An Analytically Solvable Model for Rapid Evolution of Modular Structure

Biological systems often display modularity, in the sense that they can be decomposed into nearly independent subsystems. Recent studies have suggested that modular structure can spontaneously emerge if goals (environments) change over time, such that each new goal shares the same set of sub-problems with previous goals. Such modularly varying goals can also dramatically speed up evolution, relative to evolution under a constant goal. These studies were based on simulations of model systems, such as logic circuits and RNA structure, which are generally not easy to treat analytically. We present, here, a simple model for evolution under modularly varying goals that can be solved analytically. This model helps to understand some of the fundamental mechanisms that lead to rapid emergence of modular structure under modularly varying goals. In particular, the model suggests a mechanism for the dramatic speedup in evolution observed under such temporally varying goals

Mapping Differentiation under Mixed Culture Conditions Reveals a Tunable Continuum of T Cell Fates

Cell differentiation is typically directed by external signals that drive opposing regulatory pathways. Studying differentiation under polarizing conditions, with only one input signal provided, is limited in its ability to resolve the logic of interactions between opposing pathways. Dissection of this logic can be facilitated by mapping the system's response to mixtures of input signals, which are expected to occur in vivo, where cells are simultaneously exposed to various signals with potentially opposing effects. Here, we systematically map the response of naïve T cells to mixtures of signals driving differentiation into the Th1 and Th2 lineages. We characterize cell state at the single cell level by measuring levels of the two lineage-specific transcription factors (T-bet and GATA3) and two lineage characteristic cytokines (IFN-γ and IL-4) that are driven by these transcription regulators. We find a continuum of mixed phenotypes in which individual cells co-express the two lineage-specific master regulators at levels that gradually depend on levels of the two input signals. Using mathematical modeling we show that such tunable mixed phenotype arises if autoregulatory positive feedback loops in the gene network regulating this process are gradual and dominant over cross-pathway inhibition. We also find that expression of the lineage-specific cytokines follows two independent stochastic processes that are biased by expression levels of the master regulators. Thus, cytokine expression is highly heterogeneous under mixed conditions, with subpopulations of cells expressing only IFN-γ, only IL-4, both cytokines, or neither. The fraction of cells in each of these subpopulations changes gradually with input conditions, reproducing the continuous internal state at the cell population level. These results suggest a differentiation scheme in which cells reflect uncertainty through a continuously tuneable mixed phenotype combined with a biased stochastic decision rather than a binary phenotype with a deterministic decision

Cell Lineage Analysis of the Mammalian Female Germline

Fundamental aspects of embryonic and post-natal development, including maintenance of the mammalian female germline, are largely unknown. Here we employ a retrospective, phylogenetic-based method for reconstructing cell lineage trees utilizing somatic mutations accumulated in microsatellites, to study female germline dynamics in mice. Reconstructed cell lineage trees can be used to estimate lineage relationships between different cell types, as well as cell depth (number of cell divisions since the zygote). We show that, in the reconstructed mouse cell lineage trees, oocytes form clusters that are separate from hematopoietic and mesenchymal stem cells, both in young and old mice, indicating that these populations belong to distinct lineages. Furthermore, while cumulus cells sampled from different ovarian follicles are distinctly clustered on the reconstructed trees, oocytes from the left and right ovaries are not, suggesting a mixing of their progenitor pools. We also observed an increase in oocyte depth with mouse age, which can be explained either by depth-guided selection of oocytes for ovulation or by post-natal renewal. Overall, our study sheds light on substantial novel aspects of female germline preservation and development

TKS X: Confirmation of TOI-1444b and a Comparative Analysis of the Ultra-short-period Planets with Hot Neptunes

