomes have 53.9 Mb (2.5%) and 80.0 Mb (3.7%) of human-introgressed sequences, respectively. We leverage human-introgressed sequences in Neanderthals to revise estimates of the amount of Neanderthal-introgressed sequences in modern humans. Additionally, we show that human-introgressed sequences cause Neanderthal population size to be overestimated and that accounting for their effects decrease estimates of Neanderthal population size by ~20%. Finally, we found evidence for two distinct epochs of human gene flow into Neanderthals.

CONCLUSION

Our results provide insights into the history of admixture between modern humans and Neanderthals, show that gene flow had substantial impacts on patterns of modern human and Neanderthal genomic variation, and show that accounting for human-introgressed sequences in Neanderthals enables more-accurate inferences of admixture and its consequences in both Neanderthals and modern humans. More generally, the smaller estimated population size and inferred admixture dynamics are consistent with a Neanderthal population that was decreasing in size over time and was ultimately being absorbed into the modern human gene pool.

Read the paper: https://doi.org/10.1126/science.adi1768

By Berggren KA, Sinha S, Lin AE, Schwoerer MP, Maya S, Biswas A, Cafiero TR, Liu Y, Gertje HP, Suzuki S, Berneshawi AR, Carver S, Heller B, Hassan N, Ali Q, Beard D, Wang D, Cullen JM, Kleiner RE, Crossland NA, Schwartz RE, Ploss A.

Modification of RNA with N⁶-methyladenosine (m⁶A) has gained attention in recent years as a general mechanism of gene regulation. In the liver, m⁶A, along with its associated machinery, has been studied as a potential biomarker of disease and cancer, with impacts on metabolism, cell cycle regulation, and pro-cancer state signaling. However these observational data have yet to be causally examined in vivo. For example, neither perturbation of the key m⁶A writers Mettl3 and Mettl14, nor the m⁶A readers Ythdf1 and Ythdf2 have been thoroughly mechanistically characterized in vivo as they have been in vitro. To understand the functions of these machineries, we developed mouse models and found that deleting Mettl14 led to progressive liver injury characterized by nuclear heterotypia, with changes in mRNA splicing, processing and export leading to increases in mRNA surveillance and recycling.

Read the paper: https://doi.org/10.1101/2024.06.17.599413

By Matthew A. Myers, Brian J. Arnold, Vineet Bansal, Metin Balaban, Katelyn M. Mullen, Simone Zaccaria & Benjamin J. Raphael

Bulk DNA sequencing of multiple samples from the same tumor is becoming common, yet most methods to infer copy-number aberrations (CNAs) from this data analyze individual samples independently. We introduce HATCHet2, an algorithm to identify haplotype- and clone-specific CNAs simultaneously from multiple bulk samples. HATCHet2 extends the earlier HATCHet method by improving identification of focal CNAs and introducing a novel statistic, the minor haplotype B-allele frequency (mhBAF), that enables identification of mirrored-subclonal CNAs. We demonstrate HATCHet2’s improved accuracy using simulations and a single-cell sequencing dataset. HATCHet2 analysis of 10 prostate cancer patients reveals previously unreported mirrored-subclonal CNAs affecting cancer genes.

Read the paper: https://doi.org/10.1186/s13059-024-03267-x

By Amy Defnet, William Hasling, Laura Condon, Amy Johnson, Georgios Artavanis, Amanda Triplett, William Lytle, and Reed Maxwell

The field of hydrologic modeling, or modeling of the terrestrial hydrologic cycle, is very data intensive. Models require many inputs to define topography, geology and atmospheric conditions. Additionally, in situ observations such as streamflow rate and depth to groundwater can be used to evaluate model outputs and calibrate input parameters. There are many public organizations and research groups in the United States which produce and make freely available parts of this required data. However, the data have a wide range of spatiotemporal resolutions, file types, and methods of access. This makes finding and accessing all the data required for analysis a very time-consuming part of most hydrologic studies. The hf_hydrodata package is designed to simplify this data acquisition process by providing access to a broad array of variables, all of which have been pre-processed for consistency.

Reas the paper: https://doi.org/10.21105/joss.06623

By Haoran Li, Diego Serrano, Thomas Guillod, Shukai Wang, Evan Dogariu, Andrew Nadler, Min Luo, Vineet Bansal, Niraj K. Jha, Yuxin Chen, Charles R. Sullivan, and Minjie Chen

This article applies machine learning to power magnetics modeling. We first introduce an open-source database—MagNet—which hosts a large amount of experimentally measured excitation data for many materials across a variety of operating conditions, consisting of more than 500 000 data points in its current state. The processes for data acquisition and data quality control are explained. We then demonstrate a few neural network-based power magnetics modeling tools for modeling the core losses and B–H loops. The neural network allows multiple factors that may influence the magnetic characteristics to be modeled in a unified framework, where the nonlinear behaviors are captured with high accuracy and high generality. Neural network models are found to be effective in compressing the measurement data and predicting the material characteristics, paving the way for “neural networks as datasheets” to assist power magnetics design. Transfer learning is applied to the training of neural network models to further reduce the data size requirement while maintaining sufficient model accuracy.