We report the discovery of TOI-1444b, a 1.4-$R_\oplus$ super-Earth on a 0.47-day orbit around a Sun-like star discovered by {\it TESS}. Precise radial velocities from Keck/HIRES confirmed the planet and constrained the mass to be $3.87 \pm 0.71 M_\oplus$. The RV dataset also indicates a possible non-transiting, 16-day planet ($11.8\pm2.9M_\oplus$). We report a tentative detection of phase curve variation and secondary eclipse of TOI-1444b in the {\it TESS} bandpass. TOI-1444b joins the growing sample of 17 ultra-short-period planets with well-measured masses and sizes, most of which are compatible with an Earth-like composition. We take this opportunity to examine the expanding sample of ultra-short-period planets ($<2R_\oplus$) and contrast them with the newly discovered sub-day ultra-hot Neptunes ($>3R_\oplus$, $>2000F_\oplus$ TOI-849 b, LTT9779 b and K2-100). We find that 1) USPs have predominately Earth-like compositions with inferred iron core mass fractions of 0.32$\pm$0.04; and have masses below the threshold of runaway accretion ($\sim 10M_\oplus$), while ultra-hot Neptunes are above the threshold and have H/He or other volatile envelope. 2) USPs are almost always found in multi-planet system consistent with a secular interaction formation scenario; ultra-hot Neptunes ($P_{\rm orb} \lesssim$1 day) tend to be ``lonely' similar to longer-period hot Neptunes($P_{\rm orb}$1-10 days) and hot Jupiters. 3) USPs occur around solar-metallicity stars while hot Neptunes prefer higher metallicity hosts. 4) In all these respects, the ultra-hot Neptunes show more resemblance to hot Jupiters than the smaller USP planets, although ultra-hot Neptunes are rarer than both USP and hot Jupiters by 1-2 orders of magnitude.Comment: Accepted too AJ. 12 Figures, 4 table

Identification of the top TESS objects of interest for atmospheric characterization of transiting exoplanets with JWST

Funding: Funding for the TESS mission is provided by NASA's Science Mission Directorate. This work makes use of observations from the LCOGT network. Part of the LCOGT telescope time was granted by NOIRLab through the Mid-Scale Innovations Program (MSIP). MSIP is funded by NSF. This paper is based on observations made with the MuSCAT3 instrument, developed by the Astrobiology Center and under financial support by JSPS KAKENHI (grant No. JP18H05439) and JST PRESTO (grant No. JPMJPR1775), at Faulkes Telescope North on Maui, HI, operated by the Las Cumbres Observatory. This paper makes use of data from the MEarth Project, which is a collaboration between Harvard University and the Smithsonian Astrophysical Observatory. The MEarth Project acknowledges funding from the David and Lucile Packard Fellowship for Science and Engineering, the National Science Foundation under grant Nos. AST-0807690, AST-1109468, AST-1616624 and AST-1004488 (Alan T. Waterman Award), the National Aeronautics and Space Administration under grant No. 80NSSC18K0476 issued through the XRP Program, and the John Templeton Foundation. C.M. would like to gratefully acknowledge the entire Dragonfly Telephoto Array team, and Bob Abraham in particular, for allowing their telescope bright time to be put to use observing exoplanets. B.J.H. acknowledges support from the Future Investigators in NASA Earth and Space Science and Technology (FINESST) program (grant No. 80NSSC20K1551) and support by NASA under grant No. 80GSFC21M0002. K.A.C. and C.N.W. acknowledge support from the TESS mission via subaward s3449 from MIT. D.R.C. and C.A.C. acknowledge support from NASA through the XRP grant No. 18-2XRP18_2-0007. C.A.C. acknowledges that this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004). S.Z. and A.B. acknowledge support from the Israel Ministry of Science and Technology (grant No. 3-18143). The research leading to these results has received funding from the ARC grant for Concerted Research Actions, financed by the Wallonia-Brussels Federation. TRAPPIST is funded by the Belgian Fund for Scientific Research (Fond National de la Recherche Scientifique, FNRS) under the grant No. PDR T.0120.21. The postdoctoral fellowship of K.B. is funded by F.R.S.-FNRS grant No. T.0109.20 and by the Francqui Foundation. H.P.O.'s contribution has been carried out within the framework of the NCCR PlanetS supported by the Swiss National Science Foundation under grant Nos. 51NF40_182901 and 51NF40_205606. F.J.P. acknowledges financial support from the grant No. CEX2021-001131-S funded by MCIN/AEI/ 10.13039/501100011033. A.J. acknowledges support from ANID—Millennium Science Initiative—ICN12_009 and from FONDECYT project 1210718. Z.L.D. acknowledges the MIT Presidential Fellowship and that this material is based upon work supported by the National Science Foundation Graduate Research Fellowship under grant No. 1745302. P.R. acknowledges support from the National Science Foundation grant No. 1952545. This work is partly supported by JSPS KAKENHI grant Nos. JP17H04574, JP18H05439, JP21K20376; JST CREST grant No. JPMJCR1761; and Astrobiology Center SATELLITE Research project AB022006. This publication benefits from the support of the French Community of Belgium in the context of the FRIA Doctoral Grant awarded to M.T. D.D. acknowledges support from TESS Guest Investigator Program grant Nos. 80NSSC22K1353, 80NSSC22K0185, and 80NSSC23K0769. A.B. acknowledges the support of M.V. Lomonosov Moscow State University Program of Development. T.D. was supported in part by the McDonnell Center for the Space Sciences. V.K. acknowledges support from the youth scientific laboratory project, topic FEUZ-2020-0038.JWST has ushered in an era of unprecedented ability to characterize exoplanetary atmospheres. While there are over 5000 confirmed planets, more than 4000 Transiting Exoplanet Survey Satellite (TESS) planet candidates are still unconfirmed and many of the best planets for atmospheric characterization may remain to be identified. We present a sample of TESS planets and planet candidates that we identify as “best-in-class” for transmission and emission spectroscopy with JWST. These targets are sorted into bins across equilibrium temperature Teq and planetary radius Rp and are ranked by a transmission and an emission spectroscopy metric (TSM and ESM, respectively) within each bin. We perform cuts for expected signal size and stellar brightness to remove suboptimal targets for JWST. Of the 194 targets in the resulting sample, 103 are unconfirmed TESS planet candidates, also known as TESS Objects of Interest (TOIs). We perform vetting and statistical validation analyses on these 103 targets to determine which are likely planets and which are likely false positives, incorporating ground-based follow-up from the TESS Follow-up Observation Program to aid the vetting and validation process. We statistically validate 18 TOIs, marginally validate 31 TOIs to varying levels of confidence, deem 29 TOIs likely false positives, and leave the dispositions for four TOIs as inconclusive. Twenty-one of the 103 TOIs were confirmed independently over the course of our analysis. We intend for this work to serve as a community resource and motivate formal confirmation and mass measurements of each validated planet. We encourage more detailed analysis of individual targets by the community.Peer reviewe

Geometry of the Gene Expression Space of Individual Cells.

There is a revolution in the ability to analyze gene expression of single cells in a tissue. To understand this data we must comprehend how cells are distributed in a high-dimensional gene expression space. One open question is whether cell types form discrete clusters or whether gene expression forms a continuum of states. If such a continuum exists, what is its geometry? Recent theory on evolutionary trade-offs suggests that cells that need to perform multiple tasks are arranged in a polygon or polyhedron (line, triangle, tetrahedron and so on, generally called polytopes) in gene expression space, whose vertices are the expression profiles optimal for each task. Here, we analyze single-cell data from human and mouse tissues profiled using a variety of single-cell technologies. We fit the data to shapes with different numbers of vertices, compute their statistical significance, and infer their tasks. We find cases in which single cells fill out a continuum of expression states within a polyhedron. This occurs in intestinal progenitor cells, which fill out a tetrahedron in gene expression space. The four vertices of this tetrahedron are each enriched with genes for a specific task related to stemness and early differentiation. A polyhedral continuum of states is also found in spleen dendritic cells, known to perform multiple immune tasks: cells fill out a tetrahedron whose vertices correspond to key tasks related to maturation, pathogen sensing and communication with lymphocytes. A mixture of continuum-like distributions and discrete clusters is found in other cell types, including bone marrow and differentiated intestinal crypt cells. This approach can be used to understand the geometry and biological tasks of a wide range of single-cell datasets. The present results suggest that the concept of cell type may be expanded. In addition to discreet clusters in gene-expression space, we suggest a new possibility: a continuum of states within a polyhedron, in which the vertices represent specialists at key tasks

Expression profiles of the four colon crypt archetypes are each enriched for markers of specific cell types.