Read the paper: https://doi.org/10.1109/TPEL.2023.3309232

The inaugural conference from the US-RSE Association just wrapped up in Chicago! Somewhat paradoxically, the gathering felt like a culmination and a new beginning all at once. It was the culmination of years of effort tracing back to 2017 when earnest discussions towards organizing the US-RSE began. Some RSEs who had collaborated online for years finally got to meet in person for the first time! It’s also a new beginning: the US-RSE has “grown up,” in a sense, and we hope this conference is the first of many more to come in the United States.

The conference theme was “Software-Enabled Discovery and Beyond.” Participants from academia, industry, government labs, and other research institutions across the country came together to discuss topics like software-driven discovery and scholarship, software technology trends, software engineering best practices, community engagement, training resources, and the ongoing quest to build and grow the RSE profession.

Our RSE Group sent a handful of representatives and contributed the following content to the conference’s technical program:

1. INnovative Training Enabled by a Research Software Engineering Community of Trainers (INTERSECT) – Ian Cosden and Jeffrey Carver (talk)
2. Princeton University’s RSE Summer Internship and Fellowship Programs – Joel Bretheim, Ian Cosden, Peter Elmer, Garrett Wright, Colin Swaney, Abhishek Biswas, Henry Schreiner, Kilian Lieret, and Vineet Bansal (talk)

It was gratifying to see the RSE community in the US come together in this way and collaboratively make our first ever conference a resounding success. On the final day of the conference, we learned it will indeed return next year and it will be hosted in Albuquerque!

Josko Plazonic, Jonathan Halverson, and Troy Comi

Job monitoring on high-performance computing clusters is important for evaluating hardware performance, troubleshooting failed jobs, identifying inefficient jobs and more. The combination of the Prometheus monitoring framework and the Grafana visualization toolkit has proven successful in recent years. This work shows how four Prometheus exporters can be configured for a Slurm cluster to provide detailed job-level information on CPU/GPU efficiencies and CPU/GPU memory usage as well as node-level Network File System (NFS) statistics and cluster-level General Parallel File System (GPFS) activity. A novel approach was devised to efficiently store a summary of this data in the Slurm database for each completed job. The open-source job monitoring platform introduced here can be used for batch, interactive and Open OnDemand jobs. Several tools are presented that use the Prometheus and Slurm databases to create dashboards, utilization reports and alerts.

Read the paper: https://doi.org/10.1145/3569951.3604396

By Maureen Cetera, Rishabh Sharan, Gabriela Hayward-Lara, Brooke Phillips, Abhishek Biswas, Madalene Halley, Evalyn Beall, Bridgett vonHoldt, Danelle Devenport

The planar cell polarity (PCP) pathway collectively orients cells with respect to a body axis. Hair follicles of the murine epidermis provide a striking readout of PCP activity in their uniform alignment across the skin. Here, we characterize, from the molecular to tissue-scale, PCP establishment in the rosette fancy mouse, a natural variant with posterior-specific whorls in its fur, to understand how epidermal polarity is coordinated across the tissue. We find that rosette hair follicles emerge with reversed orientations specifically in the posterior region, creating a mirror image of epidermal polarity. The rosette trait is associated with a missense mutation in the core PCP gene Fzd6, which alters a consensus site for N-linked glycosylation, inhibiting its membrane localization. Unexpectedly, the Fzd6 trafficking defect does not block asymmetric localization of the other PCP proteins. Rather, the normally uniform axis of PCP asymmetry rotates where the PCP-directed cell movements that orient follicles are reversed, suggesting the PCP axis rotates 180°. Collectively, our multiscale analysis of epidermal polarity reveals PCP patterning can be regionally decoupled to produce posterior whorls in the rosette fancy mouse.

Read the paper: https://doi.org/10.1242/dev.202078

By Yongzhen Liu, Thomas R. Cafiero, Debby Park, Abhishek Biswas, Benjamin Y. Winer, Cheul H. Cho, Yaron Bram, Vasuretha Chandar, Aoife K. O’ Connell, Hans P. Gertje, Nicholas Crossland, Robert E. Schwartz & Alexander Ploss

Hepatitis B virus (HBV) only infects humans and chimpanzees, posing major challenges for modeling HBV infection and chronic viral hepatitis. The major barrier in establishing HBV infection in non-human primates lies at incompatibilities between HBV and simian orthologues of the HBV receptor, sodium taurocholate co-transporting polypeptide (NTCP). Through mutagenesis analysis and screening among NTCP orthologues from Old World monkeys, New World monkeys and prosimians, we determined key residues responsible for viral binding and internalization, respectively and identified marmosets as a suitable candidate for HBV infection. Primary marmoset hepatocytes and induced pluripotent stem cell-derived hepatocyte-like cells support HBV and more efficient woolly monkey HBV (WMHBV) infection. Adapted chimeric HBV genome harboring residues 1–48 of WMHBV preS1 generated here led to a more efficient infection than wild-type HBV in primary and stem cell derived marmoset hepatocytes. Collectively, our data demonstrate that minimal targeted simianization of HBV can break the species barrier in small NHPs, paving the path for an HBV primate model.

Read the paper: https://www.nature.com/articles/s41467-023-39148-3

By Padraig Gleeson, Sharon Crook, David Turner, Katherine Mantel, Mayank Raunak, Ted Willke, Jonathan D. Cohen

Neuroscience, cognitive science, and computer science are increasingly benefiting through their interactions. This could be accelerated by direct sharing of computational models across disparate modeling software used in each. We describe a Model Description Format designed to meet this challenge.

Read the paper: https://doi.org/10.1016/j.neuron.2023.03.037