<p>(a) The expression profiles of the four archetypes, with enriched genes colored. Enriched genes were determined by leave-1-out enrichment analysis, binning the cells according to distance from each archetype and seeking when average expression in the bin closest to the archetype is maximal, as described in Methods: 1D Gene enrichment at archetypes (See full enriched genes list in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004224#pcbi.1004224.s026" target="_blank">S2 Table</a>). Light blue—enterocyte archetype, yellow—Nodal archetype, green—stem cells archetype, red—goblet cell archetype. Genes that are not enriched, or enriched in more than one archetype, are in dark blue. Zero level represents the average expression of each gene in the dataset. (b) Leave-1-out enrichment plot: expression of a gene (SLC26A3—an enterocyte marker) as a function of distance from archetype in equal mass bins of cells (Methods: 1D Gene enrichment at archetypes), line color indicates archetype. This gene is maximally enriched only at the enterocyte archetype (blue line). For enrichment plots for additional genes see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004224#pcbi.1004224.s005" target="_blank">S5 Fig</a>. (c) A two dimensional enrichment plot of SLC26A3, in which its expression is plotted on the plane of the first 2PCs of the data, indicating expression is maximal in the cells closest to the enterocyte archetype. Contours are expression density estimated using a Gaussian kernel (Methods: 2D Gene enrichment at archetypes). Archetype positions and PCs were calculated without the tested gene.</p

Different tissues analyzed by different single-cell technologies show polytopes and tasks.

<p>(a) Human bone marrow cells analyzed by single-cell mass cytometry [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004224#pcbi.1004224.ref013" target="_blank">13</a>,<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004224#pcbi.1004224.ref025" target="_blank">25</a>] in which proteins are detected using mass-tagged antibodies is well described by a 4D simplex (a polytope with 5 vertices). The simplex is shown projected on the first 3PCs, for other projections see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004224#pcbi.1004224.s012" target="_blank">S12c Fig</a>. The archetypes correspond to cell types as indicated. Cell density peaks near each archetype. (b) Mouse spleen LPS stimulated dendritic cells analyzed by single-cell RNA-Seq [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004224#pcbi.1004224.ref003" target="_blank">3</a>] are well described by a tetrahedron. Archetypes are labeled with functions inferred from genes maximally enriched in cells near each archetype. For more details see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004224#pcbi.1004224.s032" target="_blank">S1G</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004224#pcbi.1004224.s032" target="_blank">S1H</a> Text.</p

Progenitor crypt cells fall in a tetrahedron.

<p>(a) Enterocytes, goblet cells and nodal cells analyzed separately do not form significant polytopes. Cells are color coded by type in the tetrahedron of <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004224#pcbi.1004224.g003" target="_blank">Fig 3</a>, and each cell class is plotted in its own first 3PC. (b) Progenitor cells analyzed separately fall uniformly in a tetrahedron. The best fit tetrahedron is shown (PCHA delta = 0.5). Arrow represents direction of development according to Axin2 levels, see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004224#pcbi.1004224.s007" target="_blank">S7b Fig</a>. Also shown are projections on the principal planes, which resemble triangles or quadrangles. Archetypes and their variation upon data resampling (bootstrapping) are shown as gray ellipses. (c) Explained variance as a function of polytope order k or number of PCs D both suggest a tetrahedron (k = 4, D = 3).</p

Heterogeneity in cytokine expression is generated through independent and biased stochastic processes.

<p>(A, B) Probability of cytokine expression is biased by the level of the corresponding TF. Cells growing under Th1, Mixed, and Th2 conditions were binned according to their measured level of (A) T-bet or (B) GATA3 expression. For each bin, containing 500 cells, the fraction of cells expressing (A) IFN-γ or (B) IL-4 is plotted versus the mean TF level of cells in that bin. The red line shows the population average level of the TF, and the green line shows the fraction of cytokine positive cells in the entire cell population. (C) GATA3/T-bet ratio is plotted for the four subpopulations of cytokine expression in cells growing under mixed conditions, compared with this ratio measured in cells cultured under polarizing Th1 or Th2 conditions. All four subpopulations of cells that were cultured under mixed conditions show a similar T-bet/GATA3 ratio, irrespective of their cytokine secretion state (−/−, +/−, −/+, +/+: corresponding to the four quadrants of <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001616#pbio-1001616-g004" target="_blank">Figure 4E</a>). This observation is insensitive to the threshold values used to define the subpopulations (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001616#pbio.1001616.s009" target="_blank">Figure S9</a>). (D) Cells were cultured under mixed conditions for 1 wk, and then viably sorted into four subpopulations according to their cytokine expression pattern, as indicated (subpopulations correspond to the four quadrants of <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001616#pbio-1001616-g004" target="_blank">Figure 4E</a>). Each subpopulation was re-cultured under mixed conditions for another week, restimulated, and levels of cytokine expression were measured, as shown. Within that week all subpopulations were able to re-populate all four quadrants, such that all cytokine expression patterns reappear.</